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unknowndate 2020-02-15 11:33:14
2025-04-18 00:38:06
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int64 0
11.7k
| library_name
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tomvoelker/gpt22gpt2-gpt2-medium-cnn-dailymail-seed42 | tomvoelker | "2025-04-02T15:22:39Z" | 141 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-03-13T12:55:03Z" | ---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: gpt22gpt2-gpt2-medium-cnn-dailymail-seed42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt22gpt2-gpt2-medium-cnn-dailymail-seed42
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7212
- Rouge1: 0.2011
- Rouge2: 0.0413
- Rougel: 0.1250
- Rougelsum: 0.1887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.8287 | 0.2229 | 2000 | 2.6199 | 0.1926 | 0.0340 | 0.1248 | 0.1800 |
| 2.4721 | 0.4458 | 4000 | 2.2632 | 0.1971 | 0.0382 | 0.1284 | 0.1816 |
| 2.2324 | 0.6687 | 6000 | 2.0400 | 0.2164 | 0.0487 | 0.1345 | 0.2025 |
| 2.1025 | 0.8916 | 8000 | 1.9285 | 0.1827 | 0.0330 | 0.1175 | 0.1691 |
| 1.8926 | 1.1145 | 10000 | 1.8595 | 0.1712 | 0.0288 | 0.1124 | 0.1598 |
| 1.8605 | 1.3374 | 12000 | 1.8191 | 0.1919 | 0.0371 | 0.1230 | 0.1790 |
| 1.8327 | 1.5603 | 14000 | 1.7931 | 0.1838 | 0.0346 | 0.1189 | 0.1724 |
| 1.8209 | 1.7832 | 16000 | 1.7606 | 0.1847 | 0.0342 | 0.1169 | 0.1733 |
| 1.7879 | 2.0061 | 18000 | 1.7484 | 0.1949 | 0.0390 | 0.1227 | 0.1830 |
| 1.6658 | 2.2290 | 20000 | 1.7460 | 0.1910 | 0.0358 | 0.1201 | 0.1798 |
| 1.6547 | 2.4519 | 22000 | 1.7386 | 0.1906 | 0.0360 | 0.1196 | 0.1791 |
| 1.6426 | 2.6748 | 24000 | 1.7256 | 0.1953 | 0.0387 | 0.1226 | 0.1833 |
| 1.6495 | 2.8977 | 26000 | 1.7212 | 0.2011 | 0.0413 | 0.1250 | 0.1887 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
gradientrouting-spar/toy_goodharting_gemma-2-2b-it_olora_fruits_vegetables_20250402_130128 | gradientrouting-spar | "2025-04-02T13:03:02Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-02T13:02:56Z" | <!DOCTYPE html>
<html class="" lang="en">
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name="viewport"
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mradermacher/GWQ-9B-Preview-GGUF | mradermacher | "2025-01-09T09:09:25Z" | 214 | 0 | transformers | [
"transformers",
"gguf",
"gemma2",
"text-generation-inference",
"f16",
"en",
"base_model:prithivMLmods/GWQ-9B-Preview",
"base_model:quantized:prithivMLmods/GWQ-9B-Preview",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-01-08T23:03:08Z" | ---
base_model: prithivMLmods/GWQ-9B-Preview
language:
- en
library_name: transformers
license: gemma
quantized_by: mradermacher
tags:
- gemma2
- text-generation-inference
- f16
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/prithivMLmods/GWQ-9B-Preview
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GWQ-9B-Preview-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/GWQ-9B-Preview-GGUF/resolve/main/GWQ-9B-Preview.f16.gguf) | f16 | 18.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Phaaarus/sw-layer-2-finetuned | Phaaarus | "2024-04-08T15:57:31Z" | 116 | 0 | transformers | [
"transformers",
"safetensors",
"qwen",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-04-08T15:55:03Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
dimasik1987/74057d9d-5851-4da6-bd97-ef5c3088939d | dimasik1987 | "2025-01-19T01:59:13Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-falcon-40b",
"base_model:adapter:katuni4ka/tiny-random-falcon-40b",
"region:us"
] | null | "2025-01-19T01:48:51Z" | ---
library_name: peft
base_model: katuni4ka/tiny-random-falcon-40b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 74057d9d-5851-4da6-bd97-ef5c3088939d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: katuni4ka/tiny-random-falcon-40b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4d7696718e3a20b9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4d7696718e3a20b9_train_data.json
type:
field_input: intent
field_instruction: instruction
field_output: response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik1987/74057d9d-5851-4da6-bd97-ef5c3088939d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/4d7696718e3a20b9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b0400de6-2b4a-4f0b-ab23-3b908ed0d394
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b0400de6-2b4a-4f0b-ab23-3b908ed0d394
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 74057d9d-5851-4da6-bd97-ef5c3088939d
This model is a fine-tuned version of [katuni4ka/tiny-random-falcon-40b](https://huggingface.co/katuni4ka/tiny-random-falcon-40b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 11.0512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 11.1142 |
| 44.4645 | 0.0001 | 5 | 11.1078 |
| 44.4165 | 0.0003 | 10 | 11.0900 |
| 44.2967 | 0.0004 | 15 | 11.0719 |
| 44.2667 | 0.0006 | 20 | 11.0589 |
| 44.2339 | 0.0007 | 25 | 11.0524 |
| 44.2118 | 0.0008 | 30 | 11.0512 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
behbudiy/Mistral-7B-Instruct-Uz | behbudiy | "2024-11-27T14:41:12Z" | 176 | 10 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"text-generation-inference",
"summarization",
"translation",
"question-answering",
"conversational",
"uz",
"en",
"dataset:tahrirchi/uz-crawl",
"dataset:allenai/c4",
"dataset:MLDataScientist/Wikipedia-uzbek-2024-05-01",
"dataset:yahma/alpaca-cleaned",
"dataset:behbudiy/alpaca-cleaned-uz",
"dataset:behbudiy/translation-instruction",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-08-30T13:28:54Z" | ---
license: apache-2.0
language:
- uz
- en
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: transformers
tags:
- text-generation-inference
- summarization
- translation
- question-answering
datasets:
- tahrirchi/uz-crawl
- allenai/c4
- MLDataScientist/Wikipedia-uzbek-2024-05-01
- yahma/alpaca-cleaned
- behbudiy/alpaca-cleaned-uz
- behbudiy/translation-instruction
metrics:
- bleu
- comet
- accuracy
pipeline_tag: text-generation
---
### Model Description
The Mistral-7B-Instruct-Uz model has been continually pre-trained and instruction-tuned using a mix of publicly available and syntheticly constructed Uzbek and English data to preserve its original knowledge while enhancing its capabilities. This model is designed to support various natural language processing tasks in Uzbek, such as machine translation, summarization, and dialogue systems, ensuring robust performance across these applications.
For details regarding the performance metrics compared to the base model, see [this post.](https://www.linkedin.com/feed/update/urn:li:activity:7241389815559008256/)
- **Developed by:**
- [Eldor Fozilov](https://www.linkedin.com/in/eldor-fozilov/)
- [Azimjon Urinov](https://azimjonn.github.io/)
- [Khurshid Juraev](https://kjuraev.com/)
📊 **Performance Comparison:**
| Model Name | BLEU Uz-En (One-shot) | BLEU En-Uz (One-shot) | COMET (Uz-En) | COMET (Ez-Un) | Uzbek Sentiment Analysis | Uzbek News Classification | MMLU (English) (5-shot) |
|------------------------|------------------------------------|------------------------------------|--------------------------|----------------|----------------|-------------|-------------------|
| **Llama-3.1 8B Instruct** | 23.74 | 6.72 | 84.30 | 82.70 | 68.96 | 55.41 | 65.77
| **Llama-3.1 8B Instruct Uz** | 27.42 | 11.58 | 85.63 | 86.53 | 82.42 | 60.84 | 62.78
| **Mistral 7B Instruct** | 7.47 | 0.67 | 68.14 | 45.58 | 62.02 | 47.52 | 61.07
| **Mistral 7B Instruct Uz** | 29.39 | 16.77 | 86.91 |88.75 | 79.13 | 59.38 | 55.72
| **Mistral Nemo Instruct** | 25.68 | 9.79 | 85.56 | 85.04 | 72.47 | 49.24 |67.62
| **Mistral Nemo Instruct Uz** | 30.49 | 15.52 | 87.04 | 88.01 | 82.05 | 58.2 | 67.36
| **Google Translate** | 41.18 | 22.98 | 89.16 | 90.67 | — | — | — |
The results show that Uzbek-optimized models consistently outperform their base counterparts in translation benchmarks (BLEU and COMET) on the FLORES+ Uz-En / En-Uz evaluation datasets, sentiment analysis and news classification in Uzbek language.
Also, on the MMLU benchmark, which measures general language understanding across multiple tasks in English, the finetuned models did not show significant decline. (The base Llama model’s MMLU score differs from the official score due to our evaluation method. Refer to the links below to see evaluation details.)
Looking ahead, these models are just **early versions**. We are actively working on further improving our data curation and fine-tuning method to provide even better results in the near future. In addition, we will scale up the dataset size both for continual-pretraining and instruction-tuning, and also customize other strong open-source LLMs for Uzbek language.
We’re eager to see how these models will be used by our Uzbek 🇺🇿 community and look forward to continuing this work. 🚀
## Installation
It is recommended to use `behbudiy/Mistral-7B-Instruct-Uz` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
```
pip install mistral_inference
```
## Download
```py
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-Uz')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="behbudiy/Mistral-7B-Instruct-Uz", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
```
### Chat
After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
```
mistral-chat $HOME/mistral_models/7B-Instruct-Uz --instruct --max_tokens 256
```
### Instructiong Following
```py
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(messages=[UserMessage(content="O'zbekiston haqida ma'lumot ber.")])
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
```
## Generate with `transformers`
If you want to use Hugging Face `transformers` to generate text, you can do something like this.
```py
from transformers import pipeline
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="behbudiy/Mistral-7B-Instruct-Uz", device='cuda')
chatbot(messages)
```
## Information on Evaluation Method
To evaluate on the translation task, we used FLORES+ Uz-En / En-Uz datasets, where we merged the dev and test sets to create a bigger evaluation data for each Uz-En and En-Uz subsets.
We used the following prompt to do one-shot Uz-En evaluation both for the base model and Uzbek-optimized model (for En-Uz eval, we changed the positions of the words "English" and "Uzbek").
```python
prompt = f'''You are a professional Uzbek-English translator. Your task is to accurately translate the given Uzbek text into English.
Instructions:
1. Translate the text from Uzbek to English.
2. Maintain the original meaning and tone.
3. Use appropriate English grammar and vocabulary.
4. If you encounter an ambiguous or unfamiliar word, provide the most likely translation based on context.
5. Output only the English translation, without any additional comments.
Example:
Uzbek: "Bugun ob-havo juda yaxshi, quyosh charaqlab turibdi."
English: "The weather is very nice today, the sun is shining brightly."
Now, please translate the following Uzbek text into English:
"{sentence}"
'''
```
To assess the model's ability in Uzbek sentiment analysis, we used the **risqaliyevds/uzbek-sentiment-analysis** dataset, for which we created binary labels (0: Negative, 1: Positive) using GPT-4o API (refer to **behbudiy/uzbek-sentiment-analysis** dataset).
We used the following prompt for the evaluation:
```python
prompt = f'''Given the following text, determine the sentiment as either 'Positive' or 'Negative.' Respond with only the word 'Positive' or 'Negative' without any additional text or explanation.
Text: {text}"
'''
```
For Uzbek News Classification, we used **risqaliyevds/uzbek-zero-shot-classification** dataset and asked the model to predict the category of the news using the following prompt:
```python
prompt = f'''Classify the given Uzbek news article into one of the following categories. Provide only the category number as the answer.
Categories:
0 - Politics (Siyosat)
1 - Economy (Iqtisodiyot)
2 - Technology (Texnologiya)
3 - Sports (Sport)
4 - Culture (Madaniyat)
5 - Health (Salomatlik)
6 - Family and Society (Oila va Jamiyat)
7 - Education (Ta'lim)
8 - Ecology (Ekologiya)
9 - Foreign News (Xorijiy Yangiliklar)
Now classify this article:
"{text}"
Answer (number only):"
'''
```
## MMLU
We used [this script](https://github.com/FranxYao/chain-of-thought-hub/blob/461e2d551f3f12d54caee75fa1e915fdbc3e9d12/MMLU/run_mmlu_open_source.py).
## More
For more details and examples, refer to the base model below:
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3 |
j-morano/rrwnet-rite | j-morano | "2025-03-26T09:50:07Z" | 29 | 0 | null | [
"safetensors",
"artery-vein",
"retinal-imaging",
"segmentation",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"image-segmentation",
"arxiv:2402.03166",
"license:cc-by-nc-4.0",
"region:us"
] | image-segmentation | "2025-03-13T14:55:28Z" | ---
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- artery-vein
- retinal-imaging
- segmentation
- model_hub_mixin
- pytorch_model_hub_mixin
---
# RRWNet RITE
This repo contains the the official weights of the RRWNet model trained on the RITE dataset, from the paper ["RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification"](https://doi.org/10.1016/j.eswa.2024.124970), by José Morano, Guilherme Aresta, and Hrvoje Bogunović, published in _Expert Systems with Applications_ (2024).
[[`arXiv`](https://arxiv.org/abs/2402.03166)] [`ESWA`](https://doi.org/10.1016/j.eswa.2024.124970)] [[`GitHub`](https://github.com/j-morano/rrwnet)] [[`BibTeX`](#citation)]
## RRWNet models
Model | Dataset | Resolution | Weights
--- | --- | --- | ---
RRWNet | RITE | 584x565 (original) | [Download](https://huggingface.co/j-morano/rrwnet-rite)
RRWNet | HRF | 1024 width (resized) | [Download](https://huggingface.co/j-morano/rrwnet-hrf)
Please note that the size of the images used for training is important when using the weights for predictions.
## Usage
The model can be loaded using the `PyTorchModelHubMixin` from the `huggingface_hub` package and the code from the `model.py` file in our repo (<https://github.com/j-morano/rrwnet>), which can be downloaded from [here](https://raw.githubusercontent.com/j-morano/rrwnet/refs/heads/main/model.py).
```python
from huggingface_hub import PyTorchModelHubMixin
from model import RRWNet as RRWNetModel
class RRWNet(RRWNetModel, PyTorchModelHubMixin):
def __init__(self, input_ch=3, output_ch=3, base_ch=64, iterations=5):
super().__init__(input_ch, output_ch, base_ch, iterations)
model = RRWNet.from_pretrained("j-morano/rrwnet-rite")
```
## Preprocessing
Models are trained using enhanced images and masks.
You can preprocess the images offline using the `preprocessing.py` script in the repo.
The script will enhance the images and masks and save them in the specified directory.
```bash
python3 preprocessing.py --images-path data/images/ --masks-path data/masks/ --save-path data/enhanced
```
## Citation
If you use this code, the weights, the preprocessed data, or the predictions in your research, we would greatly appreciate it if you give a star to the repo and cite our work:
```
@article{morano2024rrwnet,
title={RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification},
author={Morano, Jos{\'e} and Aresta, Guilherme and Bogunovi{\'c}, Hrvoje},
journal={Expert Systems with Applications},
year={2024},
doi={10.1016/j.eswa.2024.124970}
}
```
|
flyingbugs/Qwen2.5-1.5B-Open-R1-Distill | flyingbugs | "2025-02-25T13:15:25Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:open-r1/OpenR1-Math-220k",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-17T03:20:56Z" | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: open-r1/OpenR1-Math-220k
library_name: transformers
model_name: Qwen2.5-1.5B-Open-R1-Distill
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-1.5B-Open-R1-Distill
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="flyingbugs/Qwen2.5-1.5B-Open-R1-Distill", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jjh233/huggingface/runs/2sdy2t94)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0.dev0
- Pytorch: 2.5.1
- Datasets: 3.3.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
crumb/gzip-openhermes | crumb | "2023-11-20T05:51:34Z" | 10 | 0 | transformers | [
"transformers",
"safetensors",
"en",
"dataset:teknium/openhermes",
"endpoints_compatible",
"region:us"
] | null | "2023-11-20T02:39:32Z" | ---
datasets:
- teknium/openhermes
language:
- en
---
# GZIP Embeddings with Normalized Text
It's so funny that the huggingface hub lets you do this
| model | parameters | embedding dimensions |
| --- | --- | --- |
| meta-llama/Llama-2-70b-hf | 70b | 8192 |
| crumb/gzip-openhermes | 1* | 242,831 |
*the huggingface pretrained model saving api requires at least one parameter, which is set to "1" in this model.
multiprocessing is suuuper weird so make sure you dont have the variables "p" or "calculate_ncd_row" in your code anywhere..
### Usage
```python
# Requirements
%pip install -qq transformers
# Download Model
from transformers import AutoModel
model = AutoModel.from_pretrained("crumb/gzip-openhermes", trust_remote_code=True)
# Prune model
model.config.update({
"corpus": model.config.corpus[:1024]
})
model.dimensionality() # 1024
# Inference
model(["this is a test sequence"], num_procs=16).shape # [1, 1024]
# Finetuning
from tqdm.auto import tqdm
new_data = ["i love GZIP! it is my favorite!", "i HATE transformers!"]
normalized_data = [
model.normalize(i) for i in tqdm(new_data)
]
print(f"Input: '{new_data[0]}'\nTransformed: '{normalized_data[0]}'")
model.config.update({
"corpus": model.config.corpus + normalized_data
})
model.dimensionality()
model.save_pretrained("my-finetuned-gzip-model")
```
config:
```
normalize = True,
normalized_corpus = True,
reduction = False,
reduced_dimension = 0,
remove_stop_words = True,
stop_words = stopwords.words('english'),
corpus = [], # openhermes instructions + outputs, i think having [instructions, outputs, instructions+outputs] would be better but its literally 3x slower also i dont care
``` |
wisdominanutshell/topic_summary | wisdominanutshell | "2023-12-26T09:25:14Z" | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
] | null | "2023-12-26T09:13:12Z" | ---
library_name: peft
base_model: teknium/OpenHermes-2.5-Mistral-7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 |
MrezaPRZ/Qwen2.5-Coder-3B-grpo-llm_ex_only_250 | MrezaPRZ | "2025-02-22T12:46:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-22T12:44:46Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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candyhaws/poca-SoccerTwos | candyhaws | "2024-01-31T10:51:23Z" | 16 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | "2024-01-31T10:42:44Z" | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: candyhaws/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
soonawg/ppo-LunarLander-v2 | soonawg | "2025-03-14T05:32:44Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-03-14T05:32:24Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.77 +/- 24.06
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hkivancoral/hushem_5x_beit_base_sgd_001_fold5 | hkivancoral | "2023-11-28T08:31:46Z" | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2023-11-28T08:01:02Z" | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_sgd_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7073170731707317
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_5x_beit_base_sgd_001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8979
- Accuracy: 0.7073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5363 | 1.0 | 28 | 1.5515 | 0.2439 |
| 1.4164 | 2.0 | 56 | 1.4971 | 0.3171 |
| 1.362 | 3.0 | 84 | 1.4523 | 0.3659 |
| 1.3381 | 4.0 | 112 | 1.3987 | 0.3902 |
| 1.2805 | 5.0 | 140 | 1.3538 | 0.4390 |
| 1.286 | 6.0 | 168 | 1.3184 | 0.4634 |
| 1.2664 | 7.0 | 196 | 1.2890 | 0.4634 |
| 1.1752 | 8.0 | 224 | 1.2605 | 0.4878 |
| 1.1615 | 9.0 | 252 | 1.2357 | 0.5366 |
| 1.1583 | 10.0 | 280 | 1.2200 | 0.5366 |
| 1.0622 | 11.0 | 308 | 1.1865 | 0.5854 |
| 1.0888 | 12.0 | 336 | 1.1579 | 0.5854 |
| 1.0774 | 13.0 | 364 | 1.1376 | 0.6098 |
| 1.0639 | 14.0 | 392 | 1.1207 | 0.6098 |
| 1.0329 | 15.0 | 420 | 1.1063 | 0.6098 |
| 1.0224 | 16.0 | 448 | 1.0819 | 0.6098 |
| 0.9888 | 17.0 | 476 | 1.0775 | 0.6098 |
| 0.9619 | 18.0 | 504 | 1.0585 | 0.6098 |
| 0.9636 | 19.0 | 532 | 1.0418 | 0.6098 |
| 0.932 | 20.0 | 560 | 1.0228 | 0.6098 |
| 0.9605 | 21.0 | 588 | 1.0275 | 0.6341 |
| 0.9245 | 22.0 | 616 | 1.0007 | 0.6829 |
| 0.9128 | 23.0 | 644 | 0.9952 | 0.6829 |
| 0.8833 | 24.0 | 672 | 0.9964 | 0.6585 |
| 0.8669 | 25.0 | 700 | 0.9755 | 0.6829 |
| 0.8822 | 26.0 | 728 | 0.9683 | 0.6829 |
| 0.8736 | 27.0 | 756 | 0.9644 | 0.6829 |
| 0.8195 | 28.0 | 784 | 0.9503 | 0.6829 |
| 0.815 | 29.0 | 812 | 0.9479 | 0.6829 |
| 0.8347 | 30.0 | 840 | 0.9483 | 0.6829 |
| 0.8334 | 31.0 | 868 | 0.9382 | 0.6829 |
| 0.7989 | 32.0 | 896 | 0.9378 | 0.6829 |
| 0.7878 | 33.0 | 924 | 0.9468 | 0.6829 |
| 0.8479 | 34.0 | 952 | 0.9186 | 0.7073 |
| 0.7868 | 35.0 | 980 | 0.9253 | 0.6829 |
| 0.7921 | 36.0 | 1008 | 0.9134 | 0.7073 |
| 0.7563 | 37.0 | 1036 | 0.9054 | 0.7073 |
| 0.7507 | 38.0 | 1064 | 0.9071 | 0.7073 |
| 0.8331 | 39.0 | 1092 | 0.8998 | 0.7073 |
| 0.7104 | 40.0 | 1120 | 0.9052 | 0.7073 |
| 0.773 | 41.0 | 1148 | 0.9044 | 0.7073 |
| 0.719 | 42.0 | 1176 | 0.9021 | 0.7073 |
| 0.7745 | 43.0 | 1204 | 0.8997 | 0.7073 |
| 0.781 | 44.0 | 1232 | 0.9016 | 0.7073 |
| 0.755 | 45.0 | 1260 | 0.9002 | 0.7073 |
| 0.7437 | 46.0 | 1288 | 0.9000 | 0.7073 |
| 0.7695 | 47.0 | 1316 | 0.8981 | 0.7073 |
| 0.7339 | 48.0 | 1344 | 0.8980 | 0.7073 |
| 0.7563 | 49.0 | 1372 | 0.8979 | 0.7073 |
| 0.755 | 50.0 | 1400 | 0.8979 | 0.7073 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Lauther/measuring-embeddings-v3-multilingual-e5-large-instruct-20e | Lauther | "2025-02-14T17:30:32Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:7552",
"loss:CoSENTLoss",
"dataset:Lauther/measuring-embeddings-v3",
"arxiv:1908.10084",
"base_model:intfloat/multilingual-e5-large-instruct",
"base_model:finetune:intfloat/multilingual-e5-large-instruct",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2025-02-14T17:01:39Z" | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7552
- loss:CoSENTLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
- source_sentence: How are calibration points linked to equipment?
sentences:
- 'How are flow computers and measurement systems related?
Flow computers can have multiple systems assigned to them. However, a measurement
system can only be assigned to one flow computer.
Database terminology:
In the database, this relationship is referred to as:
- Meter streams
- Meter runs
- Sections
Storage of the relationship:
The relationship between a flow computer and its assigned measurement system is
stored in a special table.
User context:
When a user refers to a "meter stream," they are indicating that they are searching
for a measurement system assigned to a specific flow computer.'
- "How does a flow computer generate and store reports?\nA flow computer generates\
\ daily or hourly reports to provide users with operational data. These reports\
\ are stored in the flow computer's memory in an organized format.\n\nReport structure:\n\
- Each report includes:\n- Date and time of the data recording.\n- Data recorded\
\ from flow computers.\n\nData storage in tables:\nThe reports are saved in two\
\ tables:\n1. Main table (Index):\n - Stores the date, time, and flow computer\
\ identifier.\n2. Detail table:\n - Stores the measured values associated with\
\ the report.\n\nConnection to the Modbus table:\nThe flow computer's reports\
\ are linked to a Modbus table. This table contains the names corresponding to\
\ each value in the reports, making it easier to interpret the data."
- "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
\ and reliability of results obtained from equipment or measurement systems. It\
\ quantifies the potential error or margin of error in measurements.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the equipment manufacturer's manual.\n - This uncertainty\
\ serves as a starting point for further calculations related to the equipment.\n\
\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated\
\ for the overall flow measurement.\n - It depends on the uncertainties of\
\ the individual variables (magnitudes) and represents the combined margin of\
\ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
\ (variables) are the foundation for calculating the uncertainty of the measurement\
\ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
\ types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific\
\ to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
\ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
\ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores\
\ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values\
\ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
- To find the uncertainty of the measurement system, join the measurement systems\
\ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
\ of a specific variable (magnitude), join the measurement systems table with\
\ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
\ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
\ of the measurement system, use the first join (measurement systems table + uncertainty\
\ of the measurement system table).\n- If the user requests the uncertainty of\
\ a specific variable (magnitude) in a report, use the second join (measurement\
\ systems table + uncertainty of magnitudes table)."
- source_sentence: What is the primary key of the flow computer table?
sentences:
- 'What is equipment calibration?
Calibration is a metrological verification process used to ensure the accuracy
of measurement equipment. It is performed periodically, based on intervals set
by the company or a regulatory body.
Purpose of calibration:
The calibration process corrects any deviations in how the equipment measures
physical magnitudes (variables). This ensures the equipment provides accurate
and reliable data.
Calibration cycles:
There are two main calibration cycles:
1. As-found: Represents the equipment''s measurement accuracy before any adjustments
are made. This cycle is almost always implemented.
2. As-left: Represents the equipment''s measurement accuracy after adjustments
are made. This cycle is used depending on regulatory requirements.
Calibration uncertainty:
- Uncertainty is included in the results of a calibration.
- Calibration uncertainty refers to the margin of error in the device''s measurements,
which also affects the uncertainty of the measured variable or magnitude.'
- 'What is equipment calibration?
Calibration is a metrological verification process used to ensure the accuracy
of measurement equipment. It is performed periodically, based on intervals set
by the company or a regulatory body.
Purpose of calibration:
The calibration process corrects any deviations in how the equipment measures
physical magnitudes (variables). This ensures the equipment provides accurate
and reliable data.
Calibration cycles:
There are two main calibration cycles:
1. As-found: Represents the equipment''s measurement accuracy before any adjustments
are made. This cycle is almost always implemented.
2. As-left: Represents the equipment''s measurement accuracy after adjustments
are made. This cycle is used depending on regulatory requirements.
Calibration uncertainty:
- Uncertainty is included in the results of a calibration.
- Calibration uncertainty refers to the margin of error in the device''s measurements,
which also affects the uncertainty of the measured variable or magnitude.'
- "How does a flow computer generate and store reports?\nA flow computer generates\
\ daily or hourly reports to provide users with operational data. These reports\
\ are stored in the flow computer's memory in an organized format.\n\nReport structure:\n\
- Each report includes:\n- Date and time of the data recording.\n- Data recorded\
\ from flow computers.\n\nData storage in tables:\nThe reports are saved in two\
\ tables:\n1. Main table (Index):\n - Stores the date, time, and flow computer\
\ identifier.\n2. Detail table:\n - Stores the measured values associated with\
\ the report.\n\nConnection to the Modbus table:\nThe flow computer's reports\
\ are linked to a Modbus table. This table contains the names corresponding to\
\ each value in the reports, making it easier to interpret the data."
- source_sentence: Can you provide a sample query to test the retrieval of the uncertainty
result for the specified tag and date?
sentences:
- 'What is equipment calibration?
Calibration is a metrological verification process used to ensure the accuracy
of measurement equipment. It is performed periodically, based on intervals set
by the company or a regulatory body.
Purpose of calibration:
The calibration process corrects any deviations in how the equipment measures
physical magnitudes (variables). This ensures the equipment provides accurate
and reliable data.
Calibration cycles:
There are two main calibration cycles:
1. As-found: Represents the equipment''s measurement accuracy before any adjustments
are made. This cycle is almost always implemented.
2. As-left: Represents the equipment''s measurement accuracy after adjustments
are made. This cycle is used depending on regulatory requirements.
Calibration uncertainty:
- Uncertainty is included in the results of a calibration.
- Calibration uncertainty refers to the margin of error in the device''s measurements,
which also affects the uncertainty of the measured variable or magnitude.'
- 'What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature, and
volume (magnitudes). This data is essential for users to perform various calculations.
Data storage:
- The measured values are stored in a special table in the database for magnitudes.
This table contains the values of the variables captured by the equipments.
- These values are **direct measurements** from the fluid (e.g., raw pressure,
temperature, or volume readings). **They are not calculated values**, such as
uncertainty.
- The values stored in the variable values table are **different** from variable
uncertainty values, which are calculated separately and represent the margin of
error.
Accessing the data:
- Users typically access the data by referring to the readings from the measurement
system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to another
table that stores information about the types of variables.'
- "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
\ and reliability of results obtained from equipment or measurement systems. It\
\ quantifies the potential error or margin of error in measurements.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the equipment manufacturer's manual.\n - This uncertainty\
\ serves as a starting point for further calculations related to the equipment.\n\
\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated\
\ for the overall flow measurement.\n - It depends on the uncertainties of\
\ the individual variables (magnitudes) and represents the combined margin of\
\ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
\ (variables) are the foundation for calculating the uncertainty of the measurement\
\ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
\ types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific\
\ to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
\ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
\ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores\
\ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values\
\ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
- To find the uncertainty of the measurement system, join the measurement systems\
\ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
\ of a specific variable (magnitude), join the measurement systems table with\
\ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
\ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
\ of the measurement system, use the first join (measurement systems table + uncertainty\
\ of the measurement system table).\n- If the user requests the uncertainty of\
\ a specific variable (magnitude) in a report, use the second join (measurement\
\ systems table + uncertainty of magnitudes table)."
- source_sentence: How are the secondary equipment and measurement system related?
sentences:
- 'What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature, and
volume (magnitudes). This data is essential for users to perform various calculations.
Data storage:
- The measured values are stored in a special table in the database for magnitudes.
This table contains the values of the variables captured by the equipments.
- These values are **direct measurements** from the fluid (e.g., raw pressure,
temperature, or volume readings). **They are not calculated values**, such as
uncertainty.
- The values stored in the variable values table are **different** from variable
uncertainty values, which are calculated separately and represent the margin of
error.
Accessing the data:
- Users typically access the data by referring to the readings from the measurement
system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to another
table that stores information about the types of variables.'
- 'What do measurement equipment measure?
Each equipment measures a physical magnitude, also known as a variable. Based
on the type of variable they measure, devices are classified into different categories.
Equipment classification:
- Primary meter: Assigned by default to equipments like orifice plates.
- Secondary meter: Assigned by default to equipments like transmitters.
- Tertiary meter: Used for other types of equipments.
Equipment types in the database:
The database includes a table listing all equipment types. Examples of equipment
types are:
- Differential pressure transmitters
- RTDs (Resistance Temperature Detectors)
- Orifice plates
- Multivariable transmitters
- Ultrasonic meters
Meteorological checks for equipments:
Each equipment type is assigned a meteorological check, which can be either:
- Calibration: To ensure measurement accuracy.
- Inspection: To verify proper functioning.
Data storage in tables:
The database also includes a separate table for equipment classifications, which
are:
- Primary meter
- Secondary meter
- Tertiary meter
So, an equipment has equipment types and this types has classifications.'
- 'What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature, and
volume (magnitudes). This data is essential for users to perform various calculations.
Data storage:
- The measured values are stored in a special table in the database for magnitudes.
This table contains the values of the variables captured by the equipments.
- These values are **direct measurements** from the fluid (e.g., raw pressure,
temperature, or volume readings). **They are not calculated values**, such as
uncertainty.
- The values stored in the variable values table are **different** from variable
uncertainty values, which are calculated separately and represent the margin of
error.
Accessing the data:
- Users typically access the data by referring to the readings from the measurement
system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to another
table that stores information about the types of variables.'
- source_sentence: What is the table structure for secondary equipment?
sentences:
- 'What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature, and
volume (magnitudes). This data is essential for users to perform various calculations.
Data storage:
- The measured values are stored in a special table in the database for magnitudes.
This table contains the values of the variables captured by the equipments.
- These values are **direct measurements** from the fluid (e.g., raw pressure,
temperature, or volume readings). **They are not calculated values**, such as
uncertainty.
- The values stored in the variable values table are **different** from variable
uncertainty values, which are calculated separately and represent the margin of
error.
Accessing the data:
- Users typically access the data by referring to the readings from the measurement
system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to another
table that stores information about the types of variables.'
- 'How are flow computers and measurement systems related?
Flow computers can have multiple systems assigned to them. However, a measurement
system can only be assigned to one flow computer.
Database terminology:
In the database, this relationship is referred to as:
- Meter streams
- Meter runs
- Sections
Storage of the relationship:
The relationship between a flow computer and its assigned measurement system is
stored in a special table.
User context:
When a user refers to a "meter stream," they are indicating that they are searching
for a measurement system assigned to a specific flow computer.'
- 'How are flow computers and measurement systems related?
Flow computers can have multiple systems assigned to them. However, a measurement
system can only be assigned to one flow computer.
Database terminology:
In the database, this relationship is referred to as:
- Meter streams
- Meter runs
- Sections
Storage of the relationship:
The relationship between a flow computer and its assigned measurement system is
stored in a special table.
User context:
When a user refers to a "meter stream," they are indicating that they are searching
for a measurement system assigned to a specific flow computer.'
datasets:
- Lauther/measuring-embeddings-v3
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision c9e87c786ffac96aeaeb42863276930883923ecb -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/measuring-embeddings-v3-multilingual-e5-large-instruct-20e")
# Run inference
sentences = [
'What is the table structure for secondary equipment?',
'How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.',
'What kind of data store an equipment?\nEquipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.\n\nData storage:\n- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.\n- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.\n- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.\n\nAccessing the data:\n- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.\n- The readings are stored in a "variable values" table within the database.\n\nLinking variable names:\nIf the user needs to know the name of a variable, they must link the data to another table that stores information about the types of variables.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### measuring-embeddings-v3
* Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87)
* Size: 7,552 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 9 tokens</li><li>mean: 15.96 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 255.56 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.22</li><li>max: 0.95</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>How can I combine the sub-query with the main query to fetch the last uncertainty report?</code> | <code>What do measurement equipment measure?<br>Each equipment measures a physical magnitude, also known as a variable. Based on the type of variable they measure, devices are classified into different categories.<br><br>Equipment classification:<br>- Primary meter: Assigned by default to equipments like orifice plates.<br>- Secondary meter: Assigned by default to equipments like transmitters.<br>- Tertiary meter: Used for other types of equipments.<br><br>Equipment types in the database:<br>The database includes a table listing all equipment types. Examples of equipment types are:<br>- Differential pressure transmitters<br>- RTDs (Resistance Temperature Detectors)<br>- Orifice plates<br>- Multivariable transmitters<br>- Ultrasonic meters<br><br>Meteorological checks for equipments:<br>Each equipment type is assigned a meteorological check, which can be either:<br>- Calibration: To ensure measurement accuracy.<br>- Inspection: To verify proper functioning.<br><br>Data storage in tables:<br>The database also includes a separate table for equipment classific...</code> | <code>0.1</code> |
| <code>What is the column name for the calibration date in the calibration table?</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code> | <code>0.1</code> |
| <code>What is the name of the table that contains the flow computer tags?</code> | <code>What is equipment calibration?<br>Calibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body.<br><br>Purpose of calibration:<br>The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.<br><br>Calibration cycles:<br>There are two main calibration cycles:<br>1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.<br>2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.<br><br>Calibration uncertainty:<br>- Uncertainty is included in the results of a calibration.<br>- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...</code> | <code>0.05</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### measuring-embeddings-v3
* Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87)
* Size: 1,618 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 9 tokens</li><li>mean: 15.83 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 250.41 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 0.95</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Identify any additional tables or columns that might be needed for the query.</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code> | <code>0.2</code> |
| <code>What columns in these tables contain the measurement system tag and the flow computer tag?</code> | <code>How does a flow computer generate and store reports?<br>A flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format.<br><br>Report structure:<br>- Each report includes:<br>- Date and time of the data recording.<br>- Data recorded from flow computers.<br><br>Data storage in tables:<br>The reports are saved in two tables:<br>1. Main table (Index):<br> - Stores the date, time, and flow computer identifier.<br>2. Detail table:<br> - Stores the measured values associated with the report.<br><br>Connection to the Modbus table:<br>The flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.</code> | <code>0.1</code> |
| <code>Identify the column that stores the calibration number.</code> | <code>What kind of data store an equipment?<br>Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.<br><br>Data storage:<br>- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.<br>- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.<br>- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.<br><br>Accessing the data:<br>- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.<br>- The readings are stored in a "variable values" table within the database.<br><br>Linking variable names:<br>If the user needs to kno...</code> | <code>0.1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 7
- `per_device_eval_batch_size`: 7
- `gradient_accumulation_steps`: 4
- `learning_rate`: 3e-05
- `num_train_epochs`: 20
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 7
- `per_device_eval_batch_size`: 7
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 20
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 9.5153 | 2560 | 6.782 | - |
| 9.5524 | 2570 | 7.3027 | - |
| 9.5894 | 2580 | 7.3348 | - |
| 9.6265 | 2590 | 7.7864 | - |
| 9.6636 | 2600 | 6.3552 | - |
| 9.7006 | 2610 | 7.151 | - |
| 9.7377 | 2620 | 6.1664 | - |
| 9.7748 | 2630 | 6.0398 | - |
| 9.8119 | 2640 | 7.0452 | - |
| 9.8489 | 2650 | 7.2457 | - |
| 9.8860 | 2660 | 6.7531 | - |
| 9.9231 | 2670 | 6.7149 | - |
| 9.9601 | 2680 | 6.4635 | - |
| 9.9972 | 2690 | 6.2237 | - |
| 10.0371 | 2700 | 6.1798 | 2.9939 |
| 10.0741 | 2710 | 7.2224 | - |
| 10.1112 | 2720 | 6.5327 | - |
| 10.1483 | 2730 | 7.4686 | - |
| 10.1854 | 2740 | 6.1404 | - |
| 10.2224 | 2750 | 7.0005 | - |
| 10.2595 | 2760 | 5.7726 | - |
| 10.2966 | 2770 | 6.5327 | - |
| 10.3336 | 2780 | 7.5015 | - |
| 10.3707 | 2790 | 6.5526 | - |
| 10.4078 | 2800 | 6.2078 | - |
| 10.4449 | 2810 | 6.1 | - |
| 10.4819 | 2820 | 7.1027 | - |
| 10.5190 | 2830 | 8.639 | - |
| 10.5561 | 2840 | 6.9937 | - |
| 10.5931 | 2850 | 7.2734 | 2.8532 |
| 10.6302 | 2860 | 7.6321 | - |
| 10.6673 | 2870 | 7.5788 | - |
| 10.7044 | 2880 | 6.7864 | - |
| 10.7414 | 2890 | 7.4237 | - |
| 10.7785 | 2900 | 6.9813 | - |
| 10.8156 | 2910 | 6.6884 | - |
| 10.8526 | 2920 | 6.7464 | - |
| 10.8897 | 2930 | 7.7989 | - |
| 10.9268 | 2940 | 7.3568 | - |
| 10.9639 | 2950 | 8.6706 | - |
| 11.0 | 2960 | 6.5687 | - |
| 11.0371 | 2970 | 5.8992 | - |
| 11.0741 | 2980 | 6.4543 | - |
| 11.1112 | 2990 | 6.1386 | - |
| 11.1483 | 3000 | 6.9047 | 2.9147 |
| 11.1854 | 3010 | 7.405 | - |
| 11.2224 | 3020 | 7.5441 | - |
| 11.2595 | 3030 | 6.7524 | - |
| 11.2966 | 3040 | 7.698 | - |
| 11.3336 | 3050 | 7.6167 | - |
| 11.3707 | 3060 | 7.1516 | - |
| 11.4078 | 3070 | 6.7458 | - |
| 11.4449 | 3080 | 6.7608 | - |
| 11.4819 | 3090 | 7.1508 | - |
| 11.5190 | 3100 | 6.9155 | - |
| 11.5561 | 3110 | 6.6664 | - |
| 11.5931 | 3120 | 8.3841 | - |
| 11.6302 | 3130 | 7.1934 | - |
| 11.6673 | 3140 | 6.9681 | - |
| 11.7044 | 3150 | 7.2187 | 2.7509 |
| 11.7414 | 3160 | 7.3155 | - |
| 11.7785 | 3170 | 7.3103 | - |
| 11.8156 | 3180 | 7.1959 | - |
| 11.8526 | 3190 | 6.8164 | - |
| 11.8897 | 3200 | 7.5836 | - |
| 11.9268 | 3210 | 5.2671 | - |
| 11.9639 | 3220 | 6.4929 | - |
| 12.0 | 3230 | 7.0892 | - |
| 12.0371 | 3240 | 7.0877 | - |
| 12.0741 | 3250 | 5.8302 | - |
| 12.1112 | 3260 | 5.6145 | - |
| 12.1483 | 3270 | 6.5808 | - |
| 12.1854 | 3280 | 6.6826 | - |
| 12.2224 | 3290 | 5.9819 | - |
| 12.2595 | 3300 | 6.68 | 3.0175 |
| 12.2966 | 3310 | 6.1685 | - |
| 12.3336 | 3320 | 6.4473 | - |
| 12.3707 | 3330 | 6.3965 | - |
| 12.4078 | 3340 | 6.6278 | - |
| 12.4449 | 3350 | 5.4575 | - |
| 12.4819 | 3360 | 7.3019 | - |
| 12.5190 | 3370 | 7.4843 | - |
| 12.5561 | 3380 | 6.709 | - |
| 12.5931 | 3390 | 6.7168 | - |
| 12.6302 | 3400 | 7.0223 | - |
| 12.6673 | 3410 | 6.5089 | - |
| 12.7044 | 3420 | 6.5094 | - |
| 12.7414 | 3430 | 7.2317 | - |
| 12.7785 | 3440 | 6.6885 | - |
| 12.8156 | 3450 | 6.9693 | 2.8462 |
| 12.8526 | 3460 | 6.8242 | - |
| 12.8897 | 3470 | 6.6899 | - |
| 12.9268 | 3480 | 6.9113 | - |
| 12.9639 | 3490 | 7.1903 | - |
| 13.0 | 3500 | 7.3286 | - |
| 13.0371 | 3510 | 6.5465 | - |
| 13.0741 | 3520 | 5.6804 | - |
| 13.1112 | 3530 | 5.6412 | - |
| 13.1483 | 3540 | 6.6161 | - |
| 13.1854 | 3550 | 5.761 | - |
| 13.2224 | 3560 | 5.5669 | - |
| 13.2595 | 3570 | 5.6184 | - |
| 13.2966 | 3580 | 6.2996 | - |
| 13.3336 | 3590 | 4.99 | - |
| 13.3707 | 3600 | 5.9974 | 3.2358 |
| 13.4078 | 3610 | 5.6962 | - |
| 13.4449 | 3620 | 6.3662 | - |
| 13.4819 | 3630 | 7.0398 | - |
| 13.5190 | 3640 | 7.7358 | - |
| 13.5561 | 3650 | 7.9063 | - |
| 13.5931 | 3660 | 5.7823 | - |
| 13.6302 | 3670 | 6.9861 | - |
| 13.6673 | 3680 | 7.2855 | - |
| 13.7044 | 3690 | 5.6785 | - |
| 13.7414 | 3700 | 6.4071 | - |
| 13.7785 | 3710 | 6.4294 | - |
| 13.8156 | 3720 | 6.0842 | - |
| 13.8526 | 3730 | 5.9422 | - |
| 13.8897 | 3740 | 7.0778 | - |
| 13.9268 | 3750 | 8.1597 | 3.0093 |
| 13.9639 | 3760 | 6.3154 | - |
| 14.0 | 3770 | 6.2416 | - |
| 14.0371 | 3780 | 5.9958 | - |
| 14.0741 | 3790 | 5.7032 | - |
| 14.1112 | 3800 | 4.9524 | - |
| 14.1483 | 3810 | 5.386 | - |
| 14.1854 | 3820 | 5.6353 | - |
| 14.2224 | 3830 | 5.0873 | - |
| 14.2595 | 3840 | 4.9255 | - |
| 14.2966 | 3850 | 5.1423 | - |
| 14.3336 | 3860 | 6.0775 | - |
| 14.3707 | 3870 | 4.5073 | - |
| 14.4078 | 3880 | 6.8347 | - |
| 14.4449 | 3890 | 6.5397 | - |
| 14.4819 | 3900 | 7.2143 | 3.3080 |
| 14.5190 | 3910 | 6.1123 | - |
| 14.5561 | 3920 | 6.6048 | - |
| 14.5931 | 3930 | 6.3464 | - |
| 14.6302 | 3940 | 6.3618 | - |
| 14.6673 | 3950 | 6.5718 | - |
| 14.7044 | 3960 | 5.9785 | - |
| 14.7414 | 3970 | 6.5758 | - |
| 14.7785 | 3980 | 6.4308 | - |
| 14.8156 | 3990 | 6.0208 | - |
| 14.8526 | 4000 | 6.0303 | - |
| 14.8897 | 4010 | 6.6396 | - |
| 14.9268 | 4020 | 6.0184 | - |
| 14.9639 | 4030 | 6.6248 | - |
| 15.0 | 4040 | 6.4538 | - |
| 15.0371 | 4050 | 6.4742 | 3.1761 |
| 15.0741 | 4060 | 5.5295 | - |
| 15.1112 | 4070 | 6.8753 | - |
| 15.1483 | 4080 | 5.639 | - |
| 15.1854 | 4090 | 5.6232 | - |
| 15.2224 | 4100 | 6.3026 | - |
| 15.2595 | 4110 | 6.1182 | - |
| 15.2966 | 4120 | 5.4736 | - |
| 15.3336 | 4130 | 6.2961 | - |
| 15.3707 | 4140 | 5.4742 | - |
| 15.4078 | 4150 | 5.4707 | - |
| 15.4449 | 4160 | 4.7272 | - |
| 15.4819 | 4170 | 6.1026 | - |
| 15.5190 | 4180 | 5.0468 | - |
| 15.5561 | 4190 | 5.5796 | - |
| 15.5931 | 4200 | 6.9046 | 3.1433 |
| 15.6302 | 4210 | 5.6123 | - |
| 15.6673 | 4220 | 6.7246 | - |
| 15.7044 | 4230 | 5.7076 | - |
| 15.7414 | 4240 | 6.6772 | - |
| 15.7785 | 4250 | 5.6038 | - |
| 15.8156 | 4260 | 4.9544 | - |
| 15.8526 | 4270 | 5.0661 | - |
| 15.8897 | 4280 | 5.291 | - |
| 15.9268 | 4290 | 6.6652 | - |
| 15.9639 | 4300 | 5.6797 | - |
| 16.0 | 4310 | 5.1129 | - |
| 16.0371 | 4320 | 5.4445 | - |
| 16.0741 | 4330 | 4.8946 | - |
| 16.1112 | 4340 | 6.3929 | - |
| 16.1483 | 4350 | 6.0633 | 3.1426 |
| 16.1854 | 4360 | 5.522 | - |
| 16.2224 | 4370 | 4.7067 | - |
| 16.2595 | 4380 | 5.4688 | - |
| 16.2966 | 4390 | 5.6009 | - |
| 16.3336 | 4400 | 5.1376 | - |
| 16.3707 | 4410 | 4.5196 | - |
| 16.4078 | 4420 | 5.5109 | - |
| 16.4449 | 4430 | 5.1888 | - |
| 16.4819 | 4440 | 6.0305 | - |
| 16.5190 | 4450 | 5.2791 | - |
| 16.5561 | 4460 | 5.4005 | - |
| 16.5931 | 4470 | 5.255 | - |
| 16.6302 | 4480 | 6.2026 | - |
| 16.6673 | 4490 | 6.6388 | - |
| 16.7044 | 4500 | 5.6138 | 3.2812 |
| 16.7414 | 4510 | 4.7913 | - |
| 16.7785 | 4520 | 5.6675 | - |
| 16.8156 | 4530 | 5.8975 | - |
| 16.8526 | 4540 | 5.4597 | - |
| 16.8897 | 4550 | 5.137 | - |
| 16.9268 | 4560 | 4.5395 | - |
| 16.9639 | 4570 | 4.6304 | - |
| 17.0 | 4580 | 5.8098 | - |
| 17.0371 | 4590 | 4.0267 | - |
| 17.0741 | 4600 | 4.9194 | - |
| 17.1112 | 4610 | 4.1852 | - |
| 17.1483 | 4620 | 5.129 | - |
| 17.1854 | 4630 | 4.469 | - |
| 17.2224 | 4640 | 5.4298 | - |
| 17.2595 | 4650 | 4.5234 | 3.3447 |
| 17.2966 | 4660 | 4.6856 | - |
| 17.3336 | 4670 | 6.3431 | - |
| 17.3707 | 4680 | 5.347 | - |
| 17.4078 | 4690 | 4.9223 | - |
| 17.4449 | 4700 | 5.4404 | - |
| 17.4819 | 4710 | 4.916 | - |
| 17.5190 | 4720 | 6.1744 | - |
| 17.5561 | 4730 | 4.8039 | - |
| 17.5931 | 4740 | 5.2276 | - |
| 17.6302 | 4750 | 4.4189 | - |
| 17.6673 | 4760 | 4.1434 | - |
| 17.7044 | 4770 | 4.9443 | - |
| 17.7414 | 4780 | 5.6975 | - |
| 17.7785 | 4790 | 4.6667 | - |
| 17.8156 | 4800 | 4.9876 | 3.2924 |
| 17.8526 | 4810 | 4.4342 | - |
| 17.8897 | 4820 | 5.2595 | - |
| 17.9268 | 4830 | 5.6566 | - |
| 17.9639 | 4840 | 5.5452 | - |
| 18.0 | 4850 | 4.4986 | - |
| 18.0371 | 4860 | 4.8155 | - |
| 18.0741 | 4870 | 4.2278 | - |
| 18.1112 | 4880 | 5.4733 | - |
| 18.1483 | 4890 | 4.2394 | - |
| 18.1854 | 4900 | 5.1253 | - |
| 18.2224 | 4910 | 4.7498 | - |
| 18.2595 | 4920 | 4.9775 | - |
| 18.2966 | 4930 | 4.797 | - |
| 18.3336 | 4940 | 4.5694 | - |
| 18.3707 | 4950 | 4.6192 | 3.6615 |
| 18.4078 | 4960 | 5.8114 | - |
| 18.4449 | 4970 | 4.8035 | - |
| 18.4819 | 4980 | 4.6944 | - |
| 18.5190 | 4990 | 4.8664 | - |
| 18.5561 | 5000 | 4.6916 | - |
| 18.5931 | 5010 | 4.3352 | - |
| 18.6302 | 5020 | 5.9779 | - |
| 18.6673 | 5030 | 4.7813 | - |
| 18.7044 | 5040 | 4.632 | - |
| 18.7414 | 5050 | 4.7411 | - |
| 18.7785 | 5060 | 3.6489 | - |
| 18.8156 | 5070 | 4.5373 | - |
| 18.8526 | 5080 | 5.6129 | - |
| 18.8897 | 5090 | 4.8933 | - |
| 18.9268 | 5100 | 4.27 | 3.6957 |
| 18.9639 | 5110 | 4.5338 | - |
| 19.0 | 5120 | 5.5175 | - |
| 19.0371 | 5130 | 5.0835 | - |
| 19.0741 | 5140 | 4.6826 | - |
| 19.1112 | 5150 | 4.5391 | - |
| 19.1483 | 5160 | 5.3723 | - |
| 19.1854 | 5170 | 4.8095 | - |
| 19.2224 | 5180 | 4.7402 | - |
| 19.2595 | 5190 | 4.0488 | - |
| 19.2966 | 5200 | 3.6424 | - |
| 19.3336 | 5210 | 4.2256 | - |
| 19.3707 | 5220 | 4.4607 | - |
| 19.4078 | 5230 | 3.5702 | - |
| 19.4449 | 5240 | 4.3062 | - |
| 19.4819 | 5250 | 4.2919 | 3.6594 |
| 19.5190 | 5260 | 4.6985 | - |
| 19.5561 | 5270 | 4.6907 | - |
| 19.5931 | 5280 | 4.3865 | - |
| 19.6302 | 5290 | 3.9818 | - |
| 19.6673 | 5300 | 4.3166 | - |
| 19.7044 | 5310 | 4.9131 | - |
| 19.7414 | 5320 | 4.7641 | - |
| 19.7785 | 5330 | 5.419 | - |
| 19.8156 | 5340 | 4.068 | - |
| 19.8526 | 5350 | 4.1094 | - |
| 19.8897 | 5360 | 5.2279 | - |
| 19.9268 | 5370 | 4.4818 | - |
| 19.9639 | 5380 | 4.3103 | - |
</details>
### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
kandinsky-community/kandinsky-2-2-decoder-inpaint | kandinsky-community | "2023-10-09T11:33:04Z" | 23,424 | 25 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"kandinsky",
"license:apache-2.0",
"diffusers:KandinskyV22InpaintPipeline",
"region:us"
] | text-to-image | "2023-06-16T17:14:36Z" | ---
license: apache-2.0
prior:
- kandinsky-community/kandinsky-2-2-prior
tags:
- text-to-image
- kandinsky
inference: false
---
# Kandinsky 2.2
Kandinsky inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas.
It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.
The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov)
## Usage
Kandinsky 2.2 is available in diffusers!
```python
pip install diffusers transformers accelerate
```
### Text Guided Inpainting Generation
```python
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
import torch
import numpy as np
pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = "a hat"
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
)
mask = np.zeros((768, 768), dtype=np.float32)
# Let's mask out an area above the cat's head
mask[:250, 250:-250] = 1
out = pipe(
prompt=prompt,
image=init_image,
mask_image=mask,
height=768,
width=768,
num_inference_steps=150,
)
image = out.images[0]
image.save("cat_with_hat.png")
```
🚨🚨🚨 __Breaking change for Kandinsky Mask Inpainting__ 🚨🚨🚨
We introduced a breaking change for Kandinsky inpainting pipeline in the following pull request: https://github.com/huggingface/diffusers/pull/4207. Previously we accepted a mask format where black pixels represent the masked-out area. This is inconsistent with all other pipelines in diffusers. We have changed the mask format in Knaindsky and now using white pixels instead.
Please upgrade your inpainting code to follow the above. If you are using Kandinsky Inpaint in production. You now need to change the mask to:
```python
# For PIL input
import PIL.ImageOps
mask = PIL.ImageOps.invert(mask)
# For PyTorch and Numpy input
mask = 1 - mask
```
## Model Architecture
### Overview
Kandinsky 2.1 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder.
The model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation.
<p float="left">
<img src="https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png"/>
</p>
Specifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [mCLIP model](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14). The trained image prior model is then used to generate mCLIP image embeddings for input text prompts. Both the input text prompts and its mCLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image.
### Details
The image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution).
The main Text2Image diffusion model was trained on the basis of 170M text-image pairs from the [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) (an important condition was the presence of images with a resolution of at least 768x768). The use of 170M pairs is due to the fact that we kept the UNet diffusion block from Kandinsky 2.0, which allowed us not to train it from scratch. Further, at the stage of fine-tuning, a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources.
### Evaluation
We quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID.
FID metric values for generative models on COCO_30k
| | FID (30k)|
|:------|----:|
| eDiff-I (2022) | 6.95 |
| Image (2022) | 7.27 |
| Kandinsky 2.1 (2023) | 8.21|
| Stable Diffusion 2.1 (2022) | 8.59 |
| GigaGAN, 512x512 (2023) | 9.09 |
| DALL-E 2 (2022) | 10.39 |
| GLIDE (2022) | 12.24 |
| Kandinsky 1.0 (2022) | 15.40 |
| DALL-E (2021) | 17.89 |
| Kandinsky 2.0 (2022) | 20.00 |
| GLIGEN (2022) | 21.04 |
For more information, please refer to the upcoming technical report.
## BibTex
If you find this repository useful in your research, please cite:
```
@misc{kandinsky 2.2,
title = {kandinsky 2.2},
author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov},
year = {2023},
howpublished = {},
}
``` |
cuongdz01/layoutlmv3-funsd | cuongdz01 | "2024-01-03T18:05:26Z" | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2024-01-03T17:25:06Z" | ---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-funsd
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8428
- Precision: 0.8993
- Recall: 0.9046
- F1: 0.9019
- Accuracy: 0.8354
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.63 | 100 | 0.6294 | 0.7864 | 0.8286 | 0.8070 | 0.7966 |
| No log | 5.26 | 200 | 0.5034 | 0.8389 | 0.8793 | 0.8586 | 0.8343 |
| No log | 7.89 | 300 | 0.5673 | 0.8597 | 0.9011 | 0.8799 | 0.8416 |
| No log | 10.53 | 400 | 0.5730 | 0.8783 | 0.9106 | 0.8941 | 0.8395 |
| 0.4463 | 13.16 | 500 | 0.6630 | 0.8923 | 0.9016 | 0.8970 | 0.8412 |
| 0.4463 | 15.79 | 600 | 0.7048 | 0.8850 | 0.8947 | 0.8898 | 0.8329 |
| 0.4463 | 18.42 | 700 | 0.7772 | 0.8925 | 0.9071 | 0.8997 | 0.8317 |
| 0.4463 | 21.05 | 800 | 0.8408 | 0.8959 | 0.9016 | 0.8987 | 0.8313 |
| 0.4463 | 23.68 | 900 | 0.8580 | 0.8918 | 0.9051 | 0.8984 | 0.8313 |
| 0.0611 | 26.32 | 1000 | 0.8428 | 0.8993 | 0.9046 | 0.9019 | 0.8354 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Ruidie/local_output | Ruidie | "2025-04-02T12:43:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-02T12:43:41Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
John6666/illunoob11-v10-sdxl | John6666 | "2025-03-21T12:05:15Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"QuantumMerge",
"NL prompt abilities",
"artists",
"characters",
"noobai",
"Illustrious XL v1.1",
"illustrious",
"en",
"base_model:lylogummy/illuNoob-1.1",
"base_model:finetune:lylogummy/illuNoob-1.1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2025-03-21T11:58:38Z" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- girls
- QuantumMerge
- NL prompt abilities
- artists
- characters
- noobai
- Illustrious XL v1.1
- illustrious
base_model: lylogummy/illuNoob-1.1
---
Original model is [here](https://huggingface.co/lylogummy/illuNoob-1.1) and on [Civitai](https://civitai.com/models/1370218/illunoob-11?modelVersionId=1548063).
The author is [here](https://huggingface.co/lylogummy).
This model created by [LyloGummy](https://civitai.com/user/LyloGummy).
|
ssmits/Zamba2-1.2B | ssmits | "2024-11-06T00:37:51Z" | 5 | 0 | transformers_zamba2 | [
"transformers_zamba2",
"safetensors",
"zamba2",
"arxiv:2405.16712",
"arxiv:2406.01981",
"license:apache-2.0",
"region:us"
] | null | "2024-11-05T22:23:21Z" | ---
license: apache-2.0
library_name: transformers_zamba2
---
# Model Card for Zamba2-1.2B
Zamba2-1.2B is a hybrid model composed of state-space ([Mamba](https://github.com/state-spaces/mamba)) and transformer blocks. It broadly follows the [Zamba architecture](https://arxiv.org/abs/2405.16712) which consists of a Mamba backbone alternating with shared transformer blocks (see diagram in [Model Details](#model-details)). Zamba2-1.2B possesses three major improvements over Zamba1:
1.) Mamba1 blocks have been replaced with Mamba2 blocks.
2.) We apply a LoRA projector to each shared MLP and attention block, which allows the network to specialize at each invocation of the shared transformer layer across depth. LoRA enables us to add depth-specialization for only a minimal increase in total parameter count.
3.) We utilize rotary position embeddings in the shared attention layer.
Zamba2-1.2B differs from our [2.7B model](https://huggingface.co/Zyphra/Zamba2-2.7B) in three ways:
1.) We have added rotary position embeddings
2.) A single shared transformer block (instead of two that we alternate between)
3.) Added LoRA projectors to attention blocks (instead of just a LoRA on the MLP block)
We found that while hybrid SSM-transformer models are perfectly capable of performing well without position embeddings, adding rotary embeddings to the shared attention block slightly improved performance. Secondly, we utilize a single attention block (instead of alternating between two independent transformer blocks) because this enables a higher flop count for the model at a given parameter budget and at smaller scales this becomes more important than the slightly faster latency.
Zamba2-1.2B uses the Mistral v0.1 tokenizer and was pre-trained on 3T tokens of text and code data sourced from open web-datasets, including [Zyda](https://arxiv.org/abs/2406.01981). Subsequently, in a second phase, Zamba2-1.2B was annealed on a mixture of 100B high-quality tokens.
Note: this is a temporary HuggingFace implementation of Zamba2-1.2B. It may not yet be fully compatible with all frameworks and tools intended to interface with HuggingFace models.
A standalone Pytorch implementation of Zamba2-1.2B may be found [here](https://github.com/Zyphra/Zamba2).
## Quick start
### Prerequisites
To download Zamba2-1.2B, clone Zyphra's fork of transformers:
1. `git clone https://github.com/Zyphra/transformers_zamba2.git`
2. `cd transformers_zamba2`
3. Install the repository: `pip install -e .`
4. `pip install accelerate`
You can run the model without using the optimized Mamba kernels, but it is **not** recommended as it will result in significantly higher latency and memory usage.
To run on CPU, please specify `use_mamba_kernels=False` when loading the model using ``AutoModelForCausalLM.from_pretrained``.
### Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-1.2B")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-1.2B", device_map="cuda", torch_dtype=torch.bfloat16)
input_text = "What factors contributed to the fall of the Roman Empire?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))
```
## Model Details
Zamba2-1.2B utilizes and extends our original Zamba hybrid SSM-attention architecture. The core Zamba architecture consists of a backbone of Mamba layers interleaved with one or more shared attention layers. This attention has shared weights to minimize the parameter cost of the model. We find that concatenating the original model embeddings to the input to this attention block improves performance, likely due to better maintenance of information across depth. The Zamba2 architecture also applies LoRA projection matrices to the shared transformer blocks to gain some additional expressivity in each block and allow each shared block to specialize slightly to its own unique position while keeping the additional parameter overhead small.
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/Vay6htbnBcySR3Z6NEgwj.png" width="300" alt="Zamba architecture">
</center>
## Performance
Zamba2-1.2B achieves leading and state-of-the-art performance among models of <2B parameters and is competitive with some models of significantly greater size. Moreover, due to its unique hybrid SSM architecture, Zamba2-1.2B achieves extremely low inference latency and rapid generation with a significantly smaller memory footprint than comparable transformer based models.
Zamba2-1.2B's high performance and small inference compute and memory footprint renders it an ideal generalist model for on-device applications.
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65bc13717c6ad1994b6619e9/7Japy8VaJzKaFEjJgtWBp.png" width="700" alt="Zamba performance">
</center>
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/Viwo3-bpYLFUu7cLIUFVv.png" width="800" alt="Zamba performance">
</center>
<!--
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/JVZUvVMPIpIJy9RDyohMJ.png" width="800" alt="Zamba performance">
</center>
-->
Time to First Token (TTFT) | Output Generation
:-------------------------:|:-------------------------:
 | 
And memory overhead
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/m0YUmAmiVnRg6l9m10CEt.png" width="400" alt="Zamba inference and memory cost">
</center>
## Notice
Zamba2-1.2B is a pretrained base model and therefore does not have any moderation mechanism and may output toxic or otherwise harmful language. In addition, one should not expect good instruct or chat performance, as this model was not fine-tuned for instruction following or chat.
|
iTroned/Olid_roberta_size_1 | iTroned | "2025-02-18T18:51:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-02-18T18:49:35Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Olid_roberta_size_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/ix74ytnr)
# Olid_roberta_size_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
julien-rsbrg/ppo-Huggy | julien-rsbrg | "2022-12-19T14:46:20Z" | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2022-12-19T14:46:09Z" |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: julien-rsbrg/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mrferr3t/7374d6bb-cb3f-475d-9720-0e8fbc87f698 | mrferr3t | "2025-02-02T10:17:46Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Hermes-3-Llama-3.1-8B",
"base_model:adapter:unsloth/Hermes-3-Llama-3.1-8B",
"region:us"
] | null | "2025-02-02T09:57:10Z" | ---
library_name: peft
base_model: unsloth/Hermes-3-Llama-3.1-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7374d6bb-cb3f-475d-9720-0e8fbc87f698
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Hermes-3-Llama-3.1-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 15644d45e6aa13ca_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/15644d45e6aa13ca_train_data.json
type:
field_instruction: prompt
field_output: solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 50
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/7374d6bb-cb3f-475d-9720-0e8fbc87f698
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0005
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 99
micro_batch_size: 2
mlflow_experiment_name: /tmp/15644d45e6aa13ca_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 300
saves_per_epoch: 0
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f8bd0c47-754a-441f-8785-3f331e2c3949
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f8bd0c47-754a-441f-8785-3f331e2c3949
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 7374d6bb-cb3f-475d-9720-0e8fbc87f698
This model is a fine-tuned version of [unsloth/Hermes-3-Llama-3.1-8B](https://huggingface.co/unsloth/Hermes-3-Llama-3.1-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 99
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6564 | 0.0000 | 1 | 0.8031 |
| 0.8027 | 0.0015 | 50 | 0.5991 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |
thangla01/d34f1bed-09e3-49e8-9acc-62ad2e95d51c | thangla01 | "2025-01-25T09:06:54Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-1.5B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-25T08:54:58Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d34f1bed-09e3-49e8-9acc-62ad2e95d51c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-1.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5b74c67a89b7c3d5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5b74c67a89b7c3d5_train_data.json
type:
field_input: test
field_instruction: question_content
field_output: starter_code
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: thangla01/d34f1bed-09e3-49e8-9acc-62ad2e95d51c
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/5b74c67a89b7c3d5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a505a240-a446-4506-9fa3-62d1f0992ad9
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a505a240-a446-4506-9fa3-62d1f0992ad9
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d34f1bed-09e3-49e8-9acc-62ad2e95d51c
This model is a fine-tuned version of [unsloth/Qwen2-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7366
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7485 | 0.8753 | 200 | 0.7366 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
tuanio/1-epochs61.0-char-based-freeze_cnn-dropout0.1 | tuanio | "2023-10-30T01:47:20Z" | 10 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2023-10-29T23:08:02Z" | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 1-epochs61.0-char-based-freeze_cnn-dropout0.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 1-epochs61.0-char-based-freeze_cnn-dropout0.1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0164
- Wer: 0.5324
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 40
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 61.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4352 | 5.11 | 2500 | 3.6464 | 1.0 |
| 1.9084 | 10.22 | 5000 | 1.7450 | 0.9275 |
| 1.0531 | 15.34 | 7500 | 1.1180 | 0.6646 |
| 0.8217 | 20.45 | 10000 | 1.1415 | 0.6048 |
| 0.7405 | 25.56 | 12500 | 1.0814 | 0.5776 |
| 0.6432 | 30.67 | 15000 | 1.0632 | 0.5611 |
| 0.6507 | 35.79 | 17500 | 1.0200 | 0.5427 |
| 0.5533 | 40.9 | 20000 | 1.0019 | 0.5367 |
| 0.561 | 46.01 | 22500 | 1.0246 | 0.5392 |
| 0.5292 | 51.12 | 25000 | 0.9992 | 0.5245 |
| 0.5085 | 56.24 | 27500 | 1.0164 | 0.5324 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1
|
GreenBitAI/01-Yi-9B-channel-mix-bpw-2.5 | GreenBitAI | "2024-04-29T21:39:35Z" | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-15T09:23:03Z" | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
JsSparkYyx/flan-t5-base-finetuned-lora-sst2-9 | JsSparkYyx | "2023-11-18T11:26:02Z" | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | "2023-11-18T11:21:37Z" | ---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-finetuned-lora-sst2-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-finetuned-lora-sst2-9
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.1+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e-1_s6789_v3_l5_v100 | KingKazma | "2023-08-09T17:54:48Z" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2023-08-09T17:54:47Z" | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
sheraz179/blip2-flan-t5-xxl | sheraz179 | "2023-07-12T11:07:29Z" | 8 | 1 | transformers | [
"transformers",
"pytorch",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"en",
"arxiv:2301.12597",
"arxiv:2210.11416",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-to-text | "2023-07-10T10:15:55Z" | ---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
- visual-question-answering
pipeline_tag: image-to-text
inference: false
duplicated_from: Salesforce/blip2-flan-t5-xxl
---
# BLIP-2, Flan T5-xxl, pre-trained only
BLIP-2 model, leveraging [Flan T5-xxl](https://huggingface.co/google/flan-t5-xxl) (a large language model).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2).
Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.
The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen
while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings,
which bridge the gap between the embedding space of the image encoder and the large language model.
The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/>
This allows the model to be used for tasks like:
- image captioning
- visual question answering (VQA)
- chat-like conversations by feeding the image and the previous conversation as prompt to the model
## Direct Use and Downstream Use
You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
fine-tuned versions on a task that interests you.
## Bias, Risks, Limitations, and Ethical Considerations
BLIP2-FlanT5 uses off-the-shelf Flan-T5 as the language model. It inherits the same risks and limitations from [Flan-T5](https://arxiv.org/pdf/2210.11416.pdf):
> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example), or refer to the snippets below depending on your usecase:
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, Blip2ForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", torch_dtype=torch.float16, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In 8-bit precision (`int8`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate bitsandbytes
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", load_in_8bit=True, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details> |
nice2mitya/a_621432430 | nice2mitya | "2025-03-28T14:35:08Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2025-03-28T14:06:14Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical | research-backup | "2022-11-25T00:14:13Z" | 103 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2022-11-25T00:13:00Z" | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8508333333333333
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4304812834224599
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.42729970326409494
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44580322401334077
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.63
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3684210526315789
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4375
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8945306614434232
- name: F1 (macro)
type: f1_macro
value: 0.8881298132895892
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8164319248826291
- name: F1 (macro)
type: f1_macro
value: 0.5907123053940893
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6229685807150596
- name: F1 (macro)
type: f1_macro
value: 0.6054874185412089
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9565973429783682
- name: F1 (macro)
type: f1_macro
value: 0.869940503406368
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8730805390159825
- name: F1 (macro)
type: f1_macro
value: 0.8691781735538814
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4304812834224599
- Accuracy on SAT: 0.42729970326409494
- Accuracy on BATS: 0.44580322401334077
- Accuracy on U2: 0.3684210526315789
- Accuracy on U4: 0.4375
- Accuracy on Google: 0.63
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8945306614434232
- Micro F1 score on CogALexV: 0.8164319248826291
- Micro F1 score on EVALution: 0.6229685807150596
- Micro F1 score on K&H+N: 0.9565973429783682
- Micro F1 score on ROOT09: 0.8730805390159825
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8508333333333333
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 5
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
- data_level: child_prototypical
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-nce-2-child-prototypical/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
texanrangee/29b49dc8-4297-42e9-b417-ed7fa4e1e31f | texanrangee | "2025-03-13T20:04:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-03-13T18:05:24Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
TFOCUS/nYTr_75 | TFOCUS | "2025-03-17T16:50:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-17T16:48:00Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
gaianet/Llama-3.1-Nemotron-70B-Reward-HF-GGUF | gaianet | "2024-10-19T04:32:41Z" | 52 | 1 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation",
"nvidia",
"llama3.1",
"reward model",
"base_model:nvidia/Llama-3.1-Nemotron-70B-Reward-HF",
"base_model:quantized:nvidia/Llama-3.1-Nemotron-70B-Reward-HF",
"license:llama3.1",
"autotrain_compatible",
"region:us",
"conversational"
] | text-generation | "2024-10-18T03:56:57Z" | ---
license: llama3.1
model_name: Llama-3.1-Nemotron-70B-Reward-HF
base_model: nvidia/Llama-3.1-Nemotron-70B-Reward-HF
inference: false
pipeline_tag: text-generation
library_name: transformers
model_creator: nvidia
quantized_by: Second State Inc.
tags:
- nvidia
- llama3.1
- reward model
---
# Llama-3.1-Nemotron-70B-Reward-HF-GGUF
## Original Model
[nvidia/Llama-3.1-Nemotron-70B-Reward-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward-HF)
## Run with Gaianet
**Prompt template:**
prompt template: `llama-3-chat`
**Context size:**
chat_ctx_size: `128000`
**Run with GaiaNet:**
- Quick start: https://docs.gaianet.ai/node-guide/quick-start
- Customize your node: https://docs.gaianet.ai/node-guide/customize
*Quantized with llama.cpp b3932*
|
cammy/led-base-16384-100-MDS | cammy | "2022-03-23T06:55:50Z" | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"led",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-03-23T05:32:16Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-100-MDS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# led-base-16384-100-MDS
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1425
- Rouge1: 16.7324
- Rouge2: 5.8501
- Rougel: 13.908
- Rougelsum: 13.8469
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 25 | 3.6187 | 15.1426 | 4.2468 | 13.4488 | 13.38 | 20.0 |
| No log | 2.0 | 50 | 3.9873 | 13.4341 | 3.3283 | 10.2739 | 10.8229 | 20.0 |
| No log | 3.0 | 75 | 4.0264 | 18.1891 | 5.3395 | 15.0797 | 15.3586 | 20.0 |
| No log | 4.0 | 100 | 4.0929 | 17.0091 | 5.5336 | 14.4381 | 14.5149 | 19.5 |
| No log | 5.0 | 125 | 4.1425 | 16.7324 | 5.8501 | 13.908 | 13.8469 | 20.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
beast33/4b9c5946-4543-473b-ab9f-72210a0d625f | beast33 | "2025-02-04T23:44:33Z" | 9 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Mistral-Nemo-Base-2407",
"base_model:adapter:unsloth/Mistral-Nemo-Base-2407",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-02-04T23:01:51Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4b9c5946-4543-473b-ab9f-72210a0d625f
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Mistral-Nemo-Base-2407
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 41d77b8dae978320_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/41d77b8dae978320_train_data.json
type:
field_instruction: conversations
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: beast33/4b9c5946-4543-473b-ab9f-72210a0d625f
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/41d77b8dae978320_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e3cb315c-5afc-44e3-960d-e3a4880b4e20
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e3cb315c-5afc-44e3-960d-e3a4880b4e20
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 4b9c5946-4543-473b-ab9f-72210a0d625f
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2554
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3037 | 0.3716 | 200 | 0.2554 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Koutouzov1973/Llama_3.2_3B_Instruct_4bit_Distill_Situation_Generale_2_Avril | Koutouzov1973 | "2025-04-02T05:32:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"mlx",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:mlx-community/Llama-3.2-3B-Instruct",
"base_model:quantized:mlx-community/Llama-3.2-3B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] | text-generation | "2025-04-02T05:30:33Z" | ---
base_model: mlx-community/Llama-3.2-3B-Instruct
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
license: llama3.2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- mlx
- mlx
extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\
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\ Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy\
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#### Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.2 to:\n1. Violate\
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\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\
\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
\ illegal distribution of information or materials to minors, including obscene\
\ materials, or failure to employ legally required age-gating in connection with\
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\ facilitate any action, to intentionally circumvent or remove usage restrictions\
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\ in, promote, incite, facilitate, or assist in the planning or development of activities\
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\ 3.2 related to the following:\n 8. Military, warfare, nuclear industries or\
\ applications, espionage, use for materials or activities that are subject to the\
\ International Traffic Arms Regulations (ITAR) maintained by the United States\
\ Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989\
\ or the Chemical Weapons Convention Implementation Act of 1997\n 9. Guns and\
\ illegal weapons (including weapon development)\n 10. Illegal drugs and regulated/controlled\
\ substances\n 11. Operation of critical infrastructure, transportation technologies,\
\ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\
\ and eating disorders\n 13. Any content intended to incite or promote violence,\
\ abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive\
\ or mislead others, including use of Llama 3.2 related to the following:\n 14.\
\ Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n\
\ 15. Generating, promoting, or furthering defamatory content, including the\
\ creation of defamatory statements, images, or other content\n 16. Generating,\
\ promoting, or further distributing spam\n 17. Impersonating another individual\
\ without consent, authorization, or legal right\n 18. Representing that the\
\ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\
\ false online engagement, including fake reviews and other means of fake online\
\ engagement \n4. Fail to appropriately disclose to end users any known dangers\
\ of your AI system 5. Interact with third party tools, models, or software designed\
\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\
\ that the outputs of such tools, models, or software are associated with Meta or\
\ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\
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\ are not being granted to you if you are an individual domiciled in, or a company\
\ with a principal place of business in, the European Union. This restriction does\
\ not apply to end users of a product or service that incorporates any such multimodal\
\ models.\n\nPlease report any violation of this Policy, software “bug,” or other\
\ problems that could lead to a violation of this Policy through one of the following\
\ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\
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extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
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and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
# mlx-community/Llama-3.2-3B-Instruct-4bit
The Model [mlx-community/Llama-3.2-3B-Instruct-4bit](https://huggingface.co/mlx-community/Llama-3.2-3B-Instruct-4bit) was
converted to MLX format from [mlx-community/Llama-3.2-3B-Instruct](https://huggingface.co/mlx-community/Llama-3.2-3B-Instruct)
using mlx-lm version **0.21.5**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3.2-3B-Instruct-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
grace-pro/one_half_data_high_rank_even_more_params | grace-pro | "2024-03-10T23:29:10Z" | 1 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | "2024-03-10T23:26:59Z" | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: one_half_data_high_rank_even_more_params
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# one_half_data_high_rank_even_more_params
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7115
- Precision: 0.8275
- Recall: 0.9492
- F1-score: 0.8842
- Accuracy: 0.8394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.5522 | 1.0 | 24544 | 0.7115 | 0.8275 | 0.9492 | 0.8842 | 0.8394 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
duyphu/49601c3b-affc-468c-89cd-e8bafd28ec29 | duyphu | "2025-01-24T23:40:32Z" | 5 | 0 | peft | [
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codegemma-7b",
"base_model:adapter:unsloth/codegemma-7b",
"license:apache-2.0",
"region:us"
] | null | "2025-01-24T22:54:47Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codegemma-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 49601c3b-affc-468c-89cd-e8bafd28ec29
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/codegemma-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- aebf01b69372b3a5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/aebf01b69372b3a5_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 5
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: duyphu/49601c3b-affc-468c-89cd-e8bafd28ec29
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 5
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/aebf01b69372b3a5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d02f8420-c73c-4f8a-8201-92e9e6652393
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d02f8420-c73c-4f8a-8201-92e9e6652393
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 49601c3b-affc-468c-89cd-e8bafd28ec29
This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2380
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 1.3819 |
| 1.3763 | 0.0016 | 10 | 1.3365 |
| 1.2041 | 0.0033 | 20 | 1.2745 |
| 1.195 | 0.0049 | 30 | 1.2487 |
| 1.2494 | 0.0065 | 40 | 1.2402 |
| 1.2229 | 0.0082 | 50 | 1.2380 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
XiaoFang1019/chatglm2-6b_298_v1 | XiaoFang1019 | "2024-04-03T05:35:01Z" | 2 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:THUDM/chatglm2-6b",
"base_model:adapter:THUDM/chatglm2-6b",
"region:us"
] | null | "2024-04-03T05:28:00Z" | ---
library_name: peft
base_model: THUDM/chatglm2-6b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 |
Joocheol/my_awesome_eli5_clm-model | Joocheol | "2023-05-31T03:48:32Z" | 165 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-05-31T01:13:22Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8634 | 1.0 | 1120 | 3.7541 |
| 3.776 | 2.0 | 2240 | 3.7417 |
| 3.7227 | 3.0 | 3360 | 3.7383 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
prxy5604/be753c57-7cee-405b-ba84-ee2d1e551821 | prxy5604 | "2025-03-09T23:00:57Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m-deduped",
"base_model:adapter:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"region:us"
] | null | "2025-03-09T21:56:54Z" | ---
library_name: peft
license: apache-2.0
base_model: EleutherAI/pythia-70m-deduped
tags:
- axolotl
- generated_from_trainer
model-index:
- name: be753c57-7cee-405b-ba84-ee2d1e551821
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: EleutherAI/pythia-70m-deduped
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- e962246c31556921_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e962246c31556921_train_data.json
type:
field_input: candidates
field_instruction: clean_html
field_output: action
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: false
group_by_length: false
hub_model_id: prxy5604/be753c57-7cee-405b-ba84-ee2d1e551821
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 6000
micro_batch_size: 4
mlflow_experiment_name: /tmp/e962246c31556921_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 87a1cb3a-f4f7-4cef-973e-023b459563e1
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 87a1cb3a-f4f7-4cef-973e-023b459563e1
warmup_steps: 100
weight_decay: 0
xformers_attention: null
```
</details><br>
# be753c57-7cee-405b-ba84-ee2d1e551821
This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 6000
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
LuciferXGod/ppo-LunarLander-v2 | LuciferXGod | "2023-12-01T19:32:46Z" | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-12-01T19:32:25Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.38 +/- 33.97
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
marklicata/KA_retrained_v3 | marklicata | "2024-11-22T22:37:15Z" | 113 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:marklicata/KA_retrained_v2",
"base_model:finetune:marklicata/KA_retrained_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-22T02:25:11Z" | ---
library_name: transformers
license: apache-2.0
base_model: marklicata/KA_retrained_v2
tags:
- generated_from_trainer
model-index:
- name: KA_retrained_v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KA_retrained_v3
This model is a fine-tuned version of [marklicata/KA_retrained_v2](https://huggingface.co/marklicata/KA_retrained_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 34 | 0.4693 |
| No log | 2.0 | 68 | 0.4394 |
| No log | 3.0 | 102 | 0.4524 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.3
|
TheBloke/Kimiko-13B-GPTQ | TheBloke | "2023-09-27T12:45:06Z" | 43 | 17 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"base_model:Chat-Error/Kimiko_13B",
"base_model:quantized:Chat-Error/Kimiko_13B",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | "2023-07-27T17:38:05Z" | ---
license: llama2
model_name: Kimiko 13B
base_model: nRuaif/Kimiko_13B
inference: false
model_creator: nRuaif
model_type: llama
prompt_template: '<<HUMAN>>
{prompt}
<<AIBOT>>
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Kimiko 13B - GPTQ
- Model creator: [nRuaif](https://huggingface.co/nRuaif)
- Original model: [Kimiko 13B](https://huggingface.co/nRuaif/Kimiko_13B)
<!-- description start -->
## Description
This repo contains GPTQ model files for [nRuaif's Kimiko 13B](https://huggingface.co/nRuaif/Kimiko_13B).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Kimiko-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Kimiko-13B-GGUF)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Kimiko-13B-fp16)
* [nRuaif's original LoRA adapter, which can be merged on to the base model.](https://huggingface.co/nRuaif/Kimiko_13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Kimiko
```
<<HUMAN>>
{prompt}
<<AIBOT>>
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.95 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Kimiko-13B-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/Kimiko-13B-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Kimiko-13B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Kimiko-13B-GPTQ:main`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Kimiko-13B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
### For CodeLlama models only: you must use Transformers 4.33.0 or later.
If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Kimiko-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<<HUMAN>>
{prompt}
<<AIBOT>>
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: nRuaif's Kimiko 13B
# Model Card for Kimiko_13B
<!-- Provide a quick summary of what the model is/does. -->
This is my new Kimiko models, trained with LLaMA2-13B for...purpose
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** nRuaif
- **Model type:** Decoder only
- **License:** CC BY-NC-SA
- **Finetuned from model [optional]:** LLaMA 2
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/OpenAccess-AI-Collective/axolotl
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is trained on 3k examples of instructions dataset, high quality roleplay, for best result follow this format
```
<<HUMAN>>
How to do abc
<<AIBOT>>
Here is how
Or with system prompting for roleplay
<<SYSTEM>>
A's Persona:
B's Persona:
Scenario:
Add some instruction here on how you want your RP to go.
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
All bias of this model come from LlaMA2 with an exception of NSFW bias.....
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
3000 examples from LIMAERP, LIMA and I sample 1000 good instruction from Airboro
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Model is trained with 1 L4 from GCP costing a whooping 2.5USD
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
3 epochs with 0.0002 lr, full 4096 ctx token, QLoRA
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
It takes 18 hours to train this model with xformers enable
[More Information Needed]
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** L4 with 12CPUs 48gb ram
- **Hours used:** 5
- **Cloud Provider:** GCP
- **Compute Region:** US
- **Carbon Emitted:** 0.5KG
|
kostiantynk/e28fa884-e33d-46e6-9ddb-e33ea8f1e903 | kostiantynk | "2025-01-18T03:56:29Z" | 14 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Korabbit/llama-2-ko-7b",
"base_model:adapter:Korabbit/llama-2-ko-7b",
"region:us"
] | null | "2025-01-18T03:05:46Z" | ---
library_name: peft
base_model: Korabbit/llama-2-ko-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e28fa884-e33d-46e6-9ddb-e33ea8f1e903
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Korabbit/llama-2-ko-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9a7478f7dc0c77a4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9a7478f7dc0c77a4_train_data.json
type:
field_input: dense_caption
field_instruction: video_id
field_output: short_caption
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: kostiantynk/e28fa884-e33d-46e6-9ddb-e33ea8f1e903
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/9a7478f7dc0c77a4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 503b2cb9-e990-4d34-b81a-a3d784ed7e35
wandb_project: Mine-SN56-22-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 503b2cb9-e990-4d34-b81a-a3d784ed7e35
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e28fa884-e33d-46e6-9ddb-e33ea8f1e903
This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1069
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.443 | 0.0000 | 1 | 1.7362 |
| 1.8378 | 0.0001 | 3 | 1.7307 |
| 1.7186 | 0.0001 | 6 | 1.6226 |
| 1.1027 | 0.0002 | 9 | 1.1069 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Maqqq/OpenHermes-2.5-Mistral-7B-10 | Maqqq | "2024-03-07T16:13:31Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-07T15:37:17Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF | mradermacher | "2024-12-25T02:09:50Z" | 16 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:HachiML/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1",
"base_model:quantized:HachiML/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1",
"endpoints_compatible",
"region:us"
] | null | "2024-12-25T01:14:56Z" | ---
base_model: HachiML/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/HachiML/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q2_K.gguf) | Q2_K | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q3_K_L.gguf) | Q3_K_L | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.IQ4_XS.gguf) | IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q4_K_S.gguf) | Q4_K_S | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q5_K_S.gguf) | Q5_K_S | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q5_K_M.gguf) | Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q6_K.gguf) | Q6_K | 6.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.Q8_0.gguf) | Q8_0 | 7.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.1.f16.gguf) | f16 | 14.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
globalyako/swallow-16b-bf16-4bit-magpie-bricks2 | globalyako | "2025-02-11T13:52:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:globalyako/swallow-8b-bf16-4bit",
"base_model:finetune:globalyako/swallow-8b-bf16-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-02-11T13:51:21Z" | ---
base_model: globalyako/swallow-8b-bf16-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** globalyako
- **License:** apache-2.0
- **Finetuned from model :** globalyako/swallow-8b-bf16-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
John6666/3010nc-xx-mixpony-v25-sdxl | John6666 | "2024-07-23T06:57:52Z" | 100 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-07-23T06:53:14Z" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- pony
---
Original model is [here](https://civitai.com/models/548205?modelVersionId=665891).
|
TheBlueObserver/Llama-3.2-3B-Instruct__recurv-r8-a8-epoch4 | TheBlueObserver | "2025-03-08T23:30:56Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2025-03-08T23:30:53Z" |
# TheBlueObserver/Llama-3.2-3B-Instruct__recurv-r8-a8-epoch4 Model Card
## LoRA Details
- **Rank**: 8
- **Alpha**: 8
## Training Details
- **Datasets**: recurv_clinical
- **Limit**: -1
- **Max Steps**: default
- **Epochs**: 4
|
PrunaAI/FLUX.1-schnell-4bit | PrunaAI | "2024-10-30T19:28:33Z" | 22 | 11 | null | [
"pruna-ai",
"base_model:ibm-granite/granite-8b-code-instruct-128k",
"base_model:finetune:ibm-granite/granite-8b-code-instruct-128k",
"region:us"
] | null | "2024-08-17T09:54:17Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ibm-granite/granite-8b-code-instruct-128k
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/Tun8YgzxZ9)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with Quanto to 8 bits.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model on cards with less than 12 GB of memory with these steps:
0. Check requirements from the original repo black-forest-labs/FLUX.1-schnell installed. In particular, check python, diffusers, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install -U optimum-quanto
```
2. Download the model
- Use Python:
```python
import subprocess
repo_name = "FLUX.1-schnell-4bit"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
2. Load & run the model.
```python
import torch
from optimum.quanto import freeze, qfloat8, quantize
from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
dtype = torch.bfloat16
bfl_repo = "black-forest-labs/FLUX.1-schnell"
revision = "refs/pr/1"
local_path = "FLUX.1-schnell-4bit"
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(bfl_repo, subfolder="scheduler", revision=revision)
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
text_encoder_2 = torch.load(local_path + '/text_encoder_2.pt')
tokenizer_2 = T5TokenizerFast.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype, revision=revision)
vae = AutoencoderKL.from_pretrained(bfl_repo, subfolder="vae", torch_dtype=dtype, revision=revision)
transformer = torch.load(local_path + '/transformer.pt')
pipe = FluxPipeline(
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=None,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=None,
)
pipe.text_encoder_2 = text_encoder_2
pipe.transformer = transformer
# pipe.enable_model_cpu_offload()
pipe.to('cuda')
print('done')
generator = torch.Generator().manual_seed(12345)
pipe(
"a cute apple smiling",
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model black-forest-labs/FLUX.1-schnell before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
GreenBitAI/Llama-2-7B-layer-mix-bpw-3.0 | GreenBitAI | "2024-04-29T21:49:23Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-01T09:31:09Z" | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
jainsakshi/peft_lora_wer_experiment | jainsakshi | "2025-04-15T10:36:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-15T10:36:51Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
jvelja/vllm-gemma2b-llmOversight-1.0-noDropSus_11 | jvelja | "2024-09-10T03:47:07Z" | 60 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | "2024-09-10T03:47:02Z" | ---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="jvelja//tmp/tmp70s384jy/jvelja/vllm-gemma2b-llmOversight-1.0-noDropSus_11")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("jvelja//tmp/tmp70s384jy/jvelja/vllm-gemma2b-llmOversight-1.0-noDropSus_11")
model = AutoModelForCausalLMWithValueHead.from_pretrained("jvelja//tmp/tmp70s384jy/jvelja/vllm-gemma2b-llmOversight-1.0-noDropSus_11")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
elshehawy/Reinforce-CartPole-1 | elshehawy | "2023-06-21T12:54:38Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-06-19T15:29:50Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
MarsupialAI/HelloNurse-11b_5.0bpw_exl2 | MarsupialAI | "2024-04-19T22:31:09Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] | text-generation | "2024-04-19T22:25:33Z" | Hello Nurse 11b quantized at 5.0bpw in EXL2 format. Original model - https://huggingface.co/MarsupialAI/HelloNurse-11b |
kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base | kaiserrr | "2023-08-23T10:10:46Z" | 1 | 2 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2023-08-23T10:10:32Z" | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base')
model = AutoModel.from_pretrained('kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 9525 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135 | albertvillanova | "2021-05-22T04:52:53Z" | 9 | 0 | transformers | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autonlp",
"bn",
"dataset:albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-02T23:29:05Z" | ---
tags: autonlp
language: bn
widget:
- text: "I love AutoNLP 🤗"
datasets:
- albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 1311135
## Validation Metrics
- Loss: 0.35616958141326904
- Accuracy: 0.8979447200566973
- Macro F1: 0.8545383956197669
- Micro F1: 0.8979447200566975
- Weighted F1: 0.8983951947775538
- Macro Precision: 0.8615833774439791
- Micro Precision: 0.8979447200566973
- Weighted Precision: 0.9013559365881655
- Macro Recall: 0.8516503001777104
- Micro Recall: 0.8979447200566973
- Weighted Recall: 0.8979447200566973
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
Aakali/whisper-medium-hi | Aakali | "2024-05-21T04:07:00Z" | 14 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-05-20T12:50:51Z" | ---
language:
- hi
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-medium
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper medium Hi - Aa
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args: 'config: hi, split: test'
metrics:
- type: wer
value: 24.227545923982053
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper medium Hi - Aa
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3556
- Wer: 24.2275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0564 | 2.4450 | 1000 | 0.2346 | 26.9153 |
| 0.0159 | 4.8900 | 2000 | 0.2877 | 26.1619 |
| 0.0026 | 7.3350 | 3000 | 0.3198 | 24.9471 |
| 0.0002 | 9.7800 | 4000 | 0.3457 | 24.8074 |
| 0.0001 | 12.2249 | 5000 | 0.3556 | 24.2275 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Aubi0ne/layoutlmv3-finetuned-cord_100 | Aubi0ne | "2022-10-17T14:26:35Z" | 83 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2022-10-17T12:37:24Z" | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord
type: cord
args: cord
metrics:
- name: Precision
type: precision
value: 0.9174649963154016
- name: Recall
type: recall
value: 0.9318862275449101
- name: F1
type: f1
value: 0.9246193835870776
- name: Accuracy
type: accuracy
value: 0.9405772495755518
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2834
- Precision: 0.9175
- Recall: 0.9319
- F1: 0.9246
- Accuracy: 0.9406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.17 | 250 | 1.0175 | 0.7358 | 0.7882 | 0.7611 | 0.8014 |
| 1.406 | 8.33 | 500 | 0.5646 | 0.8444 | 0.8735 | 0.8587 | 0.8671 |
| 1.406 | 12.5 | 750 | 0.3943 | 0.8950 | 0.9184 | 0.9065 | 0.9189 |
| 0.3467 | 16.67 | 1000 | 0.3379 | 0.9138 | 0.9289 | 0.9213 | 0.9291 |
| 0.3467 | 20.83 | 1250 | 0.2842 | 0.9189 | 0.9334 | 0.9261 | 0.9419 |
| 0.1484 | 25.0 | 1500 | 0.2822 | 0.9233 | 0.9371 | 0.9302 | 0.9427 |
| 0.1484 | 29.17 | 1750 | 0.2906 | 0.9168 | 0.9319 | 0.9243 | 0.9372 |
| 0.0825 | 33.33 | 2000 | 0.2922 | 0.9183 | 0.9334 | 0.9258 | 0.9410 |
| 0.0825 | 37.5 | 2250 | 0.2842 | 0.9154 | 0.9319 | 0.9236 | 0.9397 |
| 0.0596 | 41.67 | 2500 | 0.2834 | 0.9175 | 0.9319 | 0.9246 | 0.9406 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
research-dump/Llama-2-7b-chat-hf_mixed_sft_lexical_instruction_final_full_changepoint | research-dump | "2024-06-12T19:23:11Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-classification | "2024-06-12T11:28:17Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF | mradermacher | "2025-01-04T10:17:05Z" | 40 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:jaspionjader/Kosmos-EVAA-Franken-v38-8B",
"base_model:quantized:jaspionjader/Kosmos-EVAA-Franken-v38-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-01-04T10:06:35Z" | ---
base_model: jaspionjader/Kosmos-EVAA-Franken-v38-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/jaspionjader/Kosmos-EVAA-Franken-v38-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Kosmos-EVAA-Franken-v38-8B-GGUF/resolve/main/Kosmos-EVAA-Franken-v38-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
BigHuggyD/anthracite-org_magnum-v2-123b_exl2_4.0bpw_h6 | BigHuggyD | "2024-08-21T17:59:13Z" | 5 | 0 | null | [
"safetensors",
"mistral",
"chat",
"text-generation",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"license:other",
"4-bit",
"exl2",
"region:us"
] | text-generation | "2024-08-21T17:20:02Z" | ---
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
pipeline_tag: text-generation
tags:
- chat
---
This is the sixth in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407).
## Prompting
Model has been Instruct tuned with the Mistral formatting. A typical input would look like this:
```py
<s>[INST] SYSTEM MESSAGE\nUSER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]
```
We also provide SillyTavern presets for [Context](https://huggingface.co/anthracite-org/Magnum-123b-v1/resolve/main/Magnum-Mistral-Context.json) and [Instruct](https://huggingface.co/anthracite-org/Magnum-123b-v1/raw/main/Magnum-Mistral-Instruct.json) respectively.
The Mistral preset included in SillyTavern seems to be misconfigured by default, so we recommend using these as a replacement.
## Credits
- [anthracite-org/Stheno-Data-Filtered](https://huggingface.co/datasets/anthracite-org/Stheno-Data-Filtered)
- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
- [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed)
This model has been a team effort, and the credits goes to all members of Anthracite.
## Training
The training was done for 1.5 epochs. We used 8x [AMD Instinct™ MI300X Accelerators](https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html) for the full-parameter fine-tuning of the model.
In addition to this, we noticed that Mistral Large models seemed much more sensitive to learning rate adjustments than other models:
We hypothesize this is primarily due to the particularly narrow and low variance weight distributions typical of Mistral derived models regardless of their scale.
In the end, due to the costs that would be involved in training another full 2 epochs run ($600) on an even lower rate, we settled on our third attempt: 2e-6 with an effective batch size of 64. We chose to publish the 1.5 epoch run after manually testing and comparing it.
Also, we notice a correlation between the significance of the 2nd epoch loss drop and the strength of the learning rate, implying 4e-6 leads to more catastrophic forgetting.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## Safety
... |
hlillemark/sft_mc_filtered | hlillemark | "2025-03-08T23:54:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-08T23:22:21Z" | ---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft_mc_filtered
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sft_mc_filtered
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the identity and the data_mc_filtered datasets.
It achieves the following results on the evaluation set:
- Loss: 2.3079
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8433 | 1.0 | 50 | 1.0960 |
| 0.6237 | 2.0 | 100 | 1.1312 |
| 0.391 | 3.0 | 150 | 1.2466 |
| 0.2529 | 4.0 | 200 | 1.4003 |
| 0.148 | 5.0 | 250 | 1.4606 |
| 0.0818 | 6.0 | 300 | 1.5260 |
| 0.0352 | 7.0 | 350 | 2.0277 |
| 0.0197 | 8.0 | 400 | 2.1508 |
| 0.0129 | 9.0 | 450 | 2.2828 |
| 0.0066 | 10.0 | 500 | 2.3079 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
clowman/Llama-3.2-3B-Instruct-AWQ-Int4 | clowman | "2025-04-02T18:28:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | "2025-04-02T18:05:39Z" | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
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base_model:
- meta-llama/Llama-3.2-3B-Instruct
---
# Quantization
Created with [lambda-quant](https://github.com/LambdaLabsML/lambda-quant/tree/f97108fe4a9ee061a7b969b23a9605a6d561863d) on `Python 3.10.12 (main, Nov 6 2024, 20:22:13) [GCC 11.4.0]`
Base Model: [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
Quantized using [autoawq==0.2.8](https://github.com/casper-hansen/AutoAWQ)
Steps to create:
1. `git clone https://github.com/LambdaLabsML/lambda-quant`
2. `git checkout f97108fe4a9ee061a7b969b23a9605a6d561863d`
3. `python quantize.py -m meta-llama/Llama-3.2-3B-Instruct -q AWQ-Int4`
## Evaluation
TODO
## Benchmarks
TODO
# Base Model README.md
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-3B-Instruct, for use with `transformers` and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-3B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-3B-Instruct --include "original/*" --local-dir Llama-3.2-3B-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. |
facebook/maskformer-swin-tiny-coco | facebook | "2023-09-07T15:34:36Z" | 432 | 6 | transformers | [
"transformers",
"pytorch",
"safetensors",
"maskformer",
"vision",
"image-segmentation",
"dataset:coco",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | "2022-03-02T23:29:05Z" | ---
license: other
tags:
- vision
- image-segmentation
datasets:
- coco
widget:
- src: http://images.cocodataset.org/val2017/000000039769.jpg
example_title: Cats
- src: http://images.cocodataset.org/val2017/000000039770.jpg
example_title: Castle
---
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169).
Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation.
## Intended uses & limitations
You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests
# load MaskFormer fine-tuned on COCO panoptic segmentation
feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-coco")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-coco")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
# model predicts class_queries_logits of shape `(batch_size, num_queries)`
# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
# you can pass them to feature_extractor for postprocessing
result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
predicted_panoptic_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer). |
Yuyeong/Llama-3.2-1B-string_dataset_4nodev5-degree-0945bb | Yuyeong | "2025-03-21T15:20:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-21T15:11:56Z" | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-string_dataset_4nodev5-degree-0945bb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/helloyouyoung/GDV/runs/2odoqsr9)
# Llama-3.2-1B-string_dataset_4nodev5-degree-0945bb
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.49.0
- Pytorch 2.3.0
- Datasets 3.3.2
- Tokenizers 0.21.0
|
Neurora/opus-tatoeba-deu-eng | Neurora | "2025-02-26T15:30:08Z" | 0 | 0 | null | [
"safetensors",
"marian",
"translation",
"de",
"en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | translation | "2025-02-26T15:27:41Z" | ---
license: cc-by-nc-sa-4.0
language:
- de
- en
pipeline_tag: translation
---
# Opus Tatoeba | German -> English
* dataset: opus
* model: transformer-align
* source language(s): deu
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download: [opus-2021-02-22.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-eng/opus-2021-02-22.zip)
* test set translations: [opus-2021-02-22.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-eng/opus-2021-02-22.test.txt)
* test set scores: [opus-2021-02-22.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-eng/opus-2021-02-22.eval.txt)
## Benchmarks
| testset | BLEU | chr-F | #sent | #words | BP |
|---------|-------|-------|-------|--------|----|
| newssyscomb2009.deu-eng | 28.9 | 0.555 | 502 | 11821 | 0.997 |
| news-test2008.deu-eng | 28.1 | 0.551 | 2051 | 49380 | 1.000 |
| newstest2009.deu-eng | 27.1 | 0.546 | 2525 | 65402 | 0.985 |
| newstest2010.deu-eng | 30.7 | 0.588 | 2489 | 61724 | 1.000 |
| newstest2011.deu-eng | 27.6 | 0.557 | 3003 | 74681 | 1.000 |
| newstest2012.deu-eng | 29.3 | 0.571 | 3003 | 72812 | 1.000 |
| newstest2013.deu-eng | 32.2 | 0.584 | 3000 | 64505 | 1.000 |
| newstest2014-deen.deu-eng | 34.3 | 0.602 | 3003 | 67337 | 0.980 |
| newstest2015-ende.deu-eng | 34.5 | 0.603 | 2169 | 46443 | 0.980 |
| newstest2016-ende.deu-eng | 40.6 | 0.652 | 2999 | 64126 | 1.000 |
| newstest2017-ende.deu-eng | 36.2 | 0.614 | 3004 | 64399 | 0.987 |
| newstest2018-ende.deu-eng | 44.3 | 0.672 | 2998 | 67013 | 0.983 |
| newstest2019-deen.deu-eng | 41.0 | 0.649 | 2000 | 39282 | 1.000 |
| Tatoeba-test.deu-eng | 53.5 | 0.697 | 10000 | 81233 | 0.974 | |
mlx-community/DeepSeek-Coder-V2-Lite-Instruct-6bit | mlx-community | "2025-02-25T16:57:48Z" | 0 | 0 | mlx | [
"mlx",
"safetensors",
"deepseek_v2",
"custom_code",
"base_model:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"base_model:quantized:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"license:other",
"6-bit",
"region:us"
] | null | "2025-02-25T16:27:50Z" | ---
license: other
license_name: deepseek-license
license_link: LICENSE
tags:
- mlx
base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
---
# mlx-community/DeepSeek-Coder-V2-Lite-Instruct-6bit
The Model [mlx-community/DeepSeek-Coder-V2-Lite-Instruct-6bit](https://huggingface.co/mlx-community/DeepSeek-Coder-V2-Lite-Instruct-6bit) was converted to MLX format from [deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) using mlx-lm version **0.20.5**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/DeepSeek-Coder-V2-Lite-Instruct-6bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
sn56a3/2b8ecba2-549d-4141-ac66-0446a1dd5052 | sn56a3 | "2025-02-07T03:16:33Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-160m",
"base_model:adapter:JackFram/llama-160m",
"license:apache-2.0",
"region:us"
] | null | "2025-02-07T03:06:54Z" | ---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-160m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2b8ecba2-549d-4141-ac66-0446a1dd5052
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: JackFram/llama-160m
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 3532d07b974eef52_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3532d07b974eef52_train_data.json
type:
field_input: code
field_instruction: question
field_output: solution
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: '{'''':torch.cuda.current_device()}'
do_eval: true
early_stopping_patience: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 64
gradient_checkpointing: true
group_by_length: true
hub_model_id: sn56a3/2b8ecba2-549d-4141-ac66-0446a1dd5052
hub_repo: stevemonite
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 70GiB
max_steps: 1200
micro_batch_size: 1
mlflow_experiment_name: /tmp/3532d07b974eef52_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
save_strategy: steps
seed: 2399444084
sequence_len: 2048
shuffle: true
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: null
wandb_project: god
wandb_run: p2yv
wandb_runid: null
warmup_raio: 0.03
warmup_ratio: 0.03
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 2b8ecba2-549d-4141-ac66-0446a1dd5052
This model is a fine-tuned version of [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 2399444084
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 36
- training_steps: 1200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.2367 | 0.0016 | 1 | nan |
| 3.3297 | 0.0388 | 25 | nan |
| 2.4246 | 0.0777 | 50 | nan |
| 2.0829 | 0.1165 | 75 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
samuelabc/ppo-LunarLander-v2 | samuelabc | "2023-04-26T08:13:42Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-04-26T08:13:18Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 261.34 +/- 11.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
adamo1139/Yi-1.5-9B-32K-uninstruct1-0702 | adamo1139 | "2024-07-02T19:57:02Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-02T19:45:56Z" | ---
license: apache-2.0
---
|
huggingtweets/dril-nycguidovoice-senn_spud | huggingtweets | "2022-05-04T01:55:26Z" | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-05-04T01:44:12Z" | ---
language: en
thumbnail: http://www.huggingtweets.com/dril-nycguidovoice-senn_spud/1651629321136/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1510917391533830145/XW-zSFDJ_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503095773059244036/xof9dI-A_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1387151448203358209/HKNuKY7L_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Nick Mullen & Will Sennett</div>
<div style="text-align: center; font-size: 14px;">@dril-nycguidovoice-senn_spud</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & Nick Mullen & Will Sennett.
| Data | wint | Nick Mullen | Will Sennett |
| --- | --- | --- | --- |
| Tweets downloaded | 3229 | 1007 | 3231 |
| Retweets | 486 | 71 | 314 |
| Short tweets | 300 | 41 | 631 |
| Tweets kept | 2443 | 895 | 2286 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dcek2rh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dril-nycguidovoice-senn_spud's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2f1xmo4s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2f1xmo4s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dril-nycguidovoice-senn_spud')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Prisma-Multimodal/waterbirds-sweep-topk-64-patches_all_layers_1-hook_resid_post-64-82 | Prisma-Multimodal | "2025-01-18T06:37:21Z" | 5 | 0 | null | [
"region:us"
] | null | "2025-01-18T06:37:12Z" | # CLIP Sparse Autoencoder Checkpoint
This model is a sparse autoencoder trained on CLIP's internal representations.
## Model Details
### Architecture
- **Layer**: 1
- **Layer Type**: hook_resid_post
- **Model**: open-clip:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K
- **Dictionary Size**: 49152
- **Input Dimension**: 768
- **Expansion Factor**: 64
- **CLS Token Only**: False
### Training
- **Training Images**: 388952
- **Learning Rate**: 0.0205
- **L1 Coefficient**: 0.0002
- **Batch Size**: 4096
- **Context Size**: 49
## Performance Metrics
### Sparsity
- **L0 (Active Features)**: 64
- **Dead Features**: 0
- **Mean Log10 Feature Sparsity**: -8.7738
- **Features Below 1e-5**: 48363
- **Features Below 1e-6**: 44674
- **Mean Passes Since Fired**: 1972.2152
### Reconstruction
- **Explained Variance**: 0.8204
- **Explained Variance Std**: 0.0659
- **MSE Loss**: 0.0022
- **L1 Loss**: 0
- **Overall Loss**: 0.0022
## Training Details
- **Training Duration**: 1261 seconds
- **Final Learning Rate**: 0.0000
- **Warm Up Steps**: 500
- **Gradient Clipping**: 1
## Additional Information
- **Original Checkpoint Path**: /network/scratch/p/praneet.suresh/waterbird_checkpoints/28016c4c-tinyclip_sae_16_hyperparam_sweep_lr/n_images_389036.pt
- **Wandb Run**: https://wandb.ai/perceptual-alignment/waterbirds-sweep-topk-64-patches_all_layers/runs/q4snjwlw
- **Random Seed**: 42
|
sophie-rain-xxx/Sophie.Rain.Spiderman.Video.Tutorial | sophie-rain-xxx | "2025-02-28T21:30:28Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-02-28T21:29:53Z" | <p><a href="https://t.co/yLt2Ar1EVv">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a href="https://t.co/yLt2Ar1EVv">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
|
groovyda1717/ppo-LunarLander-v2 | groovyda1717 | "2023-08-19T10:32:30Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-08-19T10:32:04Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 226.30 +/- 26.86
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Watch-Sophie-Rain-Spider-Man-Leaks/Sophie.Rain.Spiderman.Video.Official.Link | Watch-Sophie-Rain-Spider-Man-Leaks | "2025-03-03T18:34:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-03T18:32:58Z" | <p><a href="https://link.rmg.co.uk/nude?Watch-Sophie" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a href="https://link.rmg.co.uk/nude?Watch-Sophie" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a href="https://link.rmg.co.uk/nude?Watch-Sophie" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p> |
Aryanne/Orca-Mini-3B-gguf | Aryanne | "2023-09-27T17:17:29Z" | 567 | 5 | null | [
"gguf",
"en",
"dataset:psmathur/alpaca_orca",
"dataset:psmathur/dolly-v2_orca",
"dataset:psmathur/WizardLM_Orca",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | "2023-09-27T09:10:42Z" | ---
license: cc-by-nc-sa-4.0
language:
- en
datasets:
- psmathur/alpaca_orca
- psmathur/dolly-v2_orca
- psmathur/WizardLM_Orca
---
Here are a few GGUF(v2) quantizations of the model [pankajmathur/orca_mini_3b](https://huggingface.co/pankajmathur/orca_mini_3b) |
Rexlygod/image2image_botox | Rexlygod | "2024-11-05T12:05:46Z" | 30 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"image-to-image",
"en",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:apache-2.0",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
] | image-to-image | "2024-11-05T11:37:52Z" | ---
license: apache-2.0
language:
- en
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
pipeline_tag: image-to-image
--- |
TheBloke/tulu-2-dpo-70B-GGUF | TheBloke | "2023-11-24T10:42:38Z" | 99 | 6 | transformers | [
"transformers",
"gguf",
"llama",
"en",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"dataset:allenai/tulu-v2-sft-mixture",
"arxiv:2305.18290",
"arxiv:2311.10702",
"base_model:allenai/tulu-2-dpo-70b",
"base_model:quantized:allenai/tulu-2-dpo-70b",
"license:other",
"region:us"
] | null | "2023-11-24T09:41:49Z" | ---
base_model: allenai/tulu-2-dpo-70b
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- allenai/tulu-v2-sft-mixture
inference: false
language:
- en
license: other
model-index:
- name: tulu-2-dpo-70b
results: []
model_creator: Allen Institute for AI
model_name: Tulu 2 DPO 70B
model_type: llama
prompt_template: '<|user|>
{prompt}
<|assistant|>
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Tulu 2 DPO 70B - GGUF
- Model creator: [Allen Institute for AI](https://huggingface.co/allenai)
- Original model: [Tulu 2 DPO 70B](https://huggingface.co/allenai/tulu-2-dpo-70b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Allen Institute for AI's Tulu 2 DPO 70B](https://huggingface.co/allenai/tulu-2-dpo-70b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/tulu-2-dpo-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF)
* [Allen Institute for AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/allenai/tulu-2-dpo-70b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Tulu
```
<|user|>
{prompt}
<|assistant|>
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [tulu-2-dpo-70b.Q2_K.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [tulu-2-dpo-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [tulu-2-dpo-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [tulu-2-dpo-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [tulu-2-dpo-70b.Q4_0.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [tulu-2-dpo-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [tulu-2-dpo-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [tulu-2-dpo-70b.Q5_0.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [tulu-2-dpo-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [tulu-2-dpo-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/blob/main/tulu-2-dpo-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| tulu-2-dpo-70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| tulu-2-dpo-70b.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `tulu-2-dpo-70b.Q6_K.gguf-split-a`
* `tulu-2-dpo-70b.Q6_K.gguf-split-b`
### q8_0
Please download:
* `tulu-2-dpo-70b.Q8_0.gguf-split-a`
* `tulu-2-dpo-70b.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat tulu-2-dpo-70b.Q6_K.gguf-split-* > tulu-2-dpo-70b.Q6_K.gguf && rm tulu-2-dpo-70b.Q6_K.gguf-split-*
cat tulu-2-dpo-70b.Q8_0.gguf-split-* > tulu-2-dpo-70b.Q8_0.gguf && rm tulu-2-dpo-70b.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B tulu-2-dpo-70b.Q6_K.gguf-split-a + tulu-2-dpo-70b.Q6_K.gguf-split-b tulu-2-dpo-70b.Q6_K.gguf
del tulu-2-dpo-70b.Q6_K.gguf-split-a tulu-2-dpo-70b.Q6_K.gguf-split-b
COPY /B tulu-2-dpo-70b.Q8_0.gguf-split-a + tulu-2-dpo-70b.Q8_0.gguf-split-b tulu-2-dpo-70b.Q8_0.gguf
del tulu-2-dpo-70b.Q8_0.gguf-split-a tulu-2-dpo-70b.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/tulu-2-dpo-70B-GGUF and below it, a specific filename to download, such as: tulu-2-dpo-70b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/tulu-2-dpo-70B-GGUF tulu-2-dpo-70b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/tulu-2-dpo-70B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/tulu-2-dpo-70B-GGUF tulu-2-dpo-70b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m tulu-2-dpo-70b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|user|>\n{prompt}\n<|assistant|>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/tulu-2-dpo-70B-GGUF", model_file="tulu-2-dpo-70b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Allen Institute for AI's Tulu 2 DPO 70B
<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png" alt="TuluV2 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for Tulu V2 DPO 70B
Tulu is a series of language models that are trained to act as helpful assistants.
Tulu V2 DPO 70B is a fine-tuned version of Llama 2 that was trained on on a mix of publicly available, synthetic and human datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290).
This model is a strong alternative to Llama 2 70b Chat.
For more details, read the paper: [Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
](https://arxiv.org/abs/2311.10702).
## Model description
- **Model type:** The flagship model of a suite of instruction and RLHF tuned chat models on a mix of publicly available, synthetic and human-created datasets.
- **Language(s) (NLP):** Primarily English
- **License:** [AI2 ImpACT](https://allenai.org/impact-license) Low-risk license.
- **Finetuned from model:** [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)
### Model Sources
- **Repository:** https://github.com/allenai/open-instruct
- **DPO Recipe:** The DPO recipe is from the [Zephyr Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) model
- **Model Family:** Other models and the dataset are found in the [Tulu V2 collection](https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101).
## Performance
| Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
|-------------|-----|----|---------------|--------------|
| **Tulu-v2-7b** 🐪 | **7B** | **SFT** | **6.30** | **73.9** |
| **Tulu-v2-dpo-7b** 🐪 | **7B** | **DPO** | **6.29** | **85.1** |
| **Tulu-v2-13b** 🐪 | **13B** | **SFT** | **6.70** | **78.9** |
| **Tulu-v2-dpo-13b** 🐪 | **13B** | **DPO** | **7.00** | **89.5** |
| **Tulu-v2-70b** 🐪 | **70B** | **SFT** | **7.49** | **86.6** |
| **Tulu-v2-dpo-70b** 🐪 | **70B** | **DPO** | **7.89** | **95.1** |
## Input Format
The model is trained to use the following format (note the newlines):
```
<|user|>
Your message here!
<|assistant|>
```
For best results, format all inputs in this manner. **Make sure to include a newline after `<|assistant|>`, this can affect generation quality quite a bit.**
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [Tulu V2 mix dataset](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.
We then further aligned the model with a [Jax DPO trainer](https://github.com/hamishivi/EasyLM/blob/main/EasyLM/models/llama/llama_train_dpo.py) built on [EasyLM](https://github.com/young-geng/EasyLM) on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4.
<!-- You can find the datasets used for training Tulu V2 [here]()
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/tulu-2-dpo-70b", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```-->
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The Tulu models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Llama 2 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
### Training hyperparameters
The following hyperparameters were used during DPO training:
- learning_rate: 5e-07
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
## Citation
If you find Tulu 2 is useful in your work, please cite it with:
```
@misc{ivison2023camels,
title={Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2},
author={Hamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
year={2023},
eprint={2311.10702},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
*Model card adapted from [Zephyr Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/blob/main/README.md)*
<!-- original-model-card end -->
|
pintu5057/resnet18-finetuned-lora-food101 | pintu5057 | "2024-04-25T11:16:19Z" | 2 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:microsoft/resnet-18",
"base_model:adapter:microsoft/resnet-18",
"region:us"
] | null | "2024-04-25T11:16:10Z" | ---
library_name: peft
base_model: microsoft/resnet-18
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 |
RichardErkhov/alfox_-_train_test_bloom560-gguf | RichardErkhov | "2025-02-13T06:38:31Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-02-13T05:54:01Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
train_test_bloom560 - GGUF
- Model creator: https://huggingface.co/alfox/
- Original model: https://huggingface.co/alfox/train_test_bloom560/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [train_test_bloom560.Q2_K.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q2_K.gguf) | Q2_K | 0.39GB |
| [train_test_bloom560.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.IQ3_XS.gguf) | IQ3_XS | 0.43GB |
| [train_test_bloom560.IQ3_S.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.IQ3_S.gguf) | IQ3_S | 0.43GB |
| [train_test_bloom560.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q3_K_S.gguf) | Q3_K_S | 0.43GB |
| [train_test_bloom560.IQ3_M.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.IQ3_M.gguf) | IQ3_M | 0.45GB |
| [train_test_bloom560.Q3_K.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q3_K.gguf) | Q3_K | 0.46GB |
| [train_test_bloom560.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q3_K_M.gguf) | Q3_K_M | 0.46GB |
| [train_test_bloom560.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q3_K_L.gguf) | Q3_K_L | 0.47GB |
| [train_test_bloom560.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.IQ4_XS.gguf) | IQ4_XS | 0.49GB |
| [train_test_bloom560.Q4_0.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q4_0.gguf) | Q4_0 | 0.5GB |
| [train_test_bloom560.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.IQ4_NL.gguf) | IQ4_NL | 0.5GB |
| [train_test_bloom560.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q4_K_S.gguf) | Q4_K_S | 0.5GB |
| [train_test_bloom560.Q4_K.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q4_K.gguf) | Q4_K | 0.52GB |
| [train_test_bloom560.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q4_K_M.gguf) | Q4_K_M | 0.52GB |
| [train_test_bloom560.Q4_1.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q4_1.gguf) | Q4_1 | 0.53GB |
| [train_test_bloom560.Q5_0.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q5_0.gguf) | Q5_0 | 0.57GB |
| [train_test_bloom560.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q5_K_S.gguf) | Q5_K_S | 0.57GB |
| [train_test_bloom560.Q5_K.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q5_K.gguf) | Q5_K | 0.58GB |
| [train_test_bloom560.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q5_K_M.gguf) | Q5_K_M | 0.58GB |
| [train_test_bloom560.Q5_1.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q5_1.gguf) | Q5_1 | 0.6GB |
| [train_test_bloom560.Q6_K.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q6_K.gguf) | Q6_K | 0.64GB |
| [train_test_bloom560.Q8_0.gguf](https://huggingface.co/RichardErkhov/alfox_-_train_test_bloom560-gguf/blob/main/train_test_bloom560.Q8_0.gguf) | Q8_0 | 0.82GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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|
Scav6411/speecht5_tts_voxpopuli_nl | Scav6411 | "2024-09-28T17:09:24Z" | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:DTU54DL/common-accent",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | "2024-09-28T15:43:02Z" | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- DTU54DL/common-accent
model-index:
- name: speecht5_tts_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_tts_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the DTU54DL/common-accent dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4884
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5484 | 5.6180 | 1000 | 0.5067 |
| 0.5304 | 11.2360 | 2000 | 0.4926 |
| 0.5346 | 16.8539 | 3000 | 0.4876 |
| 0.5235 | 22.4719 | 4000 | 0.4884 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
Andyrasika/my_awesome_wnut_model | Andyrasika | "2023-08-22T06:27:46Z" | 108 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-08-22T05:42:24Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: train
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5673758865248227
- name: Recall
type: recall
value: 0.2965708989805375
- name: F1
type: f1
value: 0.3895313451004261
- name: Accuracy
type: accuracy
value: 0.9410884528237357
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2747
- Precision: 0.5674
- Recall: 0.2966
- F1: 0.3895
- Accuracy: 0.9411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2827 | 0.5605 | 0.2317 | 0.3279 | 0.9380 |
| No log | 2.0 | 426 | 0.2747 | 0.5674 | 0.2966 | 0.3895 | 0.9411 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
Resource:
- https://docs.pinecone.io/docs/ner-search
- https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-tokenclassification-skweak-weaksupervision.html
- https://python.plainenglish.io/optimize-your-spacy-ner-results-with-this-simple-change-e59937c411ab |
Yuripoke10/bert-fine-tuned-cola | Yuripoke10 | "2023-10-02T18:59:10Z" | 62 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-10-02T14:06:44Z" | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: bert-fine-tuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2808
- Validation Loss: 0.4147
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4893 | 0.4094 | 0 |
| 0.2808 | 0.4147 | 1 |
### Framework versions
- Transformers 4.33.3
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
KrusHan/PPO-LunarLander-v2 | KrusHan | "2022-05-15T13:18:42Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2022-05-15T13:18:09Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 260.52 +/- 27.65
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
hotsuyuki/gpt_0.125B_global_step35000 | hotsuyuki | "2024-02-22T00:26:29Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-02-22T00:26:03Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dev-gonu/multi-intent-detection-model | dev-gonu | "2024-12-18T11:42:44Z" | 109 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-18T11:42:18Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
kholiavko/reception-19-11-responses-6-epoch | kholiavko | "2024-11-22T00:09:57Z" | 7 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-11-22T00:04:59Z" | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kholiavko
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bettinadbarros/Llama-2-13b-chat-norwegian-Q4_K_M-GGUF | bettinadbarros | "2024-09-24T06:16:49Z" | 7 | 0 | null | [
"gguf",
"pytorch",
"llama",
"llama-2",
"norwegian",
"norsk",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"no",
"dataset:NbAiLab/norwegian-alpaca",
"dataset:RuterNorway/OpenOrcaNo-15k",
"base_model:RuterNorway/Llama-2-13b-chat-norwegian",
"base_model:quantized:RuterNorway/Llama-2-13b-chat-norwegian",
"license:llama2",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-24T06:16:16Z" | ---
base_model: RuterNorway/Llama-2-13b-chat-norwegian
datasets:
- NbAiLab/norwegian-alpaca
- RuterNorway/OpenOrcaNo-15k
language:
- en
- 'no'
license: llama2
pipeline_tag: text-generation
tags:
- pytorch
- llama
- llama-2
- norwegian
- norsk
- llama-cpp
- gguf-my-repo
---
# bettinadbarros/Llama-2-13b-chat-norwegian-Q4_K_M-GGUF
This model was converted to GGUF format from [`RuterNorway/Llama-2-13b-chat-norwegian`](https://huggingface.co/RuterNorway/Llama-2-13b-chat-norwegian) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/RuterNorway/Llama-2-13b-chat-norwegian) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo bettinadbarros/Llama-2-13b-chat-norwegian-Q4_K_M-GGUF --hf-file llama-2-13b-chat-norwegian-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo bettinadbarros/Llama-2-13b-chat-norwegian-Q4_K_M-GGUF --hf-file llama-2-13b-chat-norwegian-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo bettinadbarros/Llama-2-13b-chat-norwegian-Q4_K_M-GGUF --hf-file llama-2-13b-chat-norwegian-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo bettinadbarros/Llama-2-13b-chat-norwegian-Q4_K_M-GGUF --hf-file llama-2-13b-chat-norwegian-q4_k_m.gguf -c 2048
```
|
tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF | tensorblock | "2025-03-06T17:53:29Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"chocolatine",
"TensorBlock",
"GGUF",
"fr",
"en",
"dataset:jpacifico/french-orca-dpo-pairs-revised",
"base_model:jpacifico/Chocolatine-2-14B-Instruct-v2.0b3",
"base_model:quantized:jpacifico/Chocolatine-2-14B-Instruct-v2.0b3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-06T16:10:59Z" | ---
library_name: transformers
tags:
- chocolatine
- TensorBlock
- GGUF
license: apache-2.0
datasets:
- jpacifico/french-orca-dpo-pairs-revised
language:
- fr
- en
base_model: jpacifico/Chocolatine-2-14B-Instruct-v2.0b3
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## jpacifico/Chocolatine-2-14B-Instruct-v2.0b3 - GGUF
This repo contains GGUF format model files for [jpacifico/Chocolatine-2-14B-Instruct-v2.0b3](https://huggingface.co/jpacifico/Chocolatine-2-14B-Instruct-v2.0b3).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).
<div style="text-align: left; margin: 20px 0;">
<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Run them on the TensorBlock client using your local machine ↗
</a>
</div>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q2_K.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q2_K.gguf) | Q2_K | 5.768 GB | smallest, significant quality loss - not recommended for most purposes |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q3_K_S.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q3_K_S.gguf) | Q3_K_S | 6.657 GB | very small, high quality loss |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q3_K_M.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q3_K_M.gguf) | Q3_K_M | 7.337 GB | very small, high quality loss |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q3_K_L.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q3_K_L.gguf) | Q3_K_L | 7.922 GB | small, substantial quality loss |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q4_0.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q4_0.gguf) | Q4_0 | 8.515 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q4_K_S.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q4_K_S.gguf) | Q4_K_S | 8.571 GB | small, greater quality loss |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q4_K_M.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q4_K_M.gguf) | Q4_K_M | 8.985 GB | medium, balanced quality - recommended |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q5_0.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q5_0.gguf) | Q5_0 | 10.263 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q5_K_S.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q5_K_S.gguf) | Q5_K_S | 10.263 GB | large, low quality loss - recommended |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q5_K_M.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q5_K_M.gguf) | Q5_K_M | 10.506 GB | large, very low quality loss - recommended |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q6_K.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q6_K.gguf) | Q6_K | 12.121 GB | very large, extremely low quality loss |
| [Chocolatine-2-14B-Instruct-v2.0b3-Q8_0.gguf](https://huggingface.co/tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF/blob/main/Chocolatine-2-14B-Instruct-v2.0b3-Q8_0.gguf) | Q8_0 | 15.697 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF --include "Chocolatine-2-14B-Instruct-v2.0b3-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/Chocolatine-2-14B-Instruct-v2.0b3-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
mmoukouba/MedPix-QnA | mmoukouba | "2025-03-30T14:08:47Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-30T14:08:47Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
ce-dric/Reinforce-cartpole-01 | ce-dric | "2023-07-07T07:49:58Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-07-07T07:49:50Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole-01
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
PrunaAI/shenzhi-wang-Llama3-8B-Chinese-Chat-bnb-8bit-smashed | PrunaAI | "2025-03-01T03:04:30Z" | 4 | 0 | null | [
"safetensors",
"llama",
"pruna-ai",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-02-24T14:34:33Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ORIGINAL_REPO_NAME
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/shenzhi-wang-Llama3-8B-Chinese-Chat-bnb-8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
LoneStriker/Qwen2.5-32B-Instruct-6.0bpw-h6-exl2 | LoneStriker | "2024-09-19T07:22:24Z" | 10 | 1 | null | [
"safetensors",
"qwen2",
"chat",
"text-generation",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-32B",
"base_model:quantized:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"6-bit",
"exl2",
"region:us"
] | text-generation | "2024-09-19T07:12:01Z" | ---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-32B
tags:
- chat
---
# Qwen2.5-32B-Instruct
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
**This repo contains the instruction-tuned 32B Qwen2.5 model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 32.5B
- Number of Paramaters (Non-Embedding): 31.0B
- Number of Layers: 64
- Number of Attention Heads (GQA): 40 for Q and 8 for KV
- Context Length: Full 131,072 tokens and generation 8192 tokens
- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-32B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to `config.json` to enable YaRN:
```json
{
...,
"rope_scaling": {
"factor": 4.0,
"original_max_position_embeddings": 32768,
"type": "yarn"
}
}
```
For deployment, we recommend using vLLM.
Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
We advise adding the `rope_scaling` configuration only when processing long contexts is required.
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
``` |
tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3_5e-5_lora2 | tyzhu | "2024-06-08T14:09:16Z" | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"dataset:tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"license:llama2",
"model-index",
"region:us"
] | null | "2024-06-07T18:21:00Z" | ---
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_doc_qa_v3_5e-5_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
type: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
- name: Accuracy
type: accuracy
value: 0.19302564102564101
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmind_nq_train6000_eval6489_v1_doc_qa_v3_5e-5_lora2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7918
- Accuracy: 0.1930
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.3891 | 1.0 | 529 | 0.6138 | 1.3015 |
| 1.3633 | 2.0 | 1058 | 0.6166 | 1.2855 |
| 1.2929 | 3.0 | 1587 | 0.6177 | 1.2954 |
| 1.2361 | 4.0 | 2116 | 0.6045 | 1.3489 |
| 1.1856 | 5.0 | 2645 | 0.6125 | 1.3968 |
| 1.1098 | 6.0 | 3174 | 0.6115 | 1.4721 |
| 1.0753 | 7.0 | 3703 | 0.6076 | 1.5798 |
| 1.0048 | 8.0 | 4232 | 0.6084 | 1.6042 |
| 0.9456 | 9.0 | 4761 | 0.5977 | 1.6843 |
| 0.8766 | 10.0 | 5290 | 0.6051 | 1.7829 |
| 0.8273 | 11.0 | 5819 | 0.6043 | 1.8060 |
| 0.7755 | 12.0 | 6348 | 0.6019 | 1.8729 |
| 0.715 | 13.0 | 6877 | 0.6017 | 1.9620 |
| 0.6804 | 14.0 | 7406 | 0.6009 | 2.0030 |
| 0.6277 | 15.0 | 7935 | 0.5998 | 2.0528 |
| 0.5733 | 16.0 | 8464 | 0.6012 | 2.0475 |
| 0.5409 | 17.0 | 8993 | 0.5749 | 2.0920 |
| 0.5024 | 18.0 | 9522 | 0.5986 | 2.1207 |
| 0.4699 | 19.0 | 10051 | 0.5993 | 2.1108 |
| 0.4367 | 20.0 | 10580 | 0.6005 | 2.1089 |
| 0.857 | 21.0 | 11109 | 0.5983 | 2.0215 |
| 3.7434 | 22.0 | 11638 | 0.2233 | 10.1186 |
| 7.7259 | 23.0 | 12167 | 0.1986 | 7.5379 |
| 4.2204 | 24.0 | 12696 | 0.5345 | 2.1568 |
| 0.7385 | 25.0 | 13225 | 0.5963 | 1.8229 |
| 1.1473 | 26.0 | 13754 | 0.5788 | 1.7570 |
| 2.0182 | 27.0 | 14283 | 0.5573 | 1.7293 |
| 2.2707 | 28.0 | 14812 | 0.4956 | 2.7017 |
| 4.1792 | 29.0 | 15341 | 0.3070 | 5.8288 |
| 7.7703 | 30.0 | 15870 | 0.1922 | 7.6619 |
| 7.7034 | 31.0 | 16399 | 0.1913 | 7.7003 |
| 7.9533 | 32.0 | 16928 | 0.1899 | 7.8667 |
| 7.8634 | 33.0 | 17457 | 0.1897 | 7.8134 |
| 7.8584 | 34.0 | 17986 | 0.1882 | 7.6760 |
| 7.824 | 35.0 | 18515 | 0.1888 | 7.7083 |
| 7.7446 | 36.0 | 19044 | 0.1888 | 7.6626 |
| 7.6708 | 37.0 | 19573 | 0.1886 | 7.5529 |
| 7.6733 | 38.0 | 20102 | 0.1903 | 7.5704 |
| 7.6271 | 39.0 | 20631 | 0.1949 | 7.5363 |
| 7.5886 | 40.0 | 21160 | 0.2130 | 7.4684 |
| 7.5514 | 41.0 | 21689 | 0.2077 | 7.4223 |
| 7.5205 | 42.0 | 22218 | 0.1946 | 7.3508 |
| 7.4577 | 43.0 | 22747 | 0.1951 | 7.1785 |
| 7.5021 | 44.0 | 23276 | 0.2092 | 6.6226 |
| 7.1133 | 45.0 | 23805 | 0.1994 | 6.4100 |
| 6.9682 | 46.0 | 24334 | 0.2250 | 6.3553 |
| 6.8891 | 47.0 | 24863 | 0.2224 | 6.3128 |
| 6.8621 | 48.0 | 25392 | 0.23 | 6.2465 |
| 6.8176 | 49.0 | 25921 | 0.2561 | 6.1966 |
| 6.9473 | 50.0 | 26450 | 0.1930 | 6.7918 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
adityarra07/whisper-med-LoRA_r64_a64 | adityarra07 | "2024-06-06T09:19:50Z" | 0 | 0 | null | [
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"region:us"
] | null | "2024-06-05T00:34:49Z" | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-med-LoRA_r64_a64
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-med-LoRA_r64_a64
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3052
- Wer: 13.4913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.8163 | 1.0 | 2801 | 0.4584 | 18.6509 |
| 0.3618 | 2.0 | 5602 | 0.3682 | 15.8800 |
| 0.305 | 3.0 | 8403 | 0.3289 | 14.2748 |
| 0.2769 | 4.0 | 11204 | 0.3099 | 13.6633 |
| 0.2626 | 5.0 | 14005 | 0.3052 | 13.4913 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Kira-wang/qwen-2.5-3b-r1-math-7k-v3-base-tr-0p2 | Kira-wang | "2025-02-20T00:20:31Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B",
"base_model:finetune:Qwen/Qwen2.5-3B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-19T16:16:41Z" | ---
base_model: Qwen/Qwen2.5-3B
library_name: transformers
model_name: qwen-2.5-3b-r1-math-7k-v3-base-tr-0p2
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for qwen-2.5-3b-r1-math-7k-v3-base-tr-0p2
This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Kira-wang/qwen-2.5-3b-r1-math-7k-v3-base-tr-0p2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bachbouch/mistral-instruct-7b-news | bachbouch | "2024-01-29T22:12:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | "2024-01-29T21:10:53Z" | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 |
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