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anonymous6435/deepseek-prover-isar | anonymous6435 | 2025-05-30T11:52:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T11:18:20Z | ---
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 ๐ค 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] |
alexmkv01/news-junkie-modernBERT-Large-v2 | alexmkv01 | 2025-05-30T11:18:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T11:17:29Z | ---
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 ๐ค 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] |
kaengreg/Qwen2.5-2B-layerwise-distilled | kaengreg | 2025-05-30T10:55:38Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2025-05-30T10:49:10Z | Model distilled from [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) using a [Iterative Layer-wise Distillation](https://github.com/kaengreg/layer-wise_distillation) approach.
Techincal Report [Comming Soon]
|
TheStageAI/Elastic-FLUX.1-schnell | TheStageAI | 2025-05-30T10:54:12Z | 28 | 3 | null | [
"base_model:black-forest-labs/FLUX.1-schnell",
"base_model:finetune:black-forest-labs/FLUX.1-schnell",
"license:apache-2.0",
"region:us"
] | null | 2025-04-08T18:07:13Z | ---
license: apache-2.0
base_model:
- black-forest-labs/FLUX.1-schnell
---
# Elastic model: Fastest self-serving models. FLUX.1-schnell.
Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models:
* __XL__: Mathematically equivalent neural network, optimized with our DNN compiler.
* __L__: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks.
* __M__: Faster model, with accuracy degradation less than 1.5%.
* __S__: The fastest model, with accuracy degradation less than 2%.
__Goals of Elastic Models:__
* Provide the fastest models and service for self-hosting.
* Provide flexibility in cost vs quality selection for inference.
* Provide clear quality and latency benchmarks.
* Provide interface of HF libraries: transformers and diffusers with a single line of code.
* Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT.
> It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well.
-----
## Inference
Currently, our demo model only supports 1024x1024 outputs without batching. This will be updated in the near future.
To infer our models, you just need to replace `diffusers` import with `elastic_models.diffusers`:
```python
import torch
from elastic_models.diffusers import FluxPipeline
mode_name = 'black-forest-labs/FLUX.1-schnell'
hf_token = ''
device = torch.device("cuda")
pipeline = FluxPipeline.from_pretrained(
mode_name,
torch_dtype=torch.bfloat16,
token=hf_token,
mode='S'
)
pipeline.to(device)
prompts = ["Kitten eating a banana"]
output = pipeline(prompt=prompts)
for prompt, output_image in zip(prompts, output.images):
output_image.save((prompt.replace(' ', '_') + '.png'))
```
### Installation
__System requirements:__
* GPUs: H100, L40s
* CPU: AMD, Intel
* Python: 3.10-3.12
To work with our models just run these lines in your terminal:
```shell
pip install thestage
pip install elastic_models[nvidia]\
--index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\
--extra-index-url https://pypi.nvidia.com\
--extra-index-url https://pypi.org/simple
# or for blackwell support
pip install elastic_models[blackwell]\
--index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\
--extra-index-url https://pypi.nvidia.com\
--extra-index-url https://pypi.org/simple
pip install flash_attn==2.7.3 --no-build-isolation
pip uninstall apex
```
Then go to [app.thestage.ai](https://app.thestage.ai), login and generate API token from your profile page. Set up API token as follows:
```shell
thestage config set --api-token <YOUR_API_TOKEN>
```
Congrats, now you can use accelerated models!
----
## Benchmarks
Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms.
### Quality benchmarks
For quality evaluation we have used: PSNR, SSIM and CLIP score. PSNR and SSIM were computed using outputs of original model.
| Metric/Model | S | M | L | XL | Original |
|---------------|---|---|---|----|----------|
| PSNR | 29.9 | 30.2 | 31 | inf | inf |
| SSIM | 0.66 | 0.71 | 0.86 | 1.0 | 1.0 |
| CLIP | 11.5 | 11.6 | 11.8 | 11.9 | 11.9|
### Latency benchmarks
Time in seconds to generate one image 1024x1024
| GPU/Model | S | M | L | XL | Original |
|-----------|-----|---|---|----|----------|
| H100 | 0.5 | 0.57 | 0.65 | 0.7 | 1.04 |
| L40s | 1.4 | 1.6 | 1.9 | 2.1 | 2.5|
| B200 | 0.3 | 0.4 | 0.42 | 0.43 | 0.74|
| GeForce RTX 5090 | 0.94 | - | - | - | -|
## Links
* __Platform__: [app.thestage.ai](https://app.thestage.ai)
<!-- * __Elastic models Github__: [app.thestage.ai](app.thestage.ai) -->
* __Subscribe for updates__: [TheStageAI X](https://x.com/TheStageAI)
* __Contact email__: [email protected]
|
thanghoang1307/victor | thanghoang1307 | 2025-05-30T10:50:12Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-30T10:34:00Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: VICTOR
---
# Victor
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `VICTOR` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "VICTOR",
"lora_weights": "https://huggingface.co/thanghoang1307/victor/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('thanghoang1307/victor', weight_name='lora.safetensors')
image = pipeline('VICTOR').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/thanghoang1307/victor/discussions) to add images that show off what youโve made with this LoRA.
|
Satram/Llama_Instruct_Sinteticos | Satram | 2025-05-30T10:44:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T10:44:18Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Satram
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
zadazada/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee | zadazada | 2025-05-30T10:42:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slow dense bee",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T12:03:41Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slow dense bee
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
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="zadazada/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_dense_bee", 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: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ali9999/mental_health_gpt2 | ali9999 | 2025-05-30T10:39:05Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T09:43:26Z | ---
library_name: transformers
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: mental_health_gpt2
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. -->
# mental_health_gpt2
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: 1.5340
## 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: 8
- seed: 42
- 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: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8297 | 1.0 | 1810 | 1.7047 |
| 1.6464 | 2.0 | 3620 | 1.6081 |
| 1.6108 | 3.0 | 5430 | 1.5645 |
| 1.5652 | 4.0 | 7240 | 1.5420 |
| 1.4691 | 5.0 | 9050 | 1.5340 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
RichardErkhov/Adansonia_-_internal_audit_new-gguf | RichardErkhov | 2025-05-30T10:39:03Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-30T08:23:09Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
internal_audit_new - GGUF
- Model creator: https://huggingface.co/Adansonia/
- Original model: https://huggingface.co/Adansonia/internal_audit_new/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [internal_audit_new.Q2_K.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q2_K.gguf) | Q2_K | 2.96GB |
| [internal_audit_new.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [internal_audit_new.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [internal_audit_new.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [internal_audit_new.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [internal_audit_new.Q3_K.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q3_K.gguf) | Q3_K | 3.74GB |
| [internal_audit_new.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [internal_audit_new.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [internal_audit_new.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [internal_audit_new.Q4_0.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q4_0.gguf) | Q4_0 | 4.34GB |
| [internal_audit_new.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [internal_audit_new.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [internal_audit_new.Q4_K.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q4_K.gguf) | Q4_K | 4.58GB |
| [internal_audit_new.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [internal_audit_new.Q4_1.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q4_1.gguf) | Q4_1 | 4.78GB |
| [internal_audit_new.Q5_0.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q5_0.gguf) | Q5_0 | 5.21GB |
| [internal_audit_new.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [internal_audit_new.Q5_K.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q5_K.gguf) | Q5_K | 5.34GB |
| [internal_audit_new.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [internal_audit_new.Q5_1.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q5_1.gguf) | Q5_1 | 5.65GB |
| [internal_audit_new.Q6_K.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q6_K.gguf) | Q6_K | 6.14GB |
| [internal_audit_new.Q8_0.gguf](https://huggingface.co/RichardErkhov/Adansonia_-_internal_audit_new-gguf/blob/main/internal_audit_new.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
base_model: Saxo/Linkbricks-Horizon-AI-Korean-Advanced-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Adansonia
- **License:** apache-2.0
- **Finetuned from model :** Saxo/Linkbricks-Horizon-AI-Korean-Advanced-8B
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)
|
huyhuung/Qwen_FFT_v4 | huyhuung | 2025-05-30T10:26:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T10:25:14Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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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
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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]
- **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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
anonymous6435/deepseek-prover-minilang | anonymous6435 | 2025-05-30T10:17:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T09:28:32Z | ---
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 ๐ค 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
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[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. -->
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[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] |
Varinder2110/sonunigam-2 | Varinder2110 | 2025-05-30T10:16:29Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-30T09:04:07Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Sonunigam 2
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Varinder2110/sonunigam-2/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Varinder2110/sonunigam-2', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 6000
- Learning rate: 0.0004
- LoRA rank: 64
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Varinder2110/sonunigam-2/discussions) to add images that show off what youโve made with this LoRA.
|
tungduong261204/DPO_3000_v2 | tungduong261204 | 2025-05-30T10:16:03Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Llama-3.2-1B",
"base_model:adapter:unsloth/Llama-3.2-1B",
"region:us"
] | null | 2025-05-30T10:15:57Z | ---
base_model: unsloth/Llama-3.2-1B
library_name: peft
---
# 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. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[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 0.15.2 |
anonymous6435/llemma-isar-SH | anonymous6435 | 2025-05-30T10:14:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T09:24:00Z | ---
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 ๐ค 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]
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## Uses
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### Direct Use
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[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. -->
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## How to Get Started with the Model
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
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rziga/llmdet_base | rziga | 2025-05-30T10:13:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mm-grounding-dino",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T10:10:59Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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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. -->
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[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[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
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[More Information Needed]
## Training Details
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### Results
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#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
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iamleonie/leonies-test | iamleonie | 2025-05-30T10:10:09Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:6448",
"loss:MultipleNegativesRankingLoss",
"en",
"dataset:yymYYM/stock_trading_QA",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:BAAI/bge-base-en-v1.5",
"base_model:finetune:BAAI/bge-base-en-v1.5",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-05-30T10:09:26Z | ---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6448
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: How are retail sales data integrated into trading models?
sentences:
- Lagged variables represent historical values of a time series variable and are
used in forecasting models to capture the impact of past observations on future
market trends, enhancing the accuracy of predictions by incorporating relevant
historical information.
- Retail sales data reflect consumer spending patterns and overall economic activity.
Traders analyze this indicator to gauge consumer confidence, sectoral performance,
and potential market trends related to retail-focused stocks.
- Regulatory approval for a new drug can have a positive impact on a pharmaceutical
company's stock price as it opens up new revenue streams and market opportunities.
- source_sentence: What impact does algorithmic trading have on market liquidity?
sentences:
- Volume analysis in stock trading involves studying the number of shares or contracts
traded in a security or market over a specific period to gauge the strength or
weakness of a price move.
- Social media sentiment analysis can assist in detecting anomalies in stock prices
by capturing public sentiment and opinions on stocks, identifying trends or sudden
shifts in sentiment that may precede abnormal price movements.
- Algorithmic trading can impact market liquidity by increasing trading speed, efficiency,
and overall trading volume, leading to potential liquidity disruptions during
certain market conditions.
- source_sentence: What considerations should traders take into account when selecting
an adaptive trading algorithm?
sentences:
- Historical price data helps analysts identify patterns and trends that can be
used to develop models for predicting future stock prices based on past performance.
- Traders should consider factors such as performance metrics, risk management capabilities,
adaptability to changing market conditions, data requirements, and the level of
transparency and control offered by the algorithm.
- A stock exchange is a centralized marketplace where securities like stocks, bonds,
and commodities are bought and sold by investors and traders.
- source_sentence: How can currency exchange rates and forex markets be integrated
into trading models alongside macroeconomic indicators?
sentences:
- Moving averages smooth out price data over a specified period, making it easier
to identify trends and reversals in stock prices.
- Currency exchange rates and forex markets are integrated into trading models to
assess currency risk, international trade impact, and cross-border investment
opportunities influenced by macroeconomic indicators.
- Investors use quantitative momentum indicators to identify securities that are
gaining positive momentum and potentially generating profits by buying those assets
and selling underperforming assets.
- source_sentence: What role does back-testing play in refining event-driven trading
strategies using historical data and real-time analysis?
sentences:
- Genetic algorithms are well-suited for solving multi-objective optimization problems,
nonlinear and non-convex optimization problems, problems with high-dimensional
search spaces, and problems where traditional methods may struggle to find optimal
solutions.
- Risk management techniques such as position sizing, portfolio diversification,
and stop-loss orders are often used in quantitative momentum strategies to manage
downside risk and protect against large losses.
- Back-testing allows traders to evaluate the performance of event-driven trading
strategies using historical data, identify patterns, optimize parameters, and
refine strategies for real-time implementation.
datasets:
- yymYYM/stock_trading_QA
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@3
- cosine_precision@3
- cosine_recall@3
- cosine_ndcg@3
- cosine_mrr@3
- cosine_map@3
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@3
value: 0.6750348675034867
name: Cosine Accuracy@3
- type: cosine_precision@3
value: 0.22501162250116222
name: Cosine Precision@3
- type: cosine_recall@3
value: 0.6750348675034867
name: Cosine Recall@3
- type: cosine_ndcg@3
value: 0.5838116811117793
name: Cosine Ndcg@3
- type: cosine_mrr@3
value: 0.5523012552301251
name: Cosine Mrr@3
- type: cosine_map@3
value: 0.5523012552301255
name: Cosine Map@3
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) dataset. It maps sentences & paragraphs to a 768-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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA)
- **Language:** en
<!-- - **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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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 ๐ค Hub
model = SentenceTransformer("iamleonie/leonies-test")
# Run inference
sentences = [
'What role does back-testing play in refining event-driven trading strategies using historical data and real-time analysis?',
'Back-testing allows traders to evaluate the performance of event-driven trading strategies using historical data, identify patterns, optimize parameters, and refine strategies for real-time implementation.',
'Risk management techniques such as position sizing, portfolio diversification, and stop-loss orders are often used in quantitative momentum strategies to manage downside risk and protect against large losses.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy@3 | 0.675 |
| cosine_precision@3 | 0.225 |
| cosine_recall@3 | 0.675 |
| **cosine_ndcg@3** | **0.5838** |
| cosine_mrr@3 | 0.5523 |
| cosine_map@3 | 0.5523 |
<!--
## 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.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### stock_trading_qa
* Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727)
* Size: 6,448 training samples
* Columns: <code>anchor</code> and <code>context</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | context |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 15.83 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 34.67 tokens</li><li>max: 59 tokens</li></ul> |
* Samples:
| anchor | context |
|:------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How should I approach investing in a volatile stock market?</code> | <code>Diversify your portfolio, invest in stable companies, consider dollar-cost averaging, and stay informed about market trends to make informed trading decisions.</code> |
| <code>What is the role of cross-validation in assessing the performance of time series forecasting models for stock market trends?</code> | <code>Cross-validation helps evaluate the generalization ability of forecasting models by partitioning historical data into training and validation sets, ensuring that the model's performance is robust and reliable for future predictions.</code> |
| <code>What role does correlation play in statistical arbitrage and pair trading?</code> | <code>Correlation measures the relationship between asset prices and helps traders identify pairs that exhibit a stable price relationship suitable for pair trading.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### stock_trading_qa
* Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727)
* Size: 717 evaluation samples
* Columns: <code>anchor</code> and <code>context</code>
* Approximate statistics based on the first 717 samples:
| | anchor | context |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 15.96 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 35.03 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
| anchor | context |
|:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How can anomaly detection in stock prices be used to identify market inefficiencies and opportunities for arbitrage?</code> | <code>Anomaly detection can help identify market inefficiencies by spotting mispricings and opportunities for arbitrage, where traders can exploit price differentials to make profits by trading on anomalies.</code> |
| <code>How do traders interpret the Head and Shoulders pattern as a trading signal?</code> | <code>The Head and Shoulders pattern is a reversal pattern with three peaks, where the middle peak (head) is higher than the other two (shoulders), signaling a potential trend reversal and offering a bearish trading signal.</code> |
| <code>How do traders use Fibonacci levels as trading signals?</code> | <code>Fibonacci levels are used as trading signals to identify potential support and resistance levels, trend reversals, and price targets in financial markets.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `optim`: adamw_8bit
- `batch_sampler`: no_duplicates
#### 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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `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_8bit
- `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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | cosine_ndcg@3 |
|:------:|:----:|:-------------:|:---------------:|:-------------:|
| -1 | -1 | - | - | 0.4451 |
| 0.3970 | 10 | 5.7817 | 0.0765 | 0.5278 |
| 0.7940 | 20 | 1.295 | 0.0251 | 0.5608 |
| 1.1588 | 30 | 0.6208 | 0.0209 | 0.5771 |
| 1.5558 | 40 | 0.5701 | 0.0183 | 0.5789 |
| 1.9529 | 50 | 0.4546 | 0.0171 | 0.5882 |
| 2.3176 | 60 | 0.2861 | 0.0160 | 0.5839 |
| 2.7146 | 70 | 0.3315 | 0.0154 | 0.5818 |
| 3.0794 | 80 | 0.3179 | 0.0152 | 0.5852 |
| 3.4764 | 90 | 0.367 | 0.0150 | 0.5843 |
| 3.8734 | 100 | 0.354 | 0.0150 | 0.5838 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
benjaminsinzore/CSM-MrDragonFox-Elise | benjaminsinzore | 2025-05-30T10:06:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"csm",
"text-to-audio",
"text-generation-inference",
"unsloth",
"en",
"dataset:MrDragonFox/Elise",
"base_model:unsloth/csm-1b",
"base_model:finetune:unsloth/csm-1b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-05-30T08:51:27Z | ---
base_model: unsloth/csm-1b
tags:
- text-generation-inference
- transformers
- unsloth
- csm
license: apache-2.0
language:
- en
datasets:
- MrDragonFox/Elise
---
# Uploaded finetuned model
- **Developed by:** Benjamin Sinzore
- **License:** apache-2.0
|
serbaz/olenav | serbaz | 2025-05-30T10:00:38Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-30T09:17:16Z | ---
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
--- |
rziga/mm_grounding_dino_base_o365v1_goldg_v3det | rziga | 2025-05-30T09:57:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mm-grounding-dino",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T09:56:15Z | ---
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 ๐ค 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] |
RandyXi/CodeLlama-34b-Instruct-QtFineTuned-EnCORTEK-3ET | RandyXi | 2025-05-30T09:55:37Z | 1 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:codellama/CodeLlama-34b-Instruct-hf",
"base_model:adapter:codellama/CodeLlama-34b-Instruct-hf",
"license:llama2",
"region:us"
] | null | 2025-05-27T00:39:55Z | ---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-34b-Instruct-hf
tags:
- generated_from_trainer
model-index:
- name: CodeLlama-34b-Instruct-QtFineTuned-EnCORTEK-3ET
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. -->
# CodeLlama-34b-Instruct-QtFineTuned-EnCORTEK-3ET
This model is a fine-tuned version of [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW 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: 0.03
- num_epochs: 1
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |
Free2035/Phi-4-freedom | Free2035 | 2025-05-30T09:46:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"custom_code",
"en",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:finetune:microsoft/Phi-4-mini-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T09:43:32Z | ---
base_model: microsoft/Phi-4-mini-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- phi3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Free2035
- **License:** apache-2.0
- **Finetuned from model :** microsoft/Phi-4-mini-instruct
This phi3 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)
|
connector/pig-1k | connector | 2025-05-30T09:43:24Z | 0 | 1 | null | [
"pig",
"text-to-image",
"en",
"license:mit",
"region:us"
] | text-to-image | 2025-01-31T09:40:44Z | ---
license: mit
language:
- en
pipeline_tag: text-to-image
tags:
- pig
---
# pig studio model: pig-1k
- diffusion model for image generation
- compatible with t5xxl text encoder
- similar architecture to pixart-ฮฑ but slightly different
- try it out you will know the difference
# pig studio model: pig-1k-aura
- diffusion model for image generation
- compatible with t5xl text encoder
- similar architecture to aura but slightly different
- try it out you will know the difference
# pig studio model: pig-1k-sd
- diffusion model for image generation
- compatible with clip:g-l and t5xxl text encoder
- similar architecture to sd but slightly different
- try it out you will know the difference
# pig studio model: pig-1k-flux
- diffusion model for image generation
- compatible with clip-l and t5xxl text encoder
- similar architecture to flux but slightly different
- try it out you will know the difference |
Varinder2110/3cd637e9-6440-4c4b-a609-02f979efeeb9 | Varinder2110 | 2025-05-30T09:39:25Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-30T08:34:18Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# 3Cd637E9 6440 4C4B A609 02F979Efeeb9
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Varinder2110/3cd637e9-6440-4c4b-a609-02f979efeeb9/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Varinder2110/3cd637e9-6440-4c4b-a609-02f979efeeb9', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 6000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Varinder2110/3cd637e9-6440-4c4b-a609-02f979efeeb9/discussions) to add images that show off what youโve made with this LoRA.
|
Tandogan/dpo_v3_alpaca_on_base_big | Tandogan | 2025-05-30T09:31:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T09:30:18Z | ---
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 ๐ค 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] |
apriasmoro/27e554a7-9349-41b8-b91f-45cc2482a433 | apriasmoro | 2025-05-30T09:29:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-05-30T09:15:17Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 27e554a7-9349-41b8-b91f-45cc2482a433
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: `0.10.0.dev0`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: true
chat_template: llama3
datasets:
- data_files:
- 12015d7c9ee7f3df_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: None
field_instruction: instruct
field_output: output
field_system: None
format: None
no_input_format: None
system_format: '{system}'
system_prompt: None
eval_max_new_tokens: 256
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/27e554a7-9349-41b8-b91f-45cc2482a433
learning_rate: 0.0002
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 15
micro_batch_size: 12
mlflow_experiment_name: /tmp/12015d7c9ee7f3df_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_steps: 200
sequence_len: 2048
special_tokens:
pad_token: </s>
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0aa91fdd-f464-4c35-9e87-5ba2524c6ecc
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: 0aa91fdd-f464-4c35-9e87-5ba2524c6ecc
warmup_steps: 100
weight_decay: 0.01
```
</details><br>
# 27e554a7-9349-41b8-b91f-45cc2482a433
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5053
## 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: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- 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: 100
- training_steps: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0702 | 1 | 1.5261 |
| No log | 0.2105 | 3 | 1.5915 |
| No log | 0.4211 | 6 | 1.5176 |
| No log | 0.6316 | 9 | 1.4834 |
| 2.1415 | 0.8421 | 12 | 1.4475 |
| 2.1415 | 1.0 | 15 | 1.5053 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
quentinbch/whisper-tiny-finetuned-minds14 | quentinbch | 2025-05-30T09:25:21Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"fr",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-30T08:41:23Z | ---
library_name: transformers
language:
- fr
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: openai/whisper-tiny-finetuned-minds14
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 33.536957849725106
---
<!-- 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. -->
# openai/whisper-tiny-finetuned-minds14
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6191
- Wer Ortho: 33.5451
- Wer: 33.5370
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:-------:|
| 0.0006 | 17.8571 | 500 | 0.6191 | 33.5451 | 33.5370 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
BAAI/Video-XL-2 | BAAI | 2025-05-30T09:14:53Z | 0 | 1 | null | [
"safetensors",
"qwen2",
"license:apache-2.0",
"region:us"
] | null | 2025-05-30T09:02:31Z | ---
license: apache-2.0
---
|
alyssacheng/my_awesome_model | alyssacheng | 2025-05-30T09:02:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T06:51:45Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_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_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1934
- Accuracy: 0.9272
## 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: 64
- eval_batch_size: 64
- 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2057 | 1.0 | 391 | 0.1969 | 0.9224 |
| 0.1759 | 2.0 | 782 | 0.1934 | 0.9272 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
aledm03/new_temp_SFT_training | aledm03 | 2025-05-30T08:56:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Qwen3-0.6B-Base",
"base_model:finetune:unsloth/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T08:55:29Z | ---
base_model: unsloth/Qwen3-0.6B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** aledm03
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B-Base
This qwen3 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)
|
FormlessAI/5be0a8cc-b3b7-4eef-a71e-25beb6b20c1e | FormlessAI | 2025-05-30T08:41:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T06:16:34Z | ---
base_model: microsoft/phi-1_5
library_name: transformers
model_name: 5be0a8cc-b3b7-4eef-a71e-25beb6b20c1e
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 5be0a8cc-b3b7-4eef-a71e-25beb6b20c1e
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5).
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="FormlessAI/5be0a8cc-b3b7-4eef-a71e-25beb6b20c1e", 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/phoenix-formless/Gradients/runs/ev4gga0p)
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: 0.18.0
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
FormlessAI/987ddd57-af27-4507-a3e1-ffdb58eb5246 | FormlessAI | 2025-05-30T08:41:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:finetune:lmsys/vicuna-7b-v1.5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T07:11:40Z | ---
base_model: lmsys/vicuna-7b-v1.5
library_name: transformers
model_name: 987ddd57-af27-4507-a3e1-ffdb58eb5246
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 987ddd57-af27-4507-a3e1-ffdb58eb5246
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5).
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="FormlessAI/987ddd57-af27-4507-a3e1-ffdb58eb5246", 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/phoenix-formless/Gradients/runs/sbcd47k3)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
jokerwu0519/dummy-model | jokerwu0519 | 2025-05-30T08:34:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T08:16:59Z | ---
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 ๐ค 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] |
xuan-luo/DiffSkip-Llama-3-8B-Instruct | xuan-luo | 2025-05-30T08:30:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"ddllama",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:allenai/tulu-v2-sft-mixture",
"dataset:xuan-luo/FlexiPatterns-Llama-3-8B-Instruct",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-05-30T07:58:16Z | ---
license: apache-2.0
datasets:
- allenai/tulu-v2-sft-mixture
- xuan-luo/FlexiPatterns-Llama-3-8B-Instruct
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
# DiffSkip-Llama-3-8B-Instruct
The implementation of the paper Differential Layer Skipping in Large Language Models.
### Model Description
DiffSkip-Llama-3-8B-Instruct is an enhanced version of the Llama-3-8B-Instruct model, incorporating the Differential Layer Skipping (DiffSkip) method to enable dynamic Feed-Forward Network (FFN) skipping during text generation. This approach leverages the self-attention input-output difference as a routing signal, allowing tokens to bypass FFN blocks based on computational needs.
- **Developed by:** Xuan Luo, Weizhi Wang, Xifeng Yan
- **Model type:** Causal Language Model with dynamic FFN skipping
- **Language(s) (NLP):** English (en)
- **License:** Apache-2.0
- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
## Model Card Contact
For questions or inquiries, please contact [[email protected]](mailto:[email protected]). |
LarryAIDraw/Noir_Black_Rabbit_Nikke_The_Goddess_of_Victory | LarryAIDraw | 2025-05-30T08:27:32Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-30T08:17:37Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/974589/noir-black-rabbit-nikke-the-goddess-of-victory?modelVersionId=1091335 |
HeOeH/ttmamba | HeOeH | 2025-05-30T08:20:45Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-30T08:10:43Z | Found. Redirecting to https://cdn-lfs-us-1.hf.co/repos/21/ed/21edfa7c4300869037612716edf31f620e4f7910b176b586add3ff2539002b29/4bcf87ecfbbb8e07a01b21415a970c8b53a5283bf6872b657040d3f45c9241f7?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1748612772&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0ODYxMjc3Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzIxL2VkLzIxZWRmYTdjNDMwMDg2OTAzNzYxMjcxNmVkZjMxZjYyMGU0Zjc5MTBiMTc2YjU4NmFkZDNmZjI1MzkwMDJiMjkvNGJjZjg3ZWNmYmJiOGUwN2EwMWIyMTQxNWE5NzBjOGI1M2E1MjgzYmY2ODcyYjY1NzA0MGQzZjQ1YzkyNDFmNz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=okAAuHtV0vLlALAirj6%7Ew47Wsc0rzkXXIdkOjJStwnu1b%7EXIuvnl4ZaiBIn3gzOVsAP1lDqRN4ZUeLdsSUBZ-R7iMvQisDgUOafBFwJb9WmPhjnYDiijt7rbFo8olQUKbNJ4PJnuzjtE%7E4TimfbX%7EJYafeTICmUmZZXSXTlq6S7zdB991nCYcWDJTiW33EKQEgtQCpDGbx-tL3mQhCu2fbL13jGShbX%7Es5-afyn9R1uB6KGw7hKYFb7eN1cGaOuuxgQmhasUUJd0PoEN0BNLvOXyND04UWBMImEfNbR--JNkcSGBJqGcL8FSiEm8zJGacu8GyKxRPFGfiPpudlGeuw__&Key-Pair-Id=K24J24Z295AEI9 |
TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B-Q4_K_M-GGUF | TheMindExpansionNetwork | 2025-05-30T08:20:09Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"llama-factory",
"llama-cpp",
"gguf-my-repo",
"base_model:TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B",
"base_model:quantized:TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T08:19:21Z | ---
library_name: transformers
tags:
- llama-factory
- llama-cpp
- gguf-my-repo
base_model: TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B
---
# TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B`](https://huggingface.co/TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B) 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/TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B) 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 TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file m1ndb0t-dr34m3r-r1-0528-qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file m1ndb0t-dr34m3r-r1-0528-qwen3-8b-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 TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file m1ndb0t-dr34m3r-r1-0528-qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo TheMindExpansionNetwork/M1NDB0T-DR34M3R-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file m1ndb0t-dr34m3r-r1-0528-qwen3-8b-q4_k_m.gguf -c 2048
```
|
Jackmin108/qwen-7b-rl-step-32 | Jackmin108 | 2025-05-30T08:13:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2501.12948",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T05:04:09Z | ---
license: mit
library_name: transformers
---
# DeepSeek-R1
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<p align="center">
<a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf"><b>Paper Link</b>๐๏ธ</a>
</p>
## 1. Introduction
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.
With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.
However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance,
we introduce DeepSeek-R1, which incorporates cold-start data before RL.
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.
**NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section.**
<p align="center">
<img width="80%" src="figures/benchmark.jpg">
</p>
## 2. Model Summary
---
**Post-Training: Large-Scale Reinforcement Learning on the Base Model**
- We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.
- We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities.
We believe the pipeline will benefit the industry by creating better models.
---
**Distillation: Smaller Models Can Be Powerful Too**
- We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future.
- Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.
## 3. Model Downloads
### DeepSeek-R1 Models
<div align="center">
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: | :------------: | :------------: |
| DeepSeek-R1-Zero | 671B | 37B | 128K | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero) |
| DeepSeek-R1 | 671B | 37B | 128K | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
</div>
DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base.
For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository.
### DeepSeek-R1-Distill Models
<div align="center">
| **Model** | **Base Model** | **Download** |
| :------------: | :------------: | :------------: |
| DeepSeek-R1-Distill-Qwen-1.5B | [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) |
| DeepSeek-R1-Distill-Qwen-7B | [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) |
| DeepSeek-R1-Distill-Llama-8B | [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) |
| DeepSeek-R1-Distill-Qwen-14B | [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) |
|DeepSeek-R1-Distill-Qwen-32B | [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) |
| DeepSeek-R1-Distill-Llama-70B | [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) | [๐ค HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) |
</div>
DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1.
We slightly change their configs and tokenizers. Please use our setting to run these models.
## 4. Evaluation Results
### DeepSeek-R1-Evaluation
For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1.
<div align="center">
| Category | Benchmark (Metric) | Claude-3.5-Sonnet-1022 | GPT-4o 0513 | DeepSeek V3 | OpenAI o1-mini | OpenAI o1-1217 | DeepSeek R1 |
|----------|-------------------|----------------------|------------|--------------|----------------|------------|--------------|
| | Architecture | - | - | MoE | - | - | MoE |
| | # Activated Params | - | - | 37B | - | - | 37B |
| | # Total Params | - | - | 671B | - | - | 671B |
| English | MMLU (Pass@1) | 88.3 | 87.2 | 88.5 | 85.2 | **91.8** | 90.8 |
| | MMLU-Redux (EM) | 88.9 | 88.0 | 89.1 | 86.7 | - | **92.9** |
| | MMLU-Pro (EM) | 78.0 | 72.6 | 75.9 | 80.3 | - | **84.0** |
| | DROP (3-shot F1) | 88.3 | 83.7 | 91.6 | 83.9 | 90.2 | **92.2** |
| | IF-Eval (Prompt Strict) | **86.5** | 84.3 | 86.1 | 84.8 | - | 83.3 |
| | GPQA-Diamond (Pass@1) | 65.0 | 49.9 | 59.1 | 60.0 | **75.7** | 71.5 |
| | SimpleQA (Correct) | 28.4 | 38.2 | 24.9 | 7.0 | **47.0** | 30.1 |
| | FRAMES (Acc.) | 72.5 | 80.5 | 73.3 | 76.9 | - | **82.5** |
| | AlpacaEval2.0 (LC-winrate) | 52.0 | 51.1 | 70.0 | 57.8 | - | **87.6** |
| | ArenaHard (GPT-4-1106) | 85.2 | 80.4 | 85.5 | 92.0 | - | **92.3** |
| Code | LiveCodeBench (Pass@1-COT) | 33.8 | 34.2 | - | 53.8 | 63.4 | **65.9** |
| | Codeforces (Percentile) | 20.3 | 23.6 | 58.7 | 93.4 | **96.6** | 96.3 |
| | Codeforces (Rating) | 717 | 759 | 1134 | 1820 | **2061** | 2029 |
| | SWE Verified (Resolved) | **50.8** | 38.8 | 42.0 | 41.6 | 48.9 | 49.2 |
| | Aider-Polyglot (Acc.) | 45.3 | 16.0 | 49.6 | 32.9 | **61.7** | 53.3 |
| Math | AIME 2024 (Pass@1) | 16.0 | 9.3 | 39.2 | 63.6 | 79.2 | **79.8** |
| | MATH-500 (Pass@1) | 78.3 | 74.6 | 90.2 | 90.0 | 96.4 | **97.3** |
| | CNMO 2024 (Pass@1) | 13.1 | 10.8 | 43.2 | 67.6 | - | **78.8** |
| Chinese | CLUEWSC (EM) | 85.4 | 87.9 | 90.9 | 89.9 | - | **92.8** |
| | C-Eval (EM) | 76.7 | 76.0 | 86.5 | 68.9 | - | **91.8** |
| | C-SimpleQA (Correct) | 55.4 | 58.7 | **68.0** | 40.3 | - | 63.7 |
</div>
### Distilled Model Evaluation
<div align="center">
| Model | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
|------------------------------------------|------------------|-------------------|-----------------|----------------------|----------------------|-------------------|
| GPT-4o-0513 | 9.3 | 13.4 | 74.6 | 49.9 | 32.9 | 759 |
| Claude-3.5-Sonnet-1022 | 16.0 | 26.7 | 78.3 | 65.0 | 38.9 | 717 |
| o1-mini | 63.6 | 80.0 | 90.0 | 60.0 | 53.8 | **1820** |
| QwQ-32B-Preview | 44.0 | 60.0 | 90.6 | 54.5 | 41.9 | 1316 |
| DeepSeek-R1-Distill-Qwen-1.5B | 28.9 | 52.7 | 83.9 | 33.8 | 16.9 | 954 |
| DeepSeek-R1-Distill-Qwen-7B | 55.5 | 83.3 | 92.8 | 49.1 | 37.6 | 1189 |
| DeepSeek-R1-Distill-Qwen-14B | 69.7 | 80.0 | 93.9 | 59.1 | 53.1 | 1481 |
| DeepSeek-R1-Distill-Qwen-32B | **72.6** | 83.3 | 94.3 | 62.1 | 57.2 | 1691 |
| DeepSeek-R1-Distill-Llama-8B | 50.4 | 80.0 | 89.1 | 49.0 | 39.6 | 1205 |
| DeepSeek-R1-Distill-Llama-70B | 70.0 | **86.7** | **94.5** | **65.2** | **57.5** | 1633 |
</div>
## 5. Chat Website & API Platform
You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com), and switch on the button "DeepThink"
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
## 6. How to Run Locally
### DeepSeek-R1 Models
Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running DeepSeek-R1 locally.
**NOTE: Hugging Face's Transformers has not been directly supported yet.**
### DeepSeek-R1-Distill Models
DeepSeek-R1-Distill models can be utilized in the same manner as Qwen or Llama models.
For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm):
```shell
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager
```
You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang)
```bash
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2
```
### Usage Recommendations
**We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:**
1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
2. **Avoid adding a system prompt; all instructions should be contained within the user prompt.**
3. For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
4. When evaluating model performance, it is recommended to conduct multiple tests and average the results.
Additionally, we have observed that the DeepSeek-R1 series models tend to bypass thinking pattern (i.e., outputting "\<think\>\n\n\</think\>") when responding to certain queries, which can adversely affect the model's performance.
**To ensure that the model engages in thorough reasoning, we recommend enforcing the model to initiate its response with "\<think\>\n" at the beginning of every output.**
## 7. License
This code repository and the model weights are licensed under the [MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE).
DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that:
- DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE), and now finetuned with 800k samples curated with DeepSeek-R1.
- DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE).
- DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE).
## 8. Citation
```
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author={DeepSeek-AI},
year={2025},
eprint={2501.12948},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.12948},
}
```
## 9. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
|
anonymous6435/llemma-minilang | anonymous6435 | 2025-05-30T08:08:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T07:09:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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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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#### Preprocessing [optional]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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).
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quentinbch/whisper-tiny-finetuned-gtzan | quentinbch | 2025-05-30T08:05:10Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2025-05-30T07:32:14Z | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-tiny-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- 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-tiny-finetuned-gtzan
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4198
- Accuracy: 0.88
## 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: 16
- eval_batch_size: 16
- seed: 42
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7863 | 1.0 | 57 | 1.5165 | 0.64 |
| 0.9074 | 2.0 | 114 | 0.9433 | 0.67 |
| 0.5972 | 3.0 | 171 | 0.6179 | 0.8 |
| 0.3472 | 4.0 | 228 | 0.5855 | 0.78 |
| 0.2699 | 5.0 | 285 | 0.4670 | 0.84 |
| 0.1025 | 6.0 | 342 | 0.5236 | 0.81 |
| 0.0892 | 7.0 | 399 | 0.4453 | 0.85 |
| 0.0163 | 8.0 | 456 | 0.4244 | 0.91 |
| 0.0109 | 9.0 | 513 | 0.3771 | 0.9 |
| 0.01 | 10.0 | 570 | 0.4198 | 0.88 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
giayphuyen/gemma-3-4b-it-sphinx-chatbot | giayphuyen | 2025-05-30T08:04:44Z | 85 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T03:54:26Z | ---
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 ๐ค 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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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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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]
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<!-- 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
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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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[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:**
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Soughing/mlra_v2_alpha_2.0_beta_1.0_medium | Soughing | 2025-05-30T08:02:00Z | 40 | 0 | null | [
"pytorch",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-05-27T03:42:55Z | ---
license: apache-2.0
---
|
bhavinjawade/may23-gemma-4b-tq_sft_finetuned-model | bhavinjawade | 2025-05-30T08:01:58Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T22:32:46Z | ---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: may23-gemma-4b-tq_sft_finetuned-model
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for may23-gemma-4b-tq_sft_finetuned-model
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
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="bhavinjawade/may23-gemma-4b-tq_sft_finetuned-model", 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 SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.50.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 3.3.2
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
noza-kit/ACbase_byGemini_2-adapter | noza-kit | 2025-05-30T07:56:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-30T07:48:28Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[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
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[More Information Needed]
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#### 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]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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]
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- **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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ariG23498/gemma-3-4b-pt-od-coco | ariG23498 | 2025-05-30T07:54:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | image-text-to-text | 2025-05-30T07:01:37Z | ---
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 ๐ค 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
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### Direct Use
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[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]
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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
<!-- 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).
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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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. -->
**BibTeX:**
[More Information Needed]
**APA:**
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[More Information Needed]
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BSC-LT/salamandraTA-7b-instruct | BSC-LT | 2025-05-30T07:42:26Z | 1,448 | 11 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"translation",
"bg",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nb",
"no",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sl",
"sk",
"sr",
"sv",
"uk",
"ast",
"an",
"arxiv:2010.11125",
"arxiv:2403.14009",
"arxiv:1907.05791",
"arxiv:1911.04944",
"arxiv:2402.17733",
"arxiv:2207.04672",
"arxiv:2404.06392",
"arxiv:2309.04662",
"base_model:BSC-LT/salamandra-7b",
"base_model:finetune:BSC-LT/salamandra-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:eu"
] | translation | 2025-01-08T15:02:52Z | ---
license: apache-2.0
library_name: transformers
pipeline_tag: translation
language:
- bg
- ca
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nb
- 'no'
- nn
- oc
- pl
- pt
- ro
- ru
- sl
- sk
- sr
- sv
- uk
- ast
- an
base_model:
- BSC-LT/salamandra-7b
---
# SalamandraTA Model Card
SalamandraTA-7b-instruct is a translation LLM that has been instruction-tuned from SalamandraTA-7b-base.
The base model results from continually pre-training [Salamandra-7b](https://huggingface.co/BSC-LT/salamandra-7b) on parallel data and has not been published,
but is reserved for internal use.
SalamandraTA-7b-instruct is proficient in 35 European languages (plus 3 varieties) and supports translation-related tasks, namely: sentence-level-translation,
paragraph-level-translation, document-level-translation, automatic post-editing, grammar checking, machine translation evaluation, alternative translations,
named-entity-recognition and context-aware translation.
> [!WARNING]
> **DISCLAIMER:** This version of Salamandra is tailored exclusively for translation tasks. It lacks chat capabilities and has not been trained with any chat instructions.
---
## Model Details
### Description
SalamandraTA-7b-base is a continual pre-training of [Salamandra-7b](https://huggingface.co/BSC-LT/salamandra-7b) using parallel data, resulting in a total of 424B tokens processed during training.
### Architecture
| | |
|-------------------------|:--------------|
| Total Parameters | 7,768,117,248 |
| Embedding Parameters | 1,048,576,000 |
| Layers | 32 |
| Hidden size | 4,096 |
| Attention heads | 32 |
| Context length | 8,192 |
| Vocabulary size | 256,000 |
| Precision | bfloat16 |
| Embedding type | RoPE |
| Activation Function | SwiGLU |
| Layer normalization | RMS Norm |
| Flash attention | โ
|
| Grouped Query Attention | โ
|
| Num. query groups | 8 |
---
## Intended Use
### Direct Use
The model is intended for both research and commercial use in any of the languages included in the training data for general machine translation tasks.
### Out-of-scope Use
The model is not intended for malicious activities, such as harming others or violating human rights.
Any downstream application must comply with current laws and regulations.
Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged.
---
## Hardware and Software
### Training Framework
SalamandraTA-7b-base was continually pre-trained using NVIDIAโs [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html),
which leverages PyTorch Lightning for efficient model training in highly distributed settings.
SalamandraTA-7b-instruct was produced with [FastChat](https://github.com/lm-sys/FastChat).
### Compute Infrastructure
All models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and
operated by Barcelona Supercomputing Center.
The accelerated partition is composed of 1,120 nodes with the following specifications:
- 4x Nvidia Hopper GPUs with 64GB HBM2 memory
- 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)
- 4x NDR200 (BW per node 800Gb/s)
- 512 GB of Main memory (DDR5)
- 460GB on NVMe storage
---
## How to use
You can translate between the following 35 languages (and 3 varieties):
Aragonese, Asturian, Basque, Bulgarian, Catalan and Valencian variety, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hungarian,
Irish, Italian, Latvian, Lithuanian, Maltese, Norwegian (Bokmรฅl and Nynorsk varieties), Occitan and Aranese variety, Polish, Portuguese, Romanian, Russian, Serbian, Slovak,
Slovenian, Spanish, Swedish, Ukrainian, Welsh.
The instruction-following model uses the commonly adopted ChatML template:
```
<|im_start|>system
{SYSTEM PROMPT}<|im_end|>
<|im_start|>user
{USER PROMPT}<|im_end|>
<|im_start|>assistant
{MODEL RESPONSE}<|im_end|>
<|im_start|>user
[...]
```
The easiest way to apply it is by using the tokenizer's built-in functions, as shown in the following snippet.
```python
from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "BSC-LT/salamandraTA-7b-instruct"
source = 'Spanish'
target = 'Catalan'
sentence = "Ayer se fue, tomรณ sus cosas y se puso a navegar. Una camisa, un pantalรณn vaquero y una canciรณn, dรณnde irรก, dรณnde irรก. Se despidiรณ, y decidiรณ batirse en duelo con el mar. Y recorrer el mundo en su velero. Y navegar, nai-na-na, navegar"
text = f"Translate the following text from {source} into {target}.\n{source}: {sentence} \n{target}:"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
message = [ { "role": "user", "content": text } ]
date_string = datetime.today().strftime('%Y-%m-%d')
prompt = tokenizer.apply_chat_template(
message,
tokenize=False,
add_generation_prompt=True,
date_string=date_string
)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
input_length = inputs.shape[1]
outputs = model.generate(input_ids=inputs.to(model.device),
max_new_tokens=400,
early_stopping=True,
num_beams=5)
print(tokenizer.decode(outputs[0, input_length:], skip_special_tokens=True))
# Ahir se'n va anar, va recollir les seves coses i es va fer a la mar. Una camisa, uns texans i una canรงรณ, on anirร , on anirร . Es va acomiadar i va decidir batre's en duel amb el mar. I fer la volta al mรณn en el seu veler. I navegar, nai-na-na, navegar
```
Using this template, each turn is preceded by a `<|im_start|>` delimiter and the role of the entity
(either `user`, for content supplied by the user, or `assistant` for LLM responses), and finished with the `<|im_end|>` token.
#### General translation
For machine translation tasks, you can use the following prompt template:
```
Translate the following text from {source} into {target}.
{source}: {source sentence}
{target}:
```
<details>
<summary>Show an example</summary>
```python
source = 'Catalan'
target = 'Galician'
source_sentence = "Als antics egipcis del perรญode de l'Imperi Nou els fascinaven els monuments dels seus predecessors, que llavors tenien mรฉs de mil anys."
text = f"Translate the following text from {source} into {target}.\n{source}: {source_sentence} \n{target}:"
# Os antigos exipcios do perรญodo do Imperio Novo estaban fascinados polos monumentos dos seus predecesores, que entรณn tiรฑan mรกis de mil anos de antigรผidade.
```
</details>
### Post-editing
For post-editing tasks, you can use the following prompt template:
```
Please fix any mistakes in the following {source}-{target} machine translation or keep it unedited if it's correct.
Source: {source_sentence}
MT: {machine_translation}
Corrected:"
```
<details>
<summary>Show an example</summary>
```python
source = 'Catalan'
target = 'English'
source_sentence = 'Rafael Nadal i Maria Magdalena van inspirar a una generaciรณ sencera.'
machine_translation = 'Rafael Christmas and Maria the Muffin inspired an entire generation each in their own way.'
text = f"Please fix any mistakes in the following {source}-{target} machine translation or keep it unedited if it's correct.\nSource: {source_sentence} \nMT: {machine_translation} \nCorrected:"
# Rafael Nadal and Maria Magdalena inspired an entire generation.
```
</details>
### Document-level translation
For document-level translation tasks, you can use the following prompt template:
```
Please translate this text from {source} into {target}.
{source}: {1st paragraph of the document}
{2nd paragraph of the document}
{Nth paragraph of the document}
{target}:
```
<details>
<summary>Show an example</summary>
```python
source = 'English'
target = 'Asturian'
text = """Please translate this text from {} into {}.\n{}: President Donald Trump, who campaigned on promises to crack down on illegal immigration, has raised alarms in the U.S. dairy industry with his threat to impose 25% tariffs on Mexico and Canada by February 2025. This move is part of a broader strategy to declare a national emergency at the southern border to halt illegal migration completely.
However, the implications for the agriculture sector, particularly dairy, are significant. Approximately half of the U.S. dairy industry's workforce consists of immigrant labor, many of whom are undocumented. The National Milk Producers Federation estimates that removing immigrant workers could decimate the dairy herd by 2.1 million cows and slash milk production by nearly 50 billion pounds, leading to a dramatic 90.4% increase in milk prices.
The complex perspectives of Americans on undocumented workers were highlighted in a Pew Research Center study. While 64% of U.S. adults support legal pathways for undocumented immigrants, 35% oppose itโa gap that has been narrowing recently. Factors influencing public opinion include the belief that immigrants should have jobs and pass security checks, contrasted by concerns about lawbreakers being rewarded, fairness for legal migrants, and resource allocation.
According to Zach Rutledge, an agricultural economist at Michigan State University, as nations grow wealthier, their labor forces transition away from agriculture toward sectors like services and manufacturing. This shift has led to the U.S. relying heavily on immigrant labor for agricultural work. Domestic workers, even with employment taxes, may cost $15 to $25 an hour, while H-2A visa program workers might cost $25 to $30 an hour, accounting for additional housing expenses.
The National Milk Producers Federation has been vocal in advocating for changes to the H-2A visa program, which outside of its current seasonal limitations, does not support the dairy industry's year-round labor needs. Executive vice-president Jaime Castaneda reiterated the need for legislative clarity to address the undocumented workforce issues in dairy farming.
The Farm Workforce Modernization Act of 2023, which could grant legal status to certain undocumented farmworkers, has been stalled in Congress, despite acknowledgment of the sector's importance to feeding America. The need for coordinated legislative efforts to ensure both border security and labor market stability is imperative moving forward.
{}:""".format(source, target, source, target)
```
</details>
### Named-entity recognition
For named-entity recognition tasks, you can use the following prompt template:
```
Analyse the following tokenized text and mark the tokens containing named entities.
Use the following annotation guidelines with these tags for named entities:
- ORG (Refers to named groups or organizations)
- PER (Refers to individual people or named groups of people)
- LOC (Refers to physical places or natural landmarks)
- MISC (Refers to entities that don't fit into standard categories).
Prepend B- to the first token of a given entity and I- to the remaining ones if they exist.
If a token is not a named entity, label it as O.
Input: {list of words in a sentence}
Marked:
```
<details>
<summary>Show an example</summary>
```python
text = """Analyse the following tokenized text and mark the tokens containing named entities.
Use the following annotation guidelines with these tags for named entities:
- ORG (Refers to named groups or organizations)
- PER (Refers to individual people or named groups of people)
- LOC (Refers to physical places or natural landmarks)
- MISC (Refers to entities that don't fit into standard categories).
Prepend B- to the first token of a given entity and I- to the remaining ones if they exist.
If a token is not a named entity, label it as O.
Input: ['La', 'defensa', 'del', 'antiguo', 'responsable', 'de', 'la', 'RFEF', 'confirma', 'que', 'interpondrรก', 'un', 'recurso.']
Marked: """
# [('La', 'O'), ('defensa', 'O'), ('del', 'O'), ('antiguo', 'O'), ('responsable', 'O'), ('de', 'O'), ('la', 'O'), ('RFEF', 'B-ORG'), ('confirma', 'O'), ('que', 'O'), ('interpondrรก', 'O'), ('un', 'O'), ('recurso.', 'O')]
```
</details>
### Grammar checker
For fixing any mistakes in grammar, you can use the following prompt template:
```
Please fix any mistakes in the following {source} sentence or keep it unedited if it's correct.
Sentence: {sentence}
Corrected:
```
<details>
<summary>Show an example</summary>
```python
source = 'Catalan'
sentence = 'Entonses, el meu jefe mโha dit que he de treballar els fins de setmana.'
text = f"Please fix any mistakes in the following {source} sentence or keep it unedited if it's correct.\nSentence: {sentence} \nCorrected:"
# Llavors, el meu cap m'ha dit que he de treballar els caps de setmana.
```
</details>
## Data
### Pretraining Data
The pretraining corpus consists of 424 billion tokens of Catalan-centric, Spanish-centric, and English-centric parallel data,
including all of the official European languages plus Catalan, Basque, Galician, Asturian, Aragonese and Aranese.
It amounts to 6,574,251,526 parallel sentence pairs.
This highly multilingual corpus is predominantly composed of data sourced from [OPUS](https://opus.nlpl.eu/),
with additional data taken from the [NTEU Project](https://nteu.eu/), [Aina Project](https://projecteaina.cat/), and other sources
(see: [Data Sources](#pre-data-sources) and [References](#pre-references)).
Where little parallel Catalan <-> xx data could be found, synthetic Catalan data was generated from the Spanish side of the collected Spanish <-> xx corpora using
[Projecte Ainaโs Spanish-Catalan model](https://huggingface.co/projecte-aina/aina-translator-es-ca). The final distribution of languages was as below:
Click the expand button below to see the full list of corpora included in the training data.
<details id="pre-data-sources">
<summary>Data Sources</summary>
| Dataset | Ca-xx Languages | Es-xx Langugages | En-xx Languages |
|-----------------------------------------------|----------------------------------------------------------------|-----------------------------------------------|----------------------------------------------------------------|
|[AINA](https://huggingface.co/projecte-aina) | en | | |
|ARANESE-SYNTH-CORPUS-BSC | arn | | |
|BOUA-SYNTH-BSC | | val | |
|[BOUMH](https://github.com/transducens/PILAR/tree/main/valencian/BOUMH) | | val | |
|[BOUA-PILAR](https://github.com/transducens/PILAR/tree/main/valencian/BOUA) | | val | |
|[CCMatrix](https://opus.nlpl.eu/CCMatrix/corpus/version/CCMatrix) |eu | | ga |
|[DGT](https://opus.nlpl.eu/DGT/corpus/version/DGT) | |bg,cs,da,de,el ,et,fi,fr,ga,hr,hu,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv | da,et,ga,hr,hu,lt,lv,mt,sh,sl|
|DOGV-SYNTH-BSC | | val | |
|[DOGV-PILAR](https://github.com/transducens/PILAR/tree/main/valencian/DOGV-html) | | val | |
|[ELRC-EMEA](https://opus.nlpl.eu/ELRC-EMEA/corpus/version/ELRC-EMEA) | |bg,cs,da,hu,lt,lv,mt,pl,ro,sk,sl | et,hr,lv,ro,sk,sl |
|[EMEA](https://opus.nlpl.eu/EMEA/corpus/version/EMEA) | |bg,cs,da,el,fi,hu,lt,mt,nl,pl,ro,sk,sl,sv | et,mt |
|[EUBookshop](https://opus.nlpl.eu/EUbookshop/corpus/version/EUbookshop) |lt,pl,pt |cs,da,de,el,fi,fr,ga,it,lv,mt,nl,pl,pt,ro,sk,sl,sv |cy,ga|
|[Europarl](https://opus.nlpl.eu/Europarl/corpus/version/Europarl) | |bg,cs,da,el,en,fi,fr,hu,lt,lv,nl,pl,pt ,ro,sk,sl,sv | |
|[Europat](https://opus.nlpl.eu/EuroPat/corpus/version/EuroPat) | |en,hr | no |
|[GAITU Corpus](https://gaitu.eus/) | | | eu|
|[KDE4](https://opus.nlpl.eu/KDE4/corpus/version/KDE4) |bg,cs,da,de,el ,et,eu,fi,fr,ga,gl,hr,it,lt,lv,nl,pl,pt,ro,sk,sl,sv |bg,ga,hr |cy,ga,nn,oc |
|[GlobalVoices](https://opus.nlpl.eu/GlobalVoices/corpus/version/GlobalVoices) | bg,de,fr,it,nl,pl,pt |bg,de,fr,pt | |
|[GNOME](https://opus.nlpl.eu/GNOME/corpus/version/GNOME) |eu,fr,ga,gl,pt |ga |cy,ga,nn|
|[JRC-Arquis](https://opus.nlpl.eu/JRC-Acquis/corpus/version/JRC-Acquis) | |cs,da,et,fr,lt,lv,mt,nl,pl ,ro,sv| et |
|LES-CORTS-VALENCIANES-SYNTH-BSC | | val | |
|[MaCoCu](https://opus.nlpl.eu/MaCoCu/corpus/version/MaCoCu) | en | | hr,mt,uk |
|[MultiCCAligned](https://opus.nlpl.eu/JRC-Acquis/corpus/version/JRC-Acquis) |bg,cs,de,el,et,fi,fr,hr,hu,it,lt,lv,nl,pl,ro,sk,sv |bg,fi,fr,hr,it,lv,nl,pt |bg,cy,da,et,fi,hr,hu,lt,lv,no,sl,sr,uk|
|[MultiHPLT](https://opus.nlpl.eu/MultiHPLT/corpus/version/MultiHPLT) |en, et,fi,ga,hr,mt | |fi,ga,gl,hr,mt,nn,sr |
|[MultiParaCrawl](https://opus.nlpl.eu/MultiParaCrawl/corpus/version/MultiParaCrawl) |bg,da |de,en,fr,ga,hr,hu,it,mt,pt |bg,cs,da,de,el,et,fi,fr,ga,hr,hu,lt,lv,mt,nn,pl,ro,sk,sl,uk|
|[MultiUN](https://opus.nlpl.eu/MultiUN/corpus/version/MultiUN) | |fr | |
|[News-Commentary](https://opus.nlpl.eu/News-Commentary/corpus/version/News-Commentary) | |fr | |
|[NLLB](https://opus.nlpl.eu/NLLB/corpus/version/NLLB) |bg,da,el,en,et,fi,fr,gl,hu,it ,lt,lv,pt,ro,sk,sl |bg,cs,da,de,el ,et,fi,fr,hu,it,lt,lv,nl,pl,pt ,ro,sk,sl,sv| bg,cs,cy,da,de,el,et,fi,fr,ga,hr,hu,it,lt,lv,mt,nl,no,oc,pl,pt,ro,ru,sk,sl,sr,sv,uk|
|[NรS Authentic Corpus](https://zenodo.org/records/7675110) | | | gl |
|[NรS Synthetic Corpus](https://zenodo.org/records/7685180) | | | gl |
|[NTEU](https://www.elrc-share.eu/repository/search/?q=NTEU) | |bg,cs,da,de,el,en,et,fi,fr,ga,hr,hu,it,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv | da,et,ga,hr,lt,lv,mt,ro,sk,sl,sv |
|[OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles) |bg,cs,da,de,el ,et,eu,fi,gl,hr,hu,lt,lv,nl,pl,pt,ro,sk,sl,sv |da,de,fi,fr,hr,hu,it,lv,nl | bg,cs,de,el,et,hr,fi,fr,hr,hu,no,sl,sr|
|[OPUS-100](https://opus.nlpl.eu/opus-100.php) | en | | gl |
|[StanfordNLP-NMT](https://opus.nlpl.eu/StanfordNLP-NMT/corpus/version/StanfordNLP-NMT) | | |cs |
|[Tatoeba](https://opus.nlpl.eu/Tatoeba/corpus/version/Tatoeba) |de,pt |pt | |
|[TildeModel](https://opus.nlpl.eu/TildeMODEL/corpus/version/TildeMODEL) | |bg | et,hr,lt,lv,mt |
|[UNPC](https://opus.nlpl.eu/UNPC/corpus/version/UNPC) | |en,fr | ru |
|[PILAR-VALENCIAN-AUTH](https://github.com/transducens/PILAR/tree/main/valencian/Generalitat) | | val | |
|[PILAR-VALENCIAN-SYNTH](https://github.com/transducens/PILAR/tree/main/valencian/Generalitat) | | val | |
|[WikiMatrix](https://opus.nlpl.eu/WikiMatrix/corpus/version/WikiMatrix) |bg,cs,da,de,el ,et,eu,fi,fr,gl,hr,hu,it,lt,nl,pl,pt,ro,sk,sl,sv |bg,en,fr,hr,it,pt | oc,sh |
|[Wikimedia](https://opus.nlpl.eu/wikimedia/corpus/version/wikimedia) | | |cy,nn |
|[XLENT](https://opus.nlpl.eu/XLEnt/corpus/version/XLEnt) |eu,ga,gl |ga |cy,et,ga,gl,hr,oc,sh|
Datasets with "-BSC" in their names (e.g., BOUA-SYNTH-BSC, DOGV-SYNTH-BSC) are synthetic datasets obtained by machine translating
pre-existing monolingual corpora with our own seq-to-seq models. These datasets were generated internally for model training and are not published.
To consult the data summary document with the respective licences, please send an e-mail to [email protected].
</details>
<details id="pre-references">
<summary>References</summary>
- Aulamo, M., Sulubacak, U., Virpioja, S., & Tiedemann, J. (2020). OpusTools and Parallel Corpus Diagnostics. In N. Calzolari, F. Bรฉchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 3782โ3789). European Language Resources Association. https://aclanthology.org/2020.lrec-1.467
- Chaudhary, V., Tang, Y., Guzmรกn, F., Schwenk, H., & Koehn, P. (2019). Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings. In O. Bojar, R. Chatterjee, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, A. J. Yepes, P. Koehn, A. Martins, C. Monz, M. Negri, A. Nรฉvรฉol, M. Neves, M. Post, M. Turchi, & K. Verspoor (Eds.), Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2) (pp. 261โ266). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-5435
- DGT-Translation MemoryโEuropean Commission. (n.d.). Retrieved November 4, 2024, from https://joint-research-centre.ec.europa.eu/language-technology-resources/dgt-translation-memory_en
- Eisele, A., & Chen, Y. (2010). MultiUN: A Multilingual Corpus from United Nation Documents. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh International Conference on Language Resources and Evaluation (LRECโ10). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf
- El-Kishky, A., Chaudhary, V., Guzmรกn, F., & Koehn, P. (2020). CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 5960โ5969. https://doi.org/10.18653/v1/2020.emnlp-main.480
- El-Kishky, A., Renduchintala, A., Cross, J., Guzmรกn, F., & Koehn, P. (2021). XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 10424โ10430. https://doi.org/10.18653/v1/2021.emnlp-main.814
- Fan, A., Bhosale, S., Schwenk, H., Ma, Z., El-Kishky, A., Goyal, S., Baines, M., Celebi, O., Wenzek, G., Chaudhary, V., Goyal, N., Birch, T., Liptchinsky, V., Edunov, S., Grave, E., Auli, M., & Joulin, A. (2020). Beyond English-Centric Multilingual Machine Translation (No. arXiv:2010.11125). arXiv. https://doi.org/10.48550/arXiv.2010.11125
- Garcรญa-Martรญnez, M., Biรฉ, L., Cerdร , A., Estela, A., Herranz, M., Kriลกlauks, R., Melero, M., OโDowd, T., OโGorman, S., Pinnis, M., Stafanoviฤ, A., Superbo, R., & Vasiฤผevskis, A. (2021). Neural Translation for European Union (NTEU). 316โ334. https://aclanthology.org/2021.mtsummit-up.23
- Gibert, O. de, Nail, G., Arefyev, N., Baรฑรณn, M., Linde, J. van der, Ji, S., Zaragoza-Bernabeu, J., Aulamo, M., Ramรญrez-Sรกnchez, G., Kutuzov, A., Pyysalo, S., Oepen, S., & Tiedemann, J. (2024). A New Massive Multilingual Dataset for High-Performance Language Technologies (No. arXiv:2403.14009). arXiv. http://arxiv.org/abs/2403.14009
- Koehn, P. (2005). Europarl: A Parallel Corpus for Statistical Machine Translation. Proceedings of Machine Translation Summit X: Papers, 79โ86. https://aclanthology.org/2005.mtsummit-papers.11
- Kreutzer, J., Caswell, I., Wang, L., Wahab, A., Van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., Setyawan, M., Sarin, S., Samb, S., Sagot, B., Rivera, C., Rios, A., Papadimitriou, I., Osei, S., Suarez, P. O., โฆ Adeyemi, M. (2022). Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Transactions of the Association for Computational Linguistics, 10, 50โ72. https://doi.org/10.1162/tacl_a_00447
- Rozis, R.,Skadiลลก, R (2017). Tilde MODEL - Multilingual Open Data for EU Languages. https://aclanthology.org/W17-0235
- Schwenk, H., Chaudhary, V., Sun, S., Gong, H., & Guzmรกn, F. (2019). WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (No. arXiv:1907.05791). arXiv. https://doi.org/10.48550/arXiv.1907.05791
- Schwenk, H., Wenzek, G., Edunov, S., Grave, E., & Joulin, A. (2020). CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB (No. arXiv:1911.04944). arXiv. https://doi.org/10.48550/arXiv.1911.04944
- Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufiล, D., & Varga, D. (n.d.). The JRC-Acquis: A Multilingual Aligned Parallel Corpus with 20+ Languages. http://www.lrec-conf.org/proceedings/lrec2006/pdf/340_pdf
- Subramani, N., Luccioni, S., Dodge, J., & Mitchell, M. (2023). Detecting Personal Information in Training Corpora: An Analysis. In A. Ovalle, K.-W. Chang, N. Mehrabi, Y. Pruksachatkun, A. Galystan, J. Dhamala, A. Verma, T. Cao, A. Kumar, & R. Gupta (Eds.), Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023) (pp. 208โ220). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.trustnlp-1.18
- Tiedemann, J. (23-25). Parallel Data, Tools and Interfaces in OPUS. In N. C. (Conference Chair), K. Choukri, T. Declerck, M. U. Doฤan, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Eight International Conference on Language Resources and Evaluation (LRECโ12). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper
- Ziemski, M., Junczys-Dowmunt, M., & Pouliquen, B. (n.d.). The United Nations Parallel Corpus v1.0. https://aclanthology.org/L16-1561
</details>
### Instruction Tuning Data
This model has been fine-tuned on ~135k instructions, primarily targeting machine translation performance for Catalan, English, and Spanish.
Additional instruction data for other European and closely related Iberian languages was also included, as it yielded a positive impact on the languages of interest.
That said, the performance in these additional languages is not guaranteed due to the limited amount of available data and the lack of resources for thorough testing.
A portion of our fine-tuning data comes directly from, or is sampled from [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2).
We also created additional datasets for our main languages of interest.
While tasks relating to machine translation are included, itโs important to note that no chat data was used in the fine-tuning process.
The final distribution of tasks was as below:
Click the expand button below to see the full list of tasks included in the finetuning data.
<details id="instr-data-sources">
<summary>Data Sources</summary>
| Task | Source | Languages | Count |
|----------------------------------|------------------------------------------------------------------------------------------|----------------------------------------------------------------|--------|
| Multi-reference Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [Tatoeba Dev (filtered)](https://github.com/Helsinki-NLP/Tatoeba-Challenge) | mixed | 10000 |
| Paraphrase | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [PAWS-X Dev](https://github.com/google-research-datasets/paws) | mixed | 3521 |
| Named-entity Recognition | [AnCora-Ca-NER](https://huggingface.co/datasets/projecte-aina/ancora-ca-ner) | ca | 12059 |
| Named-entity Recognition | [BasqueGLUE](https://huggingface.co/datasets/orai-nlp/basqueGLUE), [EusIE](https://huggingface.co/datasets/HiTZ/EusIE) | eu | 4304 |
| Named-entity Recognition | [SLI NERC Galician Gold Corpus](https://github.com/xavier-gz/SLI_Galician_Corpora) | gl | 6483 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | pt | 854 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | nl | 800 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | es | 1654 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | en | 1671 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | ru | 800 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | it | 858 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | fr | 857 |
| Named-entity Recognition | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | de | 1312 |
| Terminology-aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [WMT21 Terminology Dev (filtered)](https://www.statmt.org/wmt21/terminology-task.html) | en-ru | 50 |
| Terminology-aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [WMT21 Terminology Dev (filtered)](https://www.statmt.org/wmt21/terminology-task.html) | en-fr | 29 |
| Automatic Post Editing | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) | en-fr | 6133 |
| Automatic Post Editing | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) | en-nl | 9077 |
| Automatic Post Editing | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) | en-pt | 5762 |
| Automatic Post Editing | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) | de-en | 10000 |
| Automatic Post Editing | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/) | en-de | 10000 |
| Machine Translation Evaluation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2)-sample: [WMT20 to WMT22 Metrics MQM](https://www.statmt.org/wmt22/results.html), [WMT17 to WMT22 Metrics Direct Assessments](https://www.statmt.org/wmt22/results.html) | en-ru, en-pl, ru-en, en-de, en-ru, de-fr, de-en, en-de | 353 |
| Machine Translation Evaluation | Non-public | four pivot languages (eu, es, ca, gl) paired with European languages (bg, cs, da, de, el, en, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv) | 9700 |
| General Machine Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [WMT14 to WMT21](https://www.statmt.org/wmt22/results.html), [NTREX](https://github.com/MicrosoftTranslator/NTREX), [Flores Dev](https://github.com/facebookresearch/flores), [FRMT](https://github.com/google-research/google-research/tree/master/frmt), [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390), [ApeQuest](https://apequest.wordpress.com/), [OPUS (Quality Filtered)](https://opus.nlpl.eu/), [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | nl-en, en-ru, it-en, fr-en, es-en, en-fr, ru-en, fr-de, en-nl, de-fr | 500 |
| General Machine Translation | Non-public | three pivot languages (es, ca, en) paired with European languages (ast, arn, arg, bg, cs, cy, da, de, el, et, fi, ga, gl, hr, it, lt, lv, mt, nb, nn, nl, oc, pl, pt, ro, ru, sk, sl, sr, sv, uk, eu) | 9350 |
| Fill-in-the-Blank | Non-public | five pivot languages (ca, es, eu, gl, en) paired with European languages (cs, da, de, el, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv) | 11500 |
| Document-level Translation | Non-public | two pivot languages (es, en) paired with European languages (bg, cs, da, de, el, et, fi, fr, hu, it, lt, lv, nl, pl, pt, ro, ru, sk, sv) | 7600 |
| Paragraph-level Translation | Non-public | two pivot languages (es, en) paired with European languages (bg, cs, da, de, el, et, fi, fr, hu, it, lt, lv, nl, pl, pt, ro, ru, sk, sv) | 7600 |
| Context-Aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | en-it | 348 |
| Context-Aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | en-ru | 454 |
| Context-Aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | en-fr | 369 |
| Context-Aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | en-nl | 417 |
| Context-Aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | en-es | 431 |
| Context-Aware Translation | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | en-de | 558 |
|**Total** | | | **135,404** |
The non-public portion of this dataset was jointly created by the [ILENIA](https://proyectoilenia.es/) partners: BSC-LT, [HiTZ](http://hitz.ehu.eus/es),
and [CiTIUS](https://citius.gal/es/). For further information regarding the instruction-tuning data,
please contact <[email protected]>.
</details>
<details id="instr-references">
<summary>References</summary>
- Alves, D. M., Pombal, J., Guerreiro, N. M., Martins, P. H., Alves, J., Farajian, A., Peters, B., Rei, R., Fernandes, P., Agrawal, S., Colombo, P., de Souza, J. G. C., & Martins, A. F. T. (2024). Tower: An open multilingual large language model for translation-related tasks (No. arXiv: 2402.17733). arXiv. https://arxiv.org/abs/2402.17733
- Armengol-Estapรฉ, J., Carrino, C. P., Rodriguez-Penagos, C., de Gibert Bonet, O., Armentano-Oller, C., Gonzalez-Agirre, A., Melero, M., & Villegas, M. (2021). Are multilingual models the best choice for moderately under-resourced languages? A comprehensive assessment for Catalan. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 4933โ4946. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.437
- Currey, A., Nadejde, M., Pappagari, R. R., Mayer, M., Lauly, S., Niu, X., Hsu, B., & Dinu, G. (2022). MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 4287โ4299). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.288
- Federmann, C., Kocmi, T., & Xin, Y. (2022). NTREX-128 โ News test references for MT evaluation of 128 languages. Proceedings of the First Workshop on Scaling Up Multilingual Evaluation, 21โ24. Association for Computational Linguistics. https://aclanthology.org/2022.sumeval-1.4
- Ive, J., Specia, L., Szoc, S., Vanallemeersch, T., Van den Bogaert, J., Farah, E., Maroti, C., Ventura, A., & Khalilov, M. (2020). A post-editing dataset in the legal domain: Do we underestimate neural machine translation quality? In N. Calzolari, F. Bรฉchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 3692โ3697). European Language Resources Association. https://aclanthology.org/2020.lrec-1.455/
- Malmasi, S., Fang, A., Fetahu, B., Kar, S., & Rokhlenko, O. (2022). MultiCoNER: A large-scale multilingual dataset for complex named entity recognition. Proceedings of the 29th International Conference on Computational Linguistics, 3798โ3809. International Committee on Computational Linguistics. https://aclanthology.org/2022.coling-1.334/
- NLLB Team, Costa-jussร , M. R., Cross, J., รelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., Sun, A., Wang, S., Wenzek, G., Youngblood, A., Akula, B., Barrault, L., Mejia Gonzalez, G., Hansanti, P., Hoffman, J., Jarrett, S., Sadagopan, K. R., Rowe, D., Spruit, S., Tran, C., Andrews, P., Ayan, N. F., Bhosale, S., Edunov, S., Fan, A., Gao, C., Goswami, V., Guzmรกn, F., Koehn, P., Mourachko, A., Ropers, C., Saleem, S., Schwenk, H., & Wang, J. (2022). No language left behind: Scaling human-centered machine translation (No. arXiv: 2207.04672). arXiv. https://arxiv.org/abs/2207.04672
- Riley, P., Dozat, T., Botha, J. A., Garcia, X., Garrette, D., Riesa, J., Firat, O., & Constant, N. (2022). FRMT: A benchmark for few-shot region-aware machine translation (No. arXiv: 2210.00193). arXiv. https://doi.org/10.48550/ARXIV.2210.00193
- Specia, L., Harris, K., Blain, F., Burchardt, A., Macketanz, V., Skadiลa, I., Negri, M., & Turchi, M. (2017). Translation quality and productivity: A study on rich morphology languages. Proceedings of Machine Translation Summit XVI, 55โ71. Nagoya, Japan.
- Tiedemann, J. (2020). The Tatoeba translation challenge โ Realistic data sets for low-resource and multilingual MT. Proceedings of the Fifth Conference on Machine Translation, 1174โ1182. Association for Computational Linguistics. https://www.aclweb.org/anthology/2020.wmt-1.139
- Urbizu, G., San Vicente, I., Saralegi, X., Agerri, R., & Soroa, A. (2022). BasqueGLUE: A natural language understanding benchmark for Basque. Proceedings of the Language Resources and Evaluation Conference, 1603โ1612. European Language Resources Association. https://aclanthology.org/2022.lrec-1.172
- Yang, Y., Zhang, Y., Tar, C., & Baldridge, J. (2019). PAWS-X: A cross-lingual adversarial dataset for paraphrase identification. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3687โ3692). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1382
- Zubillaga, M., Sainz, O., Estarrona, A., Lopez de Lacalle, O., & Agirre, E. (2024). Event extraction in Basque: Typologically motivated cross-lingual transfer-learning analysis (No. arXiv: 2404.06392). arXiv. https://arxiv.org/abs/2404.06392
</details>
## Evaluation
Below are the evaluation results on the [Flores+200 devtest set](https://huggingface.co/datasets/openlanguagedata/flores_plus),
compared against the state-of-the-art [MADLAD400-7B-mt model](https://huggingface.co/google/madlad400-7b-mt) ([Kudugunta, S., et al.](https://arxiv.org/abs/2309.04662)) and SalamandraTA-7b-base model.
These results cover the translation directions CA-XX, ES-XX, EN-XX, as well as XX-CA, XX-ES, and XX-EN.
The metrics have been computed excluding Asturian, Aranese, and Aragonese, as we report them separately.
The evaluation was conducted using [MT-Lens](https://github.com/langtech-bsc/mt-evaluation), following the standard setting (beam search with beam size 5, limiting the translation length to 500 tokens). We report the following metrics:
<details>
<summary>Click to show metrics details</summary>
- `BLEU`: Sacrebleu implementation. Signature: nrefs:1โ case:mixedโ eff:noโ tok:13aโ smooth:expโversion:2.3.1
- `TER`: Sacrebleu implementation.
- `ChrF`: Sacrebleu implementation.
- `Comet`: Model checkpoint: "Unbabel/wmt22-comet-da".
- `Comet-kiwi`: Model checkpoint: "Unbabel/wmt22-cometkiwi-da".
- `Bleurt`: Model checkpoint: "lucadiliello/BLEURT-20".
- `MetricX`: Model checkpoint: "google/metricx-23-xl-v2p0".
- `MetricX-QE`: Model checkpoint: "google/metricx-23-qe-xl-v2p0".
</details>
<details>
<summary>English evaluation</summary>
### English
This section presents the evaluation metrics for English translation tasks.
| | Bleuโ | Terโ | ChrFโ | Cometโ | Comet-kiwiโ | Bleurtโ | MetricXโ | MetricX-QEโ |
|:---------------------------------|-------:|------:|-------:|--------:|-------------:|---------:|----------:|-------------:|
| **EN-XX** | | | | | | | | |
| SalamandraTA-7b-instruct | 35.20 | 53.40 | 61.58 | **0.89** | **0.86** | 0.78 | **0.96** | **0.81** |
| MADLAD400-7B | **35.73** | **51.87** | **63.46** | 0.88 | 0.85 | **0.79** | 1.16 | 1.10 |
| SalamandraTA-7b-base | 34.99 | 52.64 | 62.58 | 0.87 | 0.84 | 0.77 | 1.45 | 1.23 |
| **XX-EN** | | | | | | | | |
| SalamandraTA-7b-instruct | **44.37** | **42.49** | 68.29 | **0.89** | **0.86** | **0.80** | **1.05** | **0.99** |
| MADLAD400-7B | 43.20 | 43.33 | 67.98 | **0.89** | **0.86** | **0.80** | 1.13 | 1.15 |
| SalamandraTA-7b-base | 44.12 | 43.00 | **68.43** | **0.89** | 0.85 | **0.80** | 1.13 | 1.22 |
<img src="./images/bleu_en.png" alt="English" width="100%"/>
</details>
<details>
<summary>Spanish evaluation</summary>
### Spanish
This section presents the evaluation metrics for Spanish translation tasks.
| | Bleuโ | Terโ | ChrFโ | Cometโ | Comet-kiwiโ | Bleurtโ | MetricXโ | MetricX-QEโ |
|:---------------------------------|-------:|------:|-------:|--------:|-------------:|---------:|----------:|-------------:|
| **ES-XX** | | | | | | | | |
| SalamandraTA-7b-instruct | **23.68** | **67.31** | **53.98** | **0.87** | **0.83** | **0.76** | **0.93** | **0.80** |
| MADLAD400-7B | 22.48 | 68.91 | 53.93 | 0.86 | **0.83** | 0.75 | 1.09 | 1.14 |
| SalamandraTA-7b-base | 21.63 | 70.08 | 52.98 | 0.86 | **0.83** | 0.74 | 1.24 | 1.12 |
| **XX-ES** | | | | | | | | |
| SalamandraTA-7b-instruct | **26.40** | 62.27 | **53.54** | **0.85** | **0.84** | **0.74** | **0.80** | **1.07** |
| MADLAD400-7B | 24.85 | **61.82** | 53.00 | **0.85** | **0.84** | **0.74** | 1.05 | 1.50 |
| SalamandraTA-7b-base | 24.71 | 62.33 | 52.96 | **0.85** | **0.84** | 0.73 | 1.06 | 1.37 |
<img src="./images/bleu_es.png" alt="English" width="100%"/>
<img src="./images/es_xx_bars.png" alt="ESXX" width="100%"/>
</details>
<details>
<summary>Catalan evaluation</summary>
### Catalan
This section presents the evaluation metrics for Catalan translation tasks.
| | Bleuโ | Terโ | ChrFโ | Cometโ | Comet-kiwiโ | Bleurtโ | MetricXโ | MetricX-QEโ |
|:---------------------------------|-------:|------:|-------:|--------:|-------------:|---------:|----------:|-------------:|
| **CA-XX** | | | | | | | | |
| SalamandraTA-7b-instruct | **29.50** | 59.26 | 58.21 | **0.88** | **0.81** | **0.77** | **0.97** | **0.98** |
| MADLAD400-7B | 29.37 | **59.01** | **58.47** | 0.87 | **0.81** | **0.77** | 1.08 | 1.31 |
| SalamandraTA-7b-base | 29.06 | 59.32 | 58.00 | 0.87 | **0.81** | 0.76 | 1.23 | 1.28 |
| **XX-CA** | | | | | | | | |
| SalamandraTA-7b-instruct | **34.51** | **54.21** | **60.10** | **0.86** | **0.81** | **0.76** | **0.90** | **1.29** |
| MADLAD400-7B | 33.02 | 55.01 | 59.38 | **0.86** | **0.81** | 0.75 | 1.18 | 1.79 |
| SalamandraTA-7b-base | 32.75 | 55.78 | 59.42 | **0.86** | **0.81** | 0.75 | 1.17 | 1.63 |
<img src="./images/bleu_ca.png" alt="English" width="100%"/>
</details>
<details>
<summary>Galician evaluation</summary>
### Galician
This section presents the evaluation metrics for Galician translation tasks.
| | Bleuโ | Terโ | ChrFโ | Cometโ | Comet-kiwiโ | Bleurtโ | MetricXโ | MetricX-QEโ |
|:---------------------------------|-------:|------:|-------:|--------:|-------------:|---------:|----------:|-------------:|
| **GL-XX** | | | | | | | | |
| SalamandraTA-7b-instruct | **36.95** | **50.12** | **62.55** | **0.88** | **0.85** | **0.77** | **0.86** | **0.98** |
| MADLAD400-7B | 26.43 | 64.30 | 55.99 | 0.86 | **0.85** | 0.76 | 1.35 | 2.06 |
| SalamandraTA-7b-base | 27.47 | 61.39 | 56.96 | 0.87 | 0.82 | 0.76 | 1.23 | 1.29 |
| **XX-GL** | | | | | | | | |
| SalamandraTA-7b-instruct | **34.37** | **52.49** | **60.99** | **0.88** | **0.85** | **0.73** | **0.75** | **0.92** |
| MADLAD400-7B | 27.77 | 59.46 | 54.92 | 0.84 | **0.85** | 0.67 | 1.42 | 2.72 |
| SalamandraTA-7b-base | 28.22 | 59.52 | 56.28 | 0.85 | 0.82 | 0.69 | 1.27 | 1.78 |
<img src="./images/bleu_gl.png" alt="English" width="100%"/>
</details>
<details>
<summary>Basque evaluation</summary>
### Basque
This section presents the evaluation metrics for Basque translation tasks.
| | Bleuโ | Terโ | ChrFโ | Cometโ | Comet-kiwiโ | Bleurtโ | MetricXโ | MetricX-QEโ |
|:---------------------------------|-------:|------:|-------:|--------:|-------------:|---------:|----------:|-------------:|
| **EU-XX** | | | | | | | | |
| SalamandraTA-7b-instruct | **29.89** | **58.54** | **56.66** | **0.87** | **0.85** | **0.76** | **0.90** | **0.89** |
| MADLAD400-7B | 21.26 | 69.75 | 49.80 | 0.85 | 0.82 | 0.72 | 1.54 | 2.71 |
| SalamandraTA-7b-base | 22.87 | 67.38 | 52.19 | 0.86 | 0.79 | 0.74 | 1.19 | 1.61 |
| **XX-EU** | | | | | | | | |
| SalamandraTA-7b-instruct | **18.89** | **71.74** | **57.16** | **0.87** | **0.84** | **0.82** | **0.58** | **0.44** |
| MADLAD400-7B | 13.64 | 85.01 | 50.96 | 0.82 | 0.80 | 0.78 | 2.09 | 3.58 |
| SalamandraTA-7b-base | 17.01 | 75.92 | 55.22 | 0.85 | 0.77 | 0.80 | 1.04 | 1.17 |
<img src="./images/bleu_eu.png" alt="English" width="100%"/>
</details>
### Low-Resource Languages of Spain
The tables below summarize the performance metrics for English, Spanish, and Catalan to Asturian, Aranese and Aragonese compared
against [Transducens/IbRo-nllb](https://huggingface.co/Transducens/IbRo-nllb) [(Galiano Jimenez, et al.)](https://aclanthology.org/2024.wmt-1.85/),
[NLLB-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) ([Costa-jussร et al., 2022](https://arxiv.org/abs/2207.04672)) and [SalamandraTA-2B](https://huggingface.co/BSC-LT/salamandraTA-2B).
<details>
<summary>English evaluation</summary>
#### English-XX
| | source | target | Bleu โ | Ter โ | ChrF โ |
|:-------------------------|:---------|:---------|:----------|:----------|:----------|
| SalamandraTA-7b-instruct | en | ast | **31.79** | **54.07** | **61.78** |
| SalamandraTA-7b-base | en | ast | 26.40 | 64.02 | 57.35 |
| Transducens/IbRo-nllb | en | ast | 20.56 | 63.92 | 53.32 |
| | | | | | |
| SalamandraTA-7b-instruct | en | arn | **22.77** | **66.06** | **52.61** |
| SalamandraTA-7b-base | en | arn | 14.13 | 74.05 | 46.17 |
| Transducens/IbRo-nllb | en | arn | 12.81 | 73.21 | 45.76 |
| | | | | | |
| SalamandraTA-7b-instruct | en | arg | **19.74** | 71.58 | **51.08** |
| Transducens/IbRo-nllb | en | arg | 14.07 | **70.37** | 46.89 |
| SalamandraTA-7b-base | en | arg | 12.24 | 73.48 | 44.75 |
</details>
<details>
<summary>Spanish evaluation</summary>
#### Spanish-XX
| | source | target | Bleu โ | Ter โ | ChrF โ |
|:-------------------------|:---------|:---------|:----------|:----------|:----------|
| SalamandraTA-7b-instruct | es | ast | **20.66** | **71.81** | **53.14** |
| SalamandraTA-7b-base | es | ast | 17.65 | 75.78 | 51.05 |
| Transducens/IbRo-nllb | es | ast | 16.79 | 76.36 | 50.89 |
| | | | | | |
| SalamandraTA-7b-base | es | arn | **51.59** | **35.51** | **73.50** |
| Transducens/IbRo-nllb | es | arn | 50.20 | 36.60 | 73.16 |
| SalamandraTA-7b-instruct | es | arn | 47.37 | 39.29 | 70.65 |
| | | | | | |
| Transducens/IbRo-nllb | es | arg | **59.75** | **28.01** | **78.73** |
| SalamandraTA-7b-base | es | arg | 53.96 | 31.51 | 76.08 |
| SalamandraTA-7b-instruct | es | arg | 44.10 | 39.98 | 71.12 |
</details>
<details>
<summary>Catalan evaluation</summary>
#### Catalan-XX
| | source | target | Bleu โ | Ter โ | ChrF โ |
|:-------------------------|:---------|:---------|:----------|:----------|:----------|
| SalamandraTA-7b-instruct | ca | ast | **28.13** | **58.84** | **58.98** |
| SalamandraTA-7b-base | ca | ast | 26.11 | 63.63 | 58.08 |
| Transducens/IbRo-nllb | ca | ast | 24.77 | 61.60 | 57.49 |
| | | | | | |
| SalamandraTA-7b-base | ca | arn | **31.76** | **53.71** | **60.71** |
| Transducens/IbRo-nllb | ca | arn | 31.22 | 54.30 | 60.30 |
| SalamandraTA-7b-instruct | ca | arn | 30.89 | 54.70 | 59.78 |
| | | | | | |
| Transducens/IbRo-nllb | ca | arg | **24.44** | **60.79** | **55.51** |
| SalamandraTA-7b-base | ca | arg | 22.53 | 62.37 | 54.32 |
| SalamandraTA-7b-instruct | ca | arg | 20.96 | 65.64 | 52.41 |
</details>
### Gender Aware Translation
Below are the evaluation results for gender aware translation evaluated on the [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval?tab=readme-ov-file#mt-geneval)
dataset ([Currey, A. et al.](https://github.com/amazon-science/machine-translation-gender-eval?tab=readme-ov-file#mt-geneval)).
These have been calculated for translation from English into German, Spanish, French, Italian, Portuguese and Russian and are compared
against [MADLAD400-7B-mt](https://huggingface.co/google/madlad400-7b-mt), [TowerInstruct-7B-v0.2](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.2)
and the SalamandraTA-7b-base model.
Evaluation was conducted using [MT-Lens](https://github.com/langtech-bsc/mt-evaluation) and is reported as accuracy computed using the accuracy metric
provided with MT-GenEval.
<details>
| | Source | Target | Masc | Fem | Pair |
|:---------------------------------|:---------|:---------|-------:|-------:|-------:|
| SalamandraTA-7b-instruct | en | de | **0.883** | **0.883** | **0.773** |
| SalamandraTA-7b-base | en | de | 0.857 | 0.77 | 0.66 |
| MADLAD400-7B-mt | en | de | 0.877 | 0.823 | 0.713 |
| TowerInstruct-7B-v0.2 | en | de | 0.863 | 0.84 | 0.727 |
| | | | | | |
| SalamandraTA-7b-instruct | en | es | 0.867 | **0.85** | **0.737** |
| SalamandraTA-7b-base | en | es | **0.89** | 0.733 | 0.643 |
| MADLAD400-7B-mt | en | es | 0.887 | 0.78 | 0.687 |
| TowerInstruct-7B-v0.2 | en | es | 0.85 | 0.823 | 0.693 |
| | | | | | |
| SalamandraTA-7b-instruct | en | fr | **0.9** | 0.82 | **0.737** |
| SalamandraTA-7b-base | en | fr | 0.8867 | 0.71 | 0.617 |
| MADLAD400-7B-mt | en | fr | 0.873 | 0.777 | 0.663 |
| TowerInstruct-7B-v0.2 | en | fr | 0.88 | **0.823** | 0.717 |
| | | | | | |
| SalamandraTA-7b-instruct | en | it | 0.9 | **0.763** | 0.683 |
| SalamandraTA-7b-base | en | it | 0.893 | 0.593 | 0.513 |
| MADLAD400-7B-mt | en | it | 0.907 | 0.663 | 0.597 |
| TowerInstruct-7B-v0.2 | en | it | **0.947** | 0.747 | **0.713** |
| | | | | | |
| SalamandraTA-7b-instruct | en | pt | 0.92 | **0.77** | **0.707** |
| SalamandraTA-7b-base | en | pt | **0.923** | 0.65 | 0.597 |
| MADLAD400-7B-mt | en | pt | **0.923** | 0.687 | 0.627 |
| TowerInstruct-7B-v0.2 | en | pt | 0.907 | 0.73 | 0.67 |
| | | | | | |
| SalamandraTA-7b-instruct | en | ru | **0.95** | **0.837** | **0.793** |
| SalamandraTA-7b-base | en | ru | 0.933 | 0.713 | 0.653 |
| MADLAD400-7B-mt | en | ru | 0.94 | 0.797 | 0.74 |
| TowerInstruct-7B-v0.2 | en | ru | 0.933 | 0.797 | 0.733 |
<img src="./images/geneval.png"/>
</details>
## Ethical Considerations and Limitations
Detailed information on the work done to examine the presence of unwanted social and cognitive biases in the base model can be found
at [Salamandra-7B model card](https://huggingface.co/BSC-LT/salamandra-7b).
With regard to MT models, the only analysis related to bias which we have conducted is the MT-GenEval evaluation.
No specific analysis has yet been carried out in order to evaluate potential biases or limitations in translation
accuracy across different languages, dialects, or domains. However, we recognize the importance of identifying and addressing any harmful stereotypes,
cultural inaccuracies, or systematic performance discrepancies that may arise in Machine Translation. As such, we plan to continue performing more analyses
as we implement the necessary metrics and methods within our evaluation framework [MT-Lens](https://github.com/langtech-bsc/mt-evaluation).
Note that the model has only undergone preliminary instruction tuning.
We urge developers to consider potential limitations and conduct safety testing and tuning tailored to their specific applications.
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2025 by Language Technologies Unit, Barcelona Supercomputing Center.
### Funding
This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/).
This work is funded by the _Ministerio para la Transformaciรณn Digital y de la Funciรณn Pรบblica_ - Funded by EU โ NextGenerationEU
within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
### Acknowledgements
The success of this project has been made possible thanks to the invaluable contributions of our partners in the [ILENIA Project](https://proyectoilenia.es/):
[HiTZ](http://hitz.ehu.eus/es), and [CiTIUS](https://citius.gal/es/).
Their efforts have been instrumental in advancing our work, and we sincerely appreciate their help and support.
### Disclaimer
### Disclaimer
Be aware that the model may contain biases or other unintended distortions.
When third parties deploy systems or provide services based on this model, or use the model themselves,
they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations,
including those governing the use of Artificial Intelligence.
The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Citation
If you find our model useful, we would appreciate if you could cite our work as follows:
```
@article{title={SalamandraTA: A European Multilingual Large Language Model for Translation-Related Tasks},
author={Javier Garcรญa Gilabert, Carlos Escolano, Audrey Mash, Xixian Liao, Francesca De Luca Fornaciari, Miguel Claramunt
Argote, Ella Bohman and Maite Melero},
organization={Barcelona Supercomputing Center},
year={2025},
url={https://huggingface.co/BSC-LT/salamandraTA-7b-instruct}
}
```
|
GabrielMM/Math_SFT_v2_9epoch | GabrielMM | 2025-05-30T07:41:54Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T07:41:17Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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tonglaovn/llama3_8B_finetuned_sport_tva | tonglaovn | 2025-05-30T07:28:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-30T07:26:52Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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]
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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reesu/Finetunedgemma-3 | reesu | 2025-05-30T07:18:04Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-30T06:02:23Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** reesu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
pot99rta/BMO-CaptianMaid-12B | pot99rta | 2025-05-30T07:15:55Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:Nitral-AI/Captain_BMO-12B",
"base_model:merge:Nitral-AI/Captain_BMO-12B",
"base_model:pot99rta/CaptainMaid-12B-VioletMell-V0.420",
"base_model:merge:pot99rta/CaptainMaid-12B-VioletMell-V0.420",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T20:31:19Z | ---
base_model:
- Nitral-AI/Captain_BMO-12B
- pot99rta/CaptainMaid-12B-VioletMell-V0.420
library_name: transformers
tags:
- mergekit
- merge
---
# BMO-CaptianMaid-12B
```Models Merged:```
```1. Nitral-AI/Captain_BMO-12B```
```2. pot99rta/CaptainMaid-12B-VioletMell-V0.420```
```Preset:```
```Use ChatML or Mistral - Phi works too for some unknown reason.```
Phi and Mistral works with interesting results.. I quite like it with my settings.
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [pot99rta/CaptainMaid-12B-VioletMell-V0.420](https://huggingface.co/pot99rta/CaptainMaid-12B-VioletMell-V0.420) as a base.
### Models Merged
The following models were included in the merge:
* [Nitral-AI/Captain_BMO-12B](https://huggingface.co/Nitral-AI/Captain_BMO-12B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: pot99rta/CaptainMaid-12B-VioletMell-V0.420
#no parameters necessary for base model
- model: pot99rta/CaptainMaid-12B-VioletMell-V0.420
parameters:
density: 0.5
weight: 0.5
- model: Nitral-AI/Captain_BMO-12B
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: pot99rta/CaptainMaid-12B-VioletMell-V0.420
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
tuantranmlv/contractbert_thuenha_mucdichsudung_bin_v1 | tuantranmlv | 2025-05-30T06:56:32Z | 1,948 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-09T12:17:43Z | ---
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 ๐ค 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[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] |
Zhihu-ai/Zhi-Create-DSR1-14B | Zhihu-ai | 2025-05-30T06:54:56Z | 167 | 17 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"zh",
"en",
"dataset:Congliu/Chinese-DeepSeek-R1-Distill-data-110k",
"dataset:cognitivecomputations/dolphin-r1",
"dataset:open-thoughts/OpenThoughts-114k",
"dataset:qihoo360/Light-R1-SFTData",
"dataset:qihoo360/Light-R1-DPOData",
"arxiv:2406.18629",
"arxiv:2402.13228",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-19T02:23:41Z | ---
license: apache-2.0
datasets:
- Congliu/Chinese-DeepSeek-R1-Distill-data-110k
- cognitivecomputations/dolphin-r1
- open-thoughts/OpenThoughts-114k
- qihoo360/Light-R1-SFTData
- qihoo360/Light-R1-DPOData
language:
- zh
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
tags:
- qwen2
library_name: transformers
---
# Zhi-Create-DSR1-14B
## 1. Introduction
Zhi-Create-DSR1-14B is a fine-tuned model based on DeepSeek-R1-Distill-Qwen-14B, specifically optimized for enhanced creative writing capabilities. Several benchmark evaluations indicate the model's improved creative writing performance.
In the [LLM Creative Story-Writing Benchmark](https://github.com/lechmazur/writing), the model achieved a score of **8.33** compared to its base model's **7.8**. In the [WritingBench](https://github.com/X-PLUG/WritingBench) evaluation framework, it scored **8.46**, showing improvement over DeepSeek-R1-Distill-Qwen-14B's **7.93**. The model was also evaluated using GPT-4o on the AlpacaEval dataset, achieving an **82.6%** win rate when compared with the base model.
The figure below shows the performance comparison across different domains in WritingBench:
<figcaption style="text-align:center; font-size:0.9em; color:#666">
Figure 1: WritingBench performance of Zhi-Create-DSR1-14B and DeepSeek-R1-Distill-Qwen-14B across 6 domains and 3 writing requirements evaluated with WritingBench critic model (scale: 1-10). The six domains include: (D1) Academic & Engineering, (D2) Finance & Business, (D3) Politics & Law, (D4) Literature & Art, (D5) Education, and (D6) Advertising & Marketing. The three writing requirements assessed are: (R1) Style, (R2) Format, and (R3) Length. Here, "C" indicates category-specific scores.
</figcaption>
## 2. Training Process
### Data
The model's training corpus comprises three primary data sources: rigorously filtered open-source datasets, chain-of-thought reasoning corpora, and curated question-answer pairs from Zhihu.
To achieve optimal domain coverage, we meticulously balanced the distribution of various datasets, including [Dolphin-r1](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1), [Congliu/Chinese-DeepSeek-R1-Distill-data-110k](https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k), [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k), [Light-R1-SFTData](https://huggingface.co/datasets/qihoo360/Light-R1-SFTData), and [Light-R1-DPOData](https://huggingface.co/datasets/qihoo360/Light-R1-DPOData), alongside high-quality content from Zhihu. All datasets underwent comprehensive quality assurance through our Reward Model (RM) filtering pipeline.
### Training
**Supervised Fine-tuning (SFT)**: We employed a curriculum learning strategy for supervised fine-tuning. This methodical approach systematically enhances creative writing capabilities while incorporating diverse domain data to maintain core competencies and mitigate catastrophic forgetting.
**Direct Preference Optimization (DPO)**: For scenarios involving minimal edit distances, we utilized Step-DPO ([arxiv:2406.18629](https://arxiv.org/abs/2406.18629)) to selectively penalize incorrect tokens, while incorporating positive constraints in the loss function as proposed in DPOP ([arXiv:2402.13228](https://arxiv.org/abs/2402.13228)).
## 3. Evaluation Results
Our evaluation results suggest promising improvements in the model's creative writing capabilities. In the LLM Creative Story-Writing Benchmark evaluation, the model achieved a score of **8.33**, showing an improvement from the base model's **7.87**. When assessed on WritingBench, a comprehensive framework for evaluating large language model writing abilities, the model attained a score of **8.46**. This places it in proximity to DeepSeek-R1's performance and represents an advancement over DeepSeek-R1-Distill-Qwen-14B's score of **7.93**.
With respect to general capabilities, evaluations indicate modest improvements of **2%โ5% in knowledge and reasoning tasks (CMMLU, MMLU-Pro)**, alongside encouraging progress in mathematical reasoning as measured by benchmarks such as **AIME-2024, AIME-2025, and GSM8K**. The results suggest that the model maintains a balanced performance profile, with improvements observed across creative writing, knowledge/reasoning, and mathematical tasks compared to DeepSeek-R1-Distill-Qwen-14B. These characteristics potentially make it suitable for a range of general-purpose applications. We conducted additional evaluations on the instruction-following ifeval benchmark, with experimental results demonstrating a performance improvement in model capabilities from an initial score of **71.43** to an enhanced score of **74.71**.
<figcaption style="text-align:center; font-size:0.9em; color:#666">
Figure 2: When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use n=16 and max_tokens=32768 for mathematical tasks and n=2 for others)
</figcaption>
## 4. How to Run Locally
Zhi-Create-DSR1-14B can be deployed on various hardware configurations, including GPUs with 80GB memory, a single H20/A800/H800, or dual RTX 4090. Additionally, the INT4 quantized version Zhi-Create-DSR1-14B-GPTQ-INT4 can be deployed on a single RTX 4090.
### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
MODEL_NAME = "Zhihu-ai/Zhi-Create-DSR1-14B"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
# use bf16
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, bf16=True).eval()
# use fp16
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, fp16=True).eval()
# use cpu only
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cpu", trust_remote_code=True).eval()
# use auto mode, automatically select precision based on the device.
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="auto",
trust_remote_code=True
).eval()
# Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this.
# model.generation_config = GenerationConfig.from_pretrained(MODEL_NAME, trust_remote_code=True)
generate_configs = {
"temperature": 0.6,
"do_sample": True,
"top_p": 0.95,
"max_new_tokens": 4096
}
prompt = "่ฏทไฝ ไปฅ้ฒ่ฟ
็ๅฃๅป๏ผๅไธ็ฏไป็ป่ฅฟๆน้้ฑผ็ๆ็ซ "
messages = [
{"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,
**generate_configs
)
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]
print(response)
```
### ZhiLight
You can easily start a service using [ZhiLight](https://github.com/zhihu/ZhiLight)
```bash
docker run -it --net=host --gpus='"device=0"' -v /path/to/model:/mnt/models --entrypoints="" ghcr.io/zhihu/zhilight/zhilight:0.4.17-cu124 python -m zhilight.server.openai.entrypoints.api_server --model-path /mnt/models --port 8000 --enable-reasoning --reasoning-parser deepseek-r1 --served-model-name Zhi-Create-DSR1-14B
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Zhi-Create-DSR1-14B",
"prompt": "่ฏทไฝ ไปฅ้ฒ่ฟ
็ๅฃๅป๏ผๅไธ็ฏไป็ป่ฅฟๆน้้ฑผ็ๆ็ซ ",
"max_tokens": 4096,
"temperature": 0.6,
"top_p": 0.95
}'
```
### vllm
For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm)
```bash
# install vllm
pip install vllm>=0.6.4.post1
# huggingface model id
vllm serve Zhihu-ai/Zhi-Create-DSR1-14B --served-model-name Zhi-Create-DSR1-14B --port 8000
# local path
vllm serve /path/to/model --served-model-name Zhi-Create-DSR1-14B --port 8000
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Zhi-Create-DSR1-14B",
"prompt": "่ฏทไฝ ไปฅ้ฒ่ฟ
็ๅฃๅป๏ผๅไธ็ฏไป็ป่ฅฟๆน้้ฑผ็ๆ็ซ ",
"max_tokens": 4096,
"temperature": 0.6,
"top_p": 0.95
}'
```
### SGLang
You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang)
```bash
# install SGLang
pip install "sglang[all]>=0.4.5" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
# huggingface model id
python -m sglang.launch_server --model-path Zhihu-ai/Zhi-Create-DSR1-14B --served-model-name Zhi-Create-DSR1-14B --port 8000
# local path
python -m sglang.launch_server --model-path /path/to/model --served-model-name Zhi-Create-DSR1-14B --port 8000
# send request
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Zhi-Create-DSR1-14B",
"prompt": "่ฏทไฝ ไปฅ้ฒ่ฟ
็ๅฃๅป๏ผๅไธ็ฏไป็ป่ฅฟๆน้้ฑผ็ๆ็ซ ",
"max_tokens": 4096,
"temperature": 0.6,
"top_p": 0.95
}'
```
### ollama
You can download ollama using [this](https://ollama.com/download/)
* quantization: Q4_K_M
```bash
ollama run zhihu/zhi-create-dsr1-14b
```
* bf16
```bash
ollama run zhihu/zhi-create-dsr1-14b:bf16
```
## 5. Usage Recommendations
We recommend adhering to the following configurations when utilizing the Zhi-Create-DSR1-14B, including benchmarking, to achieve the expected performance:
* Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
* When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use `n=16` and `max_tokens=32768` for mathematical tasks and `n=2` for others)
* To ensure that the model engages in thorough reasoning like DeepSeek-R1 series models, we recommend enforcing the model to initiate its response with "\<think\>\n" at the beginning of every output.
## 6. Citation
```text
@misc{Zhi-Create-DSR1-14B,
title={Zhi-Create-DSR1-14B: Curriculum Reinforcement and Direct Preference Optimization for Robust Creative Writing in LLMs},
author={Jiewu Wang, Xu Chen, Wenyuan Su, Chao Huang, Hongkui Gao, Lin Feng, Shan Wang, Lu Xu, Penghe Liu, Zebin Ou},
year={2025},
eprint={},
archivePrefix={},
url={https://huggingface.co/Zhihu-ai/Zhi-Create-DSR1-14B},
}
```
## 7. Contact
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
|
BootesVoid/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8 | BootesVoid | 2025-05-30T06:45:19Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-30T06:45:18Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: kรฉlyah_
---
# Cmbae48860P391B1Y532Qmfid_Cmbae9Tjy001Ahy17I2N06Jj8
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `kรฉlyah_` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "kรฉlyah_",
"lora_weights": "https://huggingface.co/BootesVoid/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8', weight_name='lora.safetensors')
image = pipeline('kรฉlyah_').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbae48860p391b1y532qmfid_cmbae9tjy001ahy17i2n06jj8/discussions) to add images that show off what youโve made with this LoRA.
|
ChengzhiMu/distilhubert-finetuned-gtzan | ChengzhiMu | 2025-05-30T06:45:17Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2025-05-30T02:12:57Z | ---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.85
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5847
- Accuracy: 0.85
## 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: 8
- seed: 42
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.991 | 1.0 | 113 | 1.8920 | 0.54 |
| 1.205 | 2.0 | 226 | 1.2402 | 0.63 |
| 0.989 | 3.0 | 339 | 1.0598 | 0.68 |
| 0.6359 | 4.0 | 452 | 0.7967 | 0.74 |
| 0.5349 | 5.0 | 565 | 0.6752 | 0.8 |
| 0.3069 | 6.0 | 678 | 0.6000 | 0.8 |
| 0.3031 | 7.0 | 791 | 0.5846 | 0.83 |
| 0.1411 | 8.0 | 904 | 0.5506 | 0.82 |
| 0.1362 | 9.0 | 1017 | 0.5692 | 0.85 |
| 0.0767 | 10.0 | 1130 | 0.5847 | 0.85 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
2yunadaaa/qwen3-4b-3kingdoms-augmentedr3 | 2yunadaaa | 2025-05-30T06:42:39Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T06:39:59Z | ---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** 2yunadaaa
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 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)
|
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7 | AmberYifan | 2025-05-30T06:41:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T06:20:37Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.5-lr1e-7", 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/yifanwang/huggingface/runs/hbsctnfy)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Yuanze/Olympus | Yuanze | 2025-05-30T06:25:28Z | 47 | 2 | transformers | [
"transformers",
"safetensors",
"mipha_phi",
"text-generation",
"llm",
"lmm",
"conversational",
"olympus",
"llava",
"vision-language",
"image-text-to-text",
"en",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"dataset:Yuanze/Olympus",
"arxiv:2412.09612",
"base_model:zhumj34/Mipha-3B",
"base_model:finetune:zhumj34/Mipha-3B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-12-13T00:32:05Z | ---
license: apache-2.0
datasets:
- liuhaotian/LLaVA-Instruct-150K
- Yuanze/Olympus
language:
- en
base_model:
- zhumj34/Mipha-3B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- transformers
- llm
- lmm
- conversational
- olympus
- llava
- text-generation
- vision-language
---
<p align="center">
<img src="https://github.com/yuanze-lin/Olympus/blob/main/asset/olympus.png?raw=true" alt="icon" width="150" height="150" style="vertical-align:middle; margin-right:5px;" />
</p>
# Olympus: A Universal Task Router for Computer Vision Tasks <br> (CVPR 2025, <font color="red">Highlight</font>) <br />
[](https://arxiv.org/pdf/2412.09612)
[](https://arxiv.org/pdf/2412.09612)
[](https://yuanze-lin.me/Olympus_page/)
[](https://huggingface.co/Yuanze/Olympus)
[](https://huggingface.co/datasets/Yuanze/Olympus)
[](https://github.com/yuanze-lin/Olympus)
[](https://www.youtube.com/watch?v=N1xOdIrVvn4)
Official implementation of "Olympus: A Universal Task Router for Computer Vision Tasks"
โฅ๏ธ If you find our project is helpful for your research, please kindly give us a ๐ on https://github.com/yuanze-lin/Olympus and cite our paper ๐
## ๐ฃ News
- [ ] Release the code for integration with task-specific models.
- [x] Release the training & inference code.
- [x] Release Olympus datasets.
- [x] Release the model of Olympus.
## ๐
Overview
<p align="center">
<img src="https://github.com/yuanze-lin/Olympus/blob/main/asset/overview.png?raw=true" alt="Overview" width="1000"/>
</p>
## Getting Started
### ๐ ๏ธ Environment Installation
To establish the environment, just run this code in the shell:
```
git clone https://github.com/yuanze-lin/Olympus.git
cd Olympus
conda create -n olympus python==3.10 -y
conda activate olympus
pip install -r requirements.txt
```
That will create the environment ```olympus``` we used.
### Download Models & Data ###
We share our collected Olympus dataset as follows:
| Instruction | Link |
|---------|------|
| Olympus Task-wise Data | [Olympus_20tasks_all](https://drive.google.com/drive/folders/1m3FYHarVG8eg7X7cMAC5N5NBG-p0ymw8?usp=drive_link) |
| Olympus Fine-tuning Data | [Olympus.json](https://drive.google.com/file/d/1CMLZLa6hkVN2K1ebCcJEOaFGc2cLeLQ7/view?usp=sharing) |
- ```Olympus_20tasks_all```: There are 20 JSON files under ```20 individual tasks``` folder, each corresponding to a specific task. You can refer to the routing token definitions in our paper to identify the task associated with each JSON file, along with the chain-of-action data provided in ```coa.json```. Each of these 21 JSON files includes both training and test data.
- ```Olympus.json```: The final fine-tuning data.
(1) Download the Olympus model:
```
python download_olympus.py
```
It will save the ```Olympus``` model under the ```ckpts``` folder.
(2) Download the Olympus data for fine-tuning:
```
python download_olympus_json.py
```
The json data will be saved as ```Olympus.json``` in the ```train_data``` folder. Note that ```Olympus.json``` includes ```llava_v1_5_mix665k.json``` combined with our collected data from 20 tasks.
**If you want to merge the data manually, firstly create ```jsons``` folder by ```mkdir jsons```, download all the JSON files from [Olympus_20tasks_all](https://drive.google.com/drive/folders/1m3FYHarVG8eg7X7cMAC5N5NBG-p0ymw8?usp=drive_link) and [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json) into the ```jsons``` folder, then run the merge script:**
```
python scripts/merge_data.py
```
(3) Download the Mipha-3B model for fine-tuning:
```
python download_mipha_3b.py
```
It will save the ```Mipha-3B``` model under the ```ckpts``` folder.
### Inference
Run the following code for inference:
```
model_name=Olympus
MODELDIR=ckpts/$model_name
python predict.py \
--prompt "Generate an image of a fluffy orange cat lounging on a windowsill, \
with sunlight streaming through the glass and casting soft shadows to create a cozy atmosphere. \
Next, would it be possible to change the cat's color to white? This change will make it more eye-catching. \
In the following step, produce a high-resolution 3D model based on the modified image. \
At the next point, please show a video of a cat and a dog running on a playground." \
--model-path $MODELDIR \
--temperature 0 \
--conv-mode v0
```
Alternatively, you can run ```bash predict.sh``` as we did.
The prediction should be like:
```
Input Prompt: Generate an image of a fluffy orange cat lounging on a windowsill,
with sunlight streaming through the glass and casting soft shadows to create a cozy atmosphere.
Next, would it be possible to change the cat's color to white? This change will make it more eye-catching.
In the following step, produce a high-resolution 3D model based on the modified image.
At the next point, please show a video of a cat and a dog running on a playground.
Output: <image_gen>a fluffy orange cat lounging on a windowsill, with sunlight streaming
through the glass and casting soft shadows to create a cozy atmosphere.</image_gen>
<image_edit>change the cat's color to white.</image_edit>
<3D_gen_image>produce a high-resolution 3D model based on the modified image.</3D_gen_image>
<video_gen>a cat and a dog running on a playground.</video_gen>
```
Change the ```--prompt``` to customize the input prompt as needed.
### Visual Instruction Tuning
Please refer [here](https://github.com/haotian-liu/LLaVA/blob/9a26bd1435b4ac42c282757f2c16d34226575e96/README.md#visual-instruction-tuning) to prepare the instruction tuning data. Especially, store the images from different datasets under ```train_data``` folder.
Run the following code to fine-tune the model:
```
bash scripts/mipha/finetune.sh
```
### Evaluation
To evaluate the model's performance on different benchmarks:
See [Evaluation.md](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md).
Please place the evaluation data under the ```eval``` folder. The evaluation scripts are placed under ```scripts/mipha/eval/```.
For example, to test the model's performance on VQAv2 dataset, simply run:
```
bash scripts/mipha/eval/vqav2.sh
```
## ๐ฎ Suppored Capacities (Covering 20 tasks)
<p align="center">
<img src="https://github.com/yuanze-lin/Olympus/blob/main/asset/capacities.png?raw=true" alt="Capacity" width="1000" height="100"/>
</p>
## ๐ Diverse Applications
<p align="center">
<img src="https://github.com/yuanze-lin/Olympus/blob/main/asset/application.png?raw=true" alt="Capacity" width="1000" height="100"/>
</p>
## Citation
If you find Olympus useful for your research and applications, please cite using this BibTeX:
```
@article{lin2024olympus,
title={Olympus: A Universal Task Router for Computer Vision Tasks},
author={Lin, Yuanze and Li, Yunsheng and Chen, Dongdong and Xu, Weijian and Clark, Ronald and Torr, Philip HS},
journal={arXiv preprint arXiv:2412.09612},
year={2024}
}
```
## Acknowledgement
Our project is built upon the following foundations:
- [Mipha](https://github.com/xmoanvaf/llava-phi): An impressive open-source project for lightweight vision-language assistants
- [LLaVA](https://github.com/haotian-liu/LLaVA): A powerful open-source vision-language assistant project |
nomiooogg/tinyllama-fake-news-detector | nomiooogg | 2025-05-30T06:20:44Z | 16 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2025-05-19T03:01:29Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
---
# 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 0.15.2 |
mradermacher/gemma-3-finetune-GGUF | mradermacher | 2025-05-30T06:00:12Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3",
"en",
"base_model:loremi/gemma-3-finetune",
"base_model:quantized:loremi/gemma-3-finetune",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-30T03:42:21Z | ---
base_model: loremi/gemma-3-finetune
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/loremi/gemma-3-finetune
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-3-finetune-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/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q6_K.gguf) | Q6_K | 3.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-finetune-GGUF/resolve/main/gemma-3-finetune.f16.gguf) | f16 | 7.9 | 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 -->
|
vertings6/9c7d6b9a-7b65-4041-9002-d8b2e35aade4 | vertings6 | 2025-05-30T05:57:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:adapter:lmsys/vicuna-7b-v1.5",
"license:llama2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-30T04:40:29Z | ---
library_name: peft
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9c7d6b9a-7b65-4041-9002-d8b2e35aade4
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
absolute_data_files: false
adapter: lora
base_model: lmsys/vicuna-7b-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 1b4a1e767cffc7ad_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: 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: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/9c7d6b9a-7b65-4041-9002-d8b2e35aade4
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/1b4a1e767cffc7ad_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: de8f08f2-51a3-4255-a53d-b410c9ad1c6c
wandb_project: s56-7
wandb_run: your_name
wandb_runid: de8f08f2-51a3-4255-a53d-b410c9ad1c6c
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 9c7d6b9a-7b65-4041-9002-d8b2e35aade4
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0667
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- 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: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3214 | 0.0003 | 1 | 1.2091 |
| 1.0468 | 0.0657 | 250 | 1.0798 |
| 1.0333 | 0.1313 | 500 | 1.0667 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
danfu3000/DISC-FinLLM-Q4_K_M-GGUF | danfu3000 | 2025-05-30T05:53:40Z | 0 | 0 | null | [
"gguf",
"finance",
"llama-cpp",
"gguf-my-repo",
"zh",
"base_model:Go4miii/DISC-FinLLM",
"base_model:quantized:Go4miii/DISC-FinLLM",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T05:52:44Z | ---
license: apache-2.0
language:
- zh
tags:
- finance
- llama-cpp
- gguf-my-repo
base_model: Go4miii/DISC-FinLLM
---
# danfu3000/DISC-FinLLM-Q4_K_M-GGUF
This model was converted to GGUF format from [`Go4miii/DISC-FinLLM`](https://huggingface.co/Go4miii/DISC-FinLLM) 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/Go4miii/DISC-FinLLM) 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 danfu3000/DISC-FinLLM-Q4_K_M-GGUF --hf-file disc-finllm-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo danfu3000/DISC-FinLLM-Q4_K_M-GGUF --hf-file disc-finllm-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 danfu3000/DISC-FinLLM-Q4_K_M-GGUF --hf-file disc-finllm-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo danfu3000/DISC-FinLLM-Q4_K_M-GGUF --hf-file disc-finllm-q4_k_m.gguf -c 2048
```
|
deepmaster/Template2 | deepmaster | 2025-05-30T05:35:48Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-30T05:33:26Z | # Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928โ2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351โ2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873โ4877.
|
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7 | AmberYifan | 2025-05-30T05:13:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T04:53:09Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-beta0.6-lr5e-7", 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/yifanwang/huggingface/runs/vyec931i)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
huyhuung/qwen_SFT_FFT_v2 | huyhuung | 2025-05-30T05:04:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T05:04:07Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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] |
Hectore/80s_commercial_screenshot | Hectore | 2025-05-30T04:44:29Z | 106 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] | text-to-image | 2025-05-26T10:27:37Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/80S_COMMERCIALS_SCREENSHOT_e000001_00_20250524094529.png
- text: '-'
output:
url: images/80S_COMMERCIALS_SCREENSHOT_e000006_02_20250524124152.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: 80scommercial
license: mit
---
# 80S COMMERCIAL SCREENSHOT
<Gallery />
## Model description
LoRA for Flux designed to generate screenshots inspired by 1980s commercials. Features low-resolution images, retro graphics, overlaid text, and color palettes typical of the TV era. Ideal for creating nostalgic, vintage broadcast-style scenes.
## Trigger words
You should use `80scommercial` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Hectore/80s_commercial_screenshot/tree/main) them in the Files & versions tab.
|
llm-jp/llm-jp-3.1-13b | llm-jp | 2025-05-30T04:37:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2025-05-23T05:30:26Z | ---
license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
library_name: transformers
inference: false
---
# llm-jp-3.1-13b
LLM-jp-3.1 is a series of large language models developed by the [Research and Development Center for Large Language Models](https://llmc.nii.ac.jp/) at the [National Institute of Informatics](https://www.nii.ac.jp/en/).
Building upon the LLM-jp-3 series, the LLM-jp-3.1 models incorporate mid-training ([instruction pre-training](https://aclanthology.org/2024.emnlp-main.148/)), which significantly enhances their instruction-following capabilities compared to the original LLM-jp-3 models.
This repository provides the **llm-jp-3.1-13b** model.
For an overview of the LLM-jp-3.1 models across different parameter sizes, please refer to:
- [LLM-jp-3.1 Pre-trained Models](https://huggingface.co/collections/llm-jp/llm-jp-31-pre-trained-models-68368787c32e462c40a45f7b)
- [LLM-jp-3.1 Fine-tuned Models](https://huggingface.co/collections/llm-jp/llm-jp-31-fine-tuned-models-68368681b9b35de1c4ac8de4).
For more details on the training procedures and evaluation results, please refer to [this blog post](https://llm-jp.nii.ac.jp/ja/blog/blog-887/) (in Japanese).
Checkpoints format: Hugging Face Transformers
## Required Libraries and Their Versions
- torch>=2.3.0
- transformers>=4.40.1
- tokenizers>=0.19.1
- accelerate>=0.29.3
- flash-attn>=2.5.8
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/llm-jp-3.1-13b")
model = AutoModelForCausalLM.from_pretrained("llm-jp/llm-jp-3.1-13b", device_map="auto", torch_dtype=torch.bfloat16)
text = "่ช็ถ่จ่ชๅฆ็ใจใฏไฝใ"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
tokenized_input,
max_new_tokens=100,
do_sample=True,
top_p=0.95,
temperature=0.7,
repetition_penalty=1.05,
)[0]
print(tokenizer.decode(output))
```
## Model Details
- **Model type:** Transformer-based Language Model
- **Architectures:**
Dense model:
|Params|Layers|Hidden size|Heads|Context length|Embedding parameters|Non-embedding parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|1.8b|24|2048|16|4096|407,498,752|1,459,718,144|
|13b|40|5120|40|4096|1,018,746,880|12,688,184,320|
MoE model:
|Params|Layers|Hidden size|Heads|Routed Experts|Activated Experts|Context length|Embedding parameters|Non-embedding parameters|Activated parameters|Total parameters|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|8x13b|40|5120|40|8|2|4096|1,018,746,880|72,144,081,920|22,200,806,400|73,162,828,800|
## Tokenizer
The tokenizer of this model is based on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) Unigram byte-fallback model.
The vocabulary entries were converted from [`llm-jp-tokenizer v3.0`](https://github.com/llm-jp/llm-jp-tokenizer/releases/tag/v3.0b2).
Please refer to [README.md](https://github.com/llm-jp/llm-jp-tokenizer) of `llm-jp-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary).
## Datasets
### Pre-training
The models have been pre-trained using a blend of the following datasets.
| Language | Dataset | Tokens|
|:---|:---|---:|
|Japanese|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.6B
||[Common Crawl](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|762.8B
||[WARP/PDF](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|237.3B
||[WARP/HTML](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|2.7B
||[Kaken](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|1.8B
|English|[Wikipedia](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|4.7B
||[Dolma/CC-head](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|608.5B
||[Dolma/C4](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|181.6B
||[Dolma/Reddit](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|83.1B
||[Dolma/PeS2o](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|62.9B
||[Dolma/Gutenberg](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|5.5B
||[Dolma/Wiki](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)|3.9B
|Code|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|114.1B
|Chinese|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.8B
|Korean|[Wikipedia](https://huggingface.co/datasets/bigcode/the-stack)|0.3B
### Mid-training
In the LLM-jp-3.1 series, we performed continuous pre-training based on [Instruction Pre-Training](https://aclanthology.org/2024.emnlp-main.148/).
Instruction Pre-Training enhances a modelโs ability to follow instructions by continuing pre-training on a large collection of instructionโresponse pairs.
We prepared approximately 90B tokens of instructionโresponse data and mixed it with our pre-training datasets, conducting continuous pre-training on a total of 400B tokens.
Each model was initialized from existing checkpoints ([llm-jp/llm-jp-3-1.8b](https://huggingface.co/llm-jp/llm-jp-3-1.8b), [llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b), and [llm-jp/llm-jp-3-8x13b](https://huggingface.co/llm-jp/llm-jp-3-8x13b)) and underwent continuous instruction pre-training.
Since the LLM-jp-3 series was originally pre-trained on 2.1T tokens, the total pre-training token count amounts to 2.5T tokens.
Details of this training process will be released in a forthcoming paper. The instructionโresponse dataset used for this training will also be made publicly available.
### Post-training
We have fine-tuned the pre-trained checkpoint with supervised fine-tuning and further aligned it with Direct Preference Optimization.
#### Supervised Fine-tuning
The datasets used for supervised fine-tuning are as follows:
| Language | Dataset | Description |
|:---|:---|:---|
|Japanese|[ichikara-instruction-004-002](https://liat-aip.sakura.ne.jp/wp/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf%e4%bd%9c%e6%88%90/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf-%e5%85%ac%e9%96%8b/)| A manually constructed instruction dataset. |
| |[AnswerCarefully (ver2.0)](https://huggingface.co/datasets/llm-jp/AnswerCarefully)| A manually constructed instruction dataset focusing on LLMs' safety. |
| |ichikara-instruction-format| A small subset of the ichikara-instruction dataset, edited with some constraints on the output format. |
| |[AutoMultiTurnByCalm3-22B](https://huggingface.co/datasets/kanhatakeyama/AutoMultiTurnByCalm3-22B)| A synthetic instruction dataset. |
| |[ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)| A synthetic instruction dataset. |
| |[wizardlm8x22b-logical-math-coding-sft-ja](https://huggingface.co/datasets/llm-jp/wizardlm8x22b-logical-math-coding-sft-ja)| A synthetic instruction dataset. |
| |[magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)| A synthetic instruction dataset we created. |
| |[jaster v1.4.1](https://github.com/llm-jp/llm-jp-eval/tree/v1.4.1)| - |
| |[extraction-wiki-ja](https://huggingface.co/datasets/llm-jp/extraction-wiki-ja)| A synthetic instruction dataset we created. |
|English|[Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater)| - |
|Japanese & English|[Synthetic-JP-EN-Coding-Dataset](https://huggingface.co/datasets/llm-jp/Synthetic-JP-EN-Coding-Dataset)| A synthetic instruction dataset. |
#### Direct Preference Optimization
For Direct Preference Optimization (DPO), we adopted rejection sampling.
Prompts were sampled from the dataset used in SFT, and multiple responses were generated for each prompt.
These responses were then scored (by [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)), and DPO was performed by treating high-scoring responses as positive examples and low-scoring responses as negative examples.
We conducted DPO in two stages.
In the second stage, we additionally used [ac-self-inst](https://huggingface.co/datasets/llm-jp/ac-self-inst), a Japanese preference dataset focused on safety.
## Evaluation
### MT Bench (Japanese and English)
We evaluated the models using `gpt-4o-2024-08-06`.
The scores represent the average values obtained from three rounds of inference and evaluation.
For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
| Model Name | JA | EN |
|:------------------------------------------------------------------------------------------------------------------------------|----------:|-------:|
| gpt-35-turbo-1106 | 6.48 | 7.56 |
| gpt-4-0613 | 7.29 | 7.72 |
| gpt-4o-2024-08-06 | 8.10 | 8.38 |
| [sbintuitions/sarashina2.2-1b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-1b-instruct-v0.1) | 5.30 | 5.66 |
| [sbintuitions/sarashina2.2-3b-instruct-v0.1](https://huggingface.co/sbintuitions/sarashina2.2-3b-instruct-v0.1) | 7.07 | 6.96 |
| [Rakuten/RakutenAI-2.0-8x7B-instruct](https://huggingface.co/Rakuten/RakutenAI-2.0-8x7B-instruct) | 6.68 | 6.33 |
| [cyberagent/calm3-22b-chat](https://huggingface.co/cyberagent/calm3-22b-chat) | 6.86 | 6.77 |
| [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) | 7.07 | 7.99 |
| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 7.64 | 8.27 |
| [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | 5.46 | 6.95 |
| [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | 8.00 | 8.30 |
| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | 8.36 | 8.33 |
| [tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) | 7.64 | 8.02 |
| [stockmark/Stockmark-2-100B-Instruct-beta](https://huggingface.co/stockmark/Stockmark-2-100B-Instruct-beta) | 7.42 | 7.17 |
| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.64 | 4.09 |
| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 6.21 | 6.13 |
| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 6.60 | 6.49 |
| [llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) | 6.30 | 5.70 |
| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 7.37 | 7.01 |
| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 7.50 | 7.05 |
### AnswerCarefully-Eval
[AnswerCarefully-Eval](https://www.anlp.jp/proceedings/annual_meeting/2025/pdf_dir/Q4-19.pdf) assesses the safety of Japanese language model outputs using the LLM-as-a-Judge approach, based on the test set from [llm-jp/AnswerCarefully](https://huggingface.co/datasets/llm-jp/AnswerCarefully).
We evaluated the models using `gpt-4o-2024-08-06`.
The scores represent the average values obtained from three rounds of inference and evaluation.
For more details, please refer to the [codes](https://github.com/llm-jp/llm-jp-judge/tree/v1.0.0).
| Model name | Score | Acceptance rate (%, ↑) | Violation rate (%, ↓) |
| :--- | ---: | ---: | ---: |
| gpt-35-turbo-1106 | 3.98 | 71.7 | 12.6 |
| gpt-4-0613 | 4.06 | 72.3 | 13.2 |
| gpt-4o-2024-08-06 | 4.09 | 72.7 | 12.5 |
| [llm-jp-3-1.8b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct3) | 4.03 | 75.9 | 12.2 |
| [llm-jp-3-13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-13b-instruct3) | 4.37 | 88.4 | 6.5 |
| [llm-jp-3-8x13b-instruct3](https://huggingface.co/llm-jp/llm-jp-3-8x13b-instruct3) | 4.48 | 91.6 | 4.3 |
| [llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) | 3.66 | 64.7 | 24.3 |
| [llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) | 4.17 | 82.4 | 12.2 |
| [llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) | 4.26 | 83.1 | 11.6 |
## Risks and Limitations
The models released here are in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Send Questions to
llm-jp(at)nii.ac.jp
## License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Model Card Authors
*The names are listed in alphabetical order.*
Hirokazu Kiyomaru and Takashi Kodama. |
Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q4_K_M-GGUF | Triangle104 | 2025-05-30T04:08:05Z | 0 | 0 | null | [
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"base_model:quantized:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-30T04:03:47Z | ---
tags:
- chat
- llama-cpp
- gguf-my-repo
base_model: Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1
pipeline_tag: text-generation
---
# Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q4_K_M-GGUF
This model was converted to GGUF format from [`Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1`](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1) 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/Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1) for more details on the model.
---
The JOSIEFIED model family represents a series of highly advanced language models built upon renowned architectures such as Alibabaโs Qwen2/2.5/3, Googleโs Gemma3, and Metaโs LLaMA 3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (โabliteratedโ) and further fine-tuned to maximize uncensored behavior without compromising tool usage or instruction-following abilities.
Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks โ delivering both raw power and utility.
These models are intended for advanced users who require unrestricted, high-performance language generation.
---
## 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 Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q4_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q4_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-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 Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q4_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-Q4_K_M-GGUF --hf-file josiefied-deepseek-r1-0528-qwen3-8b-abliterated-v1-q4_k_m.gguf -c 2048
```
|
vermoney/0e8f8067-ba99-4fe1-9994-12227d37b3c1 | vermoney | 2025-05-30T04:01:25Z | 0 | 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",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-30T02:59:19Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0e8f8067-ba99-4fe1-9994-12227d37b3c1
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: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/Mistral-Nemo-Base-2407
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- afc11d6986d3bada_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vermoney/0e8f8067-ba99-4fe1-9994-12227d37b3c1
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/afc11d6986d3bada_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: dd930abf-a385-47da-8706-5426157ff6cc
wandb_project: s56-9
wandb_run: your_name
wandb_runid: dd930abf-a385-47da-8706-5426157ff6cc
warmup_steps: 40
weight_decay: 0.02
xformers_attention: false
```
</details><br>
# 0e8f8067-ba99-4fe1-9994-12227d37b3c1
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2609
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- 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: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3564 | 0.0066 | 280 | 1.2609 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
Bengb3ng/Coklatros | Bengb3ng | 2025-05-30T04:01:24Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-30T04:01:24Z | ---
license: bigscience-openrail-m
---
|
FormlessAI/8736b765-95e1-4fe2-883e-5924ab72072d | FormlessAI | 2025-05-30T03:48:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:samoline/f30fb71f-4236-4b13-9787-651ce85a71f4",
"base_model:finetune:samoline/f30fb71f-4236-4b13-9787-651ce85a71f4",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T01:25:51Z | ---
base_model: samoline/f30fb71f-4236-4b13-9787-651ce85a71f4
library_name: transformers
model_name: 8736b765-95e1-4fe2-883e-5924ab72072d
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 8736b765-95e1-4fe2-883e-5924ab72072d
This model is a fine-tuned version of [samoline/f30fb71f-4236-4b13-9787-651ce85a71f4](https://huggingface.co/samoline/f30fb71f-4236-4b13-9787-651ce85a71f4).
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="FormlessAI/8736b765-95e1-4fe2-883e-5924ab72072d", 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/phoenix-formless/Gradients/runs/fcrejcd1)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
c0ntrolZ/FT-openQA-tulu3-personas-math | c0ntrolZ | 2025-05-30T03:46:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T03:45:35Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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] |
DUTIR-Wang/PclGPT-CN | DUTIR-Wang | 2025-05-30T03:29:54Z | 3 | 0 | null | [
"pytorch",
"chatglm",
"custom_code",
"arxiv:2410.00361",
"license:mit",
"region:us"
] | null | 2024-09-29T02:20:48Z | ---
license: mit
---
# PclGPT
PclGPT is a bilingual large language model group (LLM) based on ChatGLM-3 and LLaMA-2, divided into two versions according to the training language: PclGPT-CN (based on ChatGLM) and PclGPT-EN (based on LLaMA). Built upon these foundational models, PclGPT has undergone both pre-training and supervised fine-tuning (SFT) to detect patronizing and condescending language (PCL) and other offensive speech. The maximum supported context length for the model is 4096 tokens.
# Training Process
We constructed the Pcl-PT pre-training dataset and the Pcl-SFT supervised fine-tuning dataset for application in the pre-training and supervised fine-tuning processes.
# ๐ This paper has been accepted in EMNLP 2024 (Findings)
Our paper can be accessed here. Paper link: [https://arxiv.org/abs/2410.00361](https://arxiv.org/abs/2410.00361)
Our github [https://github.com/dut-laowang/emnlp24-PclGPT](https://github.com/dut-laowang/emnlp24-PclGPT)
# Inference
After downloading the weights, use the following code for single-sample inference with PclGPT-CN.
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("DUTIR-Wang/PclGPT-CN", trust_remote_code=True)
model = AutoModel.from_pretrained("DUTIR-Wang/PclGPT-CN", trust_remote_code=True).half().cuda()
def generate_response():
# ๆ ทไพๆๆฌ
sample_text = "ๅบๅบๆซๅฐ็๏ผไฝ ๅฏ็ๆฏๅๅฎณๅไบ"
instruction = (
"ๅๅฎไฝ ๆฏไธๅ่ฏญ่จๅญฆๅฎถ๏ผๆฃๆตๅฑ
้ซไธดไธ่จ่ฎบใๅฑ
้ซไธดไธ่จ่ฎบๆฏไผๅฟๅฐไฝ็พคไฝ้ๅฏนๅผฑๅฟ็พคไฝ็ไผ่ถ่จ่ฎบ๏ผ"
"่ฏญ่จๆปๅปๆง่พๅผฑ๏ผไฝๅพๅพๅคนๆ็่ฎฝๅบ๏ผๅปๆฟๅฐ่ฑก๏ผไผๅฏนๅผฑๅฟ็พคไฝ้ ๆไผคๅฎณใ"
"ๅฑ
้ซไธดไธๆ นๆฎ่ฏญๆฐ็จๅบฆๅไธบ A็บง๏ผ้ๅฑ
้ซไธดไธ๏ผๆญฃๅธธ้ๆญง่งใ้ๅทๆดๅ่จ่ฎบ๏ผ๏ผ"
"B็บง๏ผ่ฝปๅพฎๅฑ
้ซไธดไธ๏ผ่ฏญๆฐ็ฅๅธฆ้ผๅฑๆๆๆฏ๏ผ๏ผC็บง๏ผไธญ็ญๅฑ
้ซไธดไธ๏ผ่ฏด่ฏไบบ่พไธบๅฎข่ง้่ฟฐ๏ผไฝ่ฏญๆฐๅธฆๆๆญง่ง๏ผ๏ผ"
"D็บง๏ผไธฅ้ๅฑ
้ซไธดไธ๏ผ่ฏด่ฏไบบ่ฏญๆฐ่ฝป่๏ผไธฅ้ๆญง่งๅผฑๅฟ็พคไฝ๏ผใ"
"ๆฅไธๆฅๅฐ็ปไฝ ไธๆฎตๆๆฌ๏ผๆ นๆฎไธ่ฟฐ่งๅ๏ผไฝ ่ด่ดฃๅคๆญ่ฏฅๆๆฌๅฑไบ๏ผA/B/C/D็บง๏ผ็ๅชไธ็บง๏ผๅนถๅชๅ็ญ้้กนใ"
"-> ๆๆฌ๏ผ({})"
).format(sample_text)
# Tokenizer ๅๆจกๅๆจ็
inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=1024)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
model_output = extract_option(output_text)
return classify_output(model_output)
def extract_option(output_text):
options = ['A', 'B', 'C', 'D']
for char in reversed(output_text.strip()):
if char in options:
return char
return "ๆ ๆณ่ฏๅซ็่พๅบ"
def classify_output(model_output):
# ๆ นๆฎๆจกๅ่พๅบ็้้กน่ฟๅ็ธๅบ็่งฃ้
if model_output == "A":
return "ๅคๆญไธบA็บง๏ผ้ๅฑ
้ซไธดไธ"
elif model_output == "B":
return "ๅคๆญไธบB็บง๏ผ่ฝปๅพฎๅฑ
้ซไธดไธ"
elif model_output == "C":
return "ๅคๆญไธบC็บง๏ผไธญ็ญๅฑ
้ซไธดไธ"
elif model_output == "D":
return "ๅคๆญไธบD็บง๏ผไธฅ้ๅฑ
้ซไธดไธ"
else:
return "ๆ ๆณ่ฏๅซ็่พๅบ๏ผ่ฏทๆฃๆฅ่พๅ
ฅๆๆจกๅ่พๅบ"
response = generate_response()
print(response)
```
The output will be
```
"ๅคๆญไธบD็บง๏ผไธฅ้ๅฑ
้ซไธดไธ"
```
# Cite
```bibtex
@misc{wang2024pclgptlargelanguagemodel,
title={PclGPT: A Large Language Model for Patronizing and Condescending Language Detection},
author={Hongbo Wang and Mingda Li and Junyu Lu and Hebin Xia and Liang Yang and Bo Xu and Ruizhu Liu and Hongfei Lin},
year={2024},
eprint={2410.00361},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.00361},
}
```
# Other Parts
For the PclGPT-EN model trained on English data, please check [https://huggingface.co/DUTIR-Wang/PclGPT-EN](https://huggingface.co/DUTIR-Wang/PclGPT-EN). |
vertings6/a08d6e3a-54d9-4e2d-b864-148802b88fc7 | vertings6 | 2025-05-30T03:26:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-7B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-29T23:13:44Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a08d6e3a-54d9-4e2d-b864-148802b88fc7
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
absolute_data_files: false
adapter: lora
base_model: Qwen/Qwen2-7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 307d2e5af7dc1af9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: 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: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/a08d6e3a-54d9-4e2d-b864-148802b88fc7
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/307d2e5af7dc1af9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: f6dfc0da-948d-4f17-970d-c62678115b48
wandb_project: s56-7
wandb_run: your_name
wandb_runid: f6dfc0da-948d-4f17-970d-c62678115b48
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# a08d6e3a-54d9-4e2d-b864-148802b88fc7
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3054
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- 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: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1571 | 0.0000 | 1 | 2.3468 |
| 1.3714 | 0.0038 | 250 | 1.3894 |
| 1.3922 | 0.0077 | 500 | 1.3054 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
themendu/HarmFormer | themendu | 2025-05-30T03:13:30Z | 0 | 0 | null | [
"pytorch",
"text-classification",
"en",
"arxiv:2505.02009",
"base_model:allenai/longformer-base-4096",
"base_model:finetune:allenai/longformer-base-4096",
"license:apache-2.0",
"region:us"
] | text-classification | 2025-05-01T12:32:16Z | ---
license: apache-2.0
language:
- en
base_model:
- allenai/longformer-base-4096
pipeline_tag: text-classification
---
# HarmFormer
HarmFormer is a finetuned `allenai/longformer-base-4096`, which was trained to detect potentially harmful content across 5 different harm categories with three dimensions (Safe, Topical, Toxic) across long text and short text scenarios:
- H: Hate and Violence
- IH: Ideological Harm
- SE: Sexual Harm
- IL: Illegal Activities
- SI: Self-Inflicted Harm
We create and define HarmFormer to identify and detect harmful content in text data (especially web pages), which can be used for content moderation, safety checks, and other applications where understanding the nature of text's harmfulness is crucial.
More details about HarmFormer can be found in [our paper - Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs](https://arxiv.org/pdf/2505.02009).
## Model Details
- **Base Model:** allenai/longformer-base-4096
- **Number of Classes:** 5
- **Risk Levels per Class:** 3
- **Max Sequence Length:** 1024
## Usage
```python
from transformers import AutoTokenizer
from modeling import HarmFormer
import torch
# Load the model and tokenizer
model_path = "themendu/HarmFormer"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = HarmFormer.from_pretrained(model_path)
# Prepare input text
text = "Your text here"
inputs = tokenizer(
text,
add_special_tokens=True,
max_length=1024,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
# Run inference
with torch.no_grad():
outputs = model(**inputs)
# Process outputs
logits = torch.stack(outputs, dim=0).permute(1, 0, 2)
probabilities = torch.softmax(logits, dim=-1)
predictions = [[[round(prob, 3) for prob in class_probs] for class_probs in sample] for sample in probabilities.cpu().tolist()]
print(predictions)
```
### Batch Processing
For processing multiple texts at once:
```python
texts = ["Text 1", "Text 2", "Text 3"]
inputs = tokenizer(
texts,
add_special_tokens=True,
max_length=1024,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
with torch.no_grad():
outputs = model(**inputs)
logits = torch.stack(outputs, dim=0).permute(1, 0, 2)
probabilities = torch.softmax(logits, dim=-1)
predictions = [[[round(prob, 3) for prob in class_probs] for class_probs in sample] for sample in probabilities.cpu().tolist()]
```
## Citation
If you use this model in your research, please cite:
```
@misc{mendu2025saferpretraininganalyzingfiltering,
title={Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs},
author={Sai Krishna Mendu and Harish Yenala and Aditi Gulati and Shanu Kumar and Parag Agrawal},
year={2025},
eprint={2505.02009},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.02009},
}
``` |
Azzindani/Qwen2.5_1.5B_IT_ID_Legal | Azzindani | 2025-05-30T03:12:14Z | 29 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"dataset:Azzindani/Indonesian_Legal_QA",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-11T23:29:56Z | ---
library_name: transformers
tags:
- unsloth
license: apache-2.0
datasets:
- Azzindani/Indonesian_Legal_QA
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
---
# ๐ง Qwen 2.5 1.5B Instruct Fine-Tuned with GRPO on Indonesian Legal QA Dataset
Welcome! This repository hosts a **fine-tuned version of [Qwen 2.5 1.5B Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)** using **Group Relative Policy Optimization (GRPO)**, trained on a custom **Indonesian Legal Q\&A Dataset**. The goal is to enhance the model's reasoning and **structured thinking** capabilities for legal question-answering tasks.
You can try the demo [here](https://huggingface.co/spaces/Azzindani/ID_Legal)
---
## ๐ Model Summary
* **Purpose**: Research and Development
* **Base Model**: Qwen 2.5 1.5B Instruct
* **Fine-tuning Method**: Group Relative Policy Optimization (GRPO)
* **Language**: Bahasa Indonesia ๐ฎ๐ฉ
* **Domain**: Legal / Law (Q\&A format)
* **Purpose**: Boost performance in structured, legal reasoning under Indonesian legal context
---
## ๐งฉ What is GRPO?
**Group Relative Policy Optimization (GRPO)** is an advanced reinforcement learning fine-tuning technique that:
* Groups samples by difficulty or topic (e.g., legal concepts)
* Encourages policies (model outputs) to optimize within their group context
* Promotes **structured and relative improvements**, not just raw accuracy
This method leads to:
* **Better structured answers**
* **Improved logical flow**
* **Greater consistency** in domain-specific reasoning (e.g., answering legal queries with relevant laws and regulations)
---
## ๐ Dataset Overview
Our dataset consists of:
* **Indonesian Legal Questions** [pertanyaan hukum](https://huggingface.co/datasets/Azzindani/Indonesian_Regulation_QA)
---
## ๐ง Structured Thinking Enabled
The fine-tuned model is trained to think in **steps** using GRPO:
1. **Understand the legal context**
2. **Identify the relevant law**
3. **Apply reasoning with facts**
4. **Summarize the legal conclusion clearly**
This mimics how **law students or practitioners approach** legal cases, making the model suitable for:
* Law education
* Legal chatbot assistants
* Indonesian legal exam prep
---
## ๐ค Acknowledgements
* [Qwen team](https://huggingface.co/Qwen)
* [GRPO research paper](https://arxiv.org/abs/2402.03300)
---
## ๐ฌ Contact
Want to collaborate or discuss ideas?
๐ง [Github](https://github.com/azzindani)
๐ [LinkedIn](https://www.linkedin.com/in/azzindan1/) |
Flock2Moooooo/task-10-microsoft-Phi-3.5-mini-instruct | Flock2Moooooo | 2025-05-30T03:10:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"license:other",
"region:us"
] | null | 2025-05-29T13:10:10Z | ---
library_name: peft
license: other
base_model: microsoft/Phi-3.5-mini-instruct
---
<!-- 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. -->
# lora
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
hyj117/bert-kor-kosa-nsmc | hyj117 | 2025-05-30T03:03:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:20Z | ---
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 ๐ค 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] |
RosyHey/bert-kor-kosa-nsmc2 | RosyHey | 2025-05-30T03:03:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:20Z | ---
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 ๐ค 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]
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lhss0520/bert-kor-kosa-nsmc | lhss0520 | 2025-05-30T03:02:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T03:01:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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harriscj/gemma-3 | harriscj | 2025-05-30T02:46:41Z | 60 | 0 | peft | [
"peft",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"region:us"
] | null | 2025-05-24T16:31:29Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
library_name: peft
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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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).
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### Framework versions
- PEFT 0.15.2 |
JosephTong/llava-v1.5-7b-flowcut128 | JosephTong | 2025-05-30T02:41:20Z | 0 | 1 | null | [
"safetensors",
"llava_llama",
"image-text-to-text",
"arxiv:2505.19536",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:finetune:lmsys/vicuna-7b-v1.5",
"license:apache-2.0",
"region:us"
] | image-text-to-text | 2025-05-29T03:12:40Z | ---
license: apache-2.0
base_model:
- lmsys/vicuna-7b-v1.5
pipeline_tag: image-text-to-text
---
# FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models
Jintao Tong<sup>1</sup>,
Wenwei Jin<sup>2</sup>,
Pengda Qin<sup>2</sup>,
Anqi Li<sup>3</sup>,
Yixiong Zou<sup>1โ</sup>
Yuhong Li<sup>2โ</sup>,
Yuhua Li<sup>1</sup>,
Ruixuan Li<sup>1</sup>
<br><br>
<sup>1</sup>School of Computer Science and Technology, Huazhong University of Science and Technology<br> <sup>2</sup>Xiaohongshu Inc., <sup>3</sup>Institute of Information Science, Beijing Jiaotong University
[](https://github.com/TungChintao/FlowCut)
[](https://arxiv.org/pdf/2505.19536)
[](https://github.com/TungChintao/FlowCut/blob/main/LICENSE)
## ๐ก Highlights
> **TLDR:** To address inefficiency from excessive visual tokens in LVLMs, we propose a unified, bottom-up perspective based on information-flow, revealing dynamic redundancy emergence and introduce FlowCut, making pruning decision aligned with the model's inherent behavior, outperforming all existing approaches.
## ๐ Preparation
Our code is easy to use.
1. Clone the [LLaVA](https://github.com/haotian-liu/LLaVA)'s repository.
```
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
```
2. Install the [LLaVA](https://github.com/haotian-liu/LLaVA)'s environment.
```
conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip
pip install -e .
pip install flash-attn --no-build-isolation
```
3. For formal usage, you can install the package from PyPI by running the following command:
```
pip install flowcut
```
For development, you can install the package by cloning the repository and running the following command:
```
git clone https://github.com/TungChintao/FlowCut
cd flowcut
pip install -e .
```
File organization as follow:
```
โโโ LLaVA-main
โโโ flowcut
โโโ llava
โโโ playground
โโโ script
```
## ๐ Quick Start
```Python
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava import eval_model
from flowcut import flowcut
model_path = "liuhaotian/llava-v1.5-7b"
tokenizer, model, image_processor, context_len = load_pretrained_model(
model_path=model_path,
model_base=None,
model_name=get_model_name_from_path(model_path)
)
## FlowCut retains 64 visual tokens
model = flowcut(model, target_num=64)
```
## ๐ Evaluation
The evaluation code follows the structure of [LLaVA](https://github.com/haotian-liu/LLaVA) or [Lmms-Eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). After loading the model, simply add two lines as shown below:
```python
## Load LLaVA Model (code from llava.eval.model_vqa_loader)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
## add FlowCut
from flowcut import flowcut
model = flowcut(model, target_num=64)
```
Script templetes (please follow the detailed instruction in [LLaVA-Evaluation](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)).
```Shell
bash scripts/v1_5/eval/[Benchmark].sh
```
Examples:
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mme.sh
```
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/vqav2.sh
```
## ๐ฏ Training
The training code follows the structure of [LLaVA](https://github.com/haotian-liu/LLaVA). After loading the model, simply add two lines as shown below:
```python
## Load LLaVA Model (code from llava.train)
code of loading model...
## add FlowCut
from flowcut import flowcut
model = flowcut(model, target_num=64)
## training
trainer = LLaVATrainer(model=model,
tokenizer=tokenizer,
args=training_args,
**data_module)
```
## ๐ License
- This project is released under the [Apache 2.0 license](https://github.com/TungChintao/FlowCut/blob/main/LICENSE).
## ๐ Citation
- If you find this project useful in your research, please consider citing:
```bibtex
@article{tong2025flowcut,
title={FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models},
author={Tong, Jintao and Jin, Wenwei and Qin, Pengda and Li, Anqi and Zou, Yixiong and Li, Yuhong and Li, Yuhua and Li, Ruixuan},
journal={arXiv preprint arXiv:2505.19536},
year={2025}
}
```
|
Gusanidas/branch-grpo-model-qwen-3b-branch | Gusanidas | 2025-05-30T02:33:31Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T10:43:38Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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<!-- 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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[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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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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hdong0/Qwen2.5-Math-1.5B-Open-R1-GRPO_MATH_1000steps_lr1e-6_kl1e-3_acc | hdong0 | 2025-05-30T02:09:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T18:28:42Z | ---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen2.5-Math-1.5B-Open-R1-GRPO_MATH_1000steps_lr1e-6_kl1e-3_acc
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-Math-1.5B-Open-R1-GRPO_MATH_1000steps_lr1e-6_kl1e-3_acc
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="hdong0/Qwen2.5-Math-1.5B-Open-R1-GRPO_MATH_1000steps_lr1e-6_kl1e-3_acc", 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: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
httppp/finetuned-llama3.1Ins-4bit-gguf | httppp | 2025-05-30T02:05:58Z | 0 | 0 | null | [
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-30T01:32:01Z | ---
license: apache-2.0
---
|
alana-flores-18/original.exlusive.twitter.foto.filtrada.de.alana.video.alana.flores.telegram.viral.x | alana-flores-18 | 2025-05-30T02:05:47Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-30T02:04:55Z | original exlusive twitter foto filtrada de alana video alana flores telegram viral x
<a rel="nofollow" href="http://viralflix.xyz/leaked?pa">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ</a>
<a rel="nofollow" href="http://viralflix.xyz/leaked?pa">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )</a>
<a rel="nofollow" href="http://viralflix.xyz/leaked?pa"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
original exlusive twitter foto filtrada de alana video alana flores telegram viral x
original exlusive twitter foto filtrada de alana video alana flores telegram viral x |
jinx2321/nllb-1e4-paper-4 | jinx2321 | 2025-05-30T02:02:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"base_model:jinx2321/nllb-1e4-paper",
"base_model:finetune:jinx2321/nllb-1e4-paper",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-29T23:18:05Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: jinx2321/nllb-1e4-paper
tags:
- generated_from_trainer
model-index:
- name: nllb-1e4-paper-4
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. -->
# nllb-1e4-paper-4
This model is a fine-tuned version of [jinx2321/nllb-1e4-paper](https://huggingface.co/jinx2321/nllb-1e4-paper) 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: 128
- 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
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
mradermacher/gemma-3-12b-it-qat-abliterated-GGUF | mradermacher | 2025-05-30T02:00:24Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:mlabonne/gemma-3-12b-it-qat-abliterated",
"base_model:quantized:mlabonne/gemma-3-12b-it-qat-abliterated",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T11:28:50Z | ---
base_model: mlabonne/gemma-3-12b-it-qat-abliterated
language:
- en
library_name: transformers
license: gemma
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/mlabonne/gemma-3-12b-it-qat-abliterated
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-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/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q3_K_L.gguf) | Q3_K_L | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.IQ4_XS.gguf) | IQ4_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q4_K_S.gguf) | Q4_K_S | 7.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q4_K_M.gguf) | Q4_K_M | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q5_K_S.gguf) | Q5_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q5_K_M.gguf) | Q5_K_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q6_K.gguf) | Q6_K | 9.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-12b-it-qat-abliterated-GGUF/resolve/main/gemma-3-12b-it-qat-abliterated.Q8_0.gguf) | Q8_0 | 12.6 | fast, best quality |
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 -->
|
m-i/QwQ-32B-abliterated-mlx-8Bit | m-i | 2025-05-30T01:58:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"abliterated",
"uncensored",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:huihui-ai/QwQ-32B-abliterated",
"base_model:quantized:huihui-ai/QwQ-32B-abliterated",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] | text-generation | 2025-05-30T01:56:07Z | ---
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/QwQ-32B-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: huihui-ai/QwQ-32B-abliterated
tags:
- chat
- abliterated
- uncensored
- mlx
- mlx-my-repo
library_name: transformers
---
# m-i/QwQ-32B-abliterated-mlx-8Bit
The Model [m-i/QwQ-32B-abliterated-mlx-8Bit](https://huggingface.co/m-i/QwQ-32B-abliterated-mlx-8Bit) was converted to MLX format from [huihui-ai/QwQ-32B-abliterated](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("m-i/QwQ-32B-abliterated-mlx-8Bit")
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)
```
|
Cloudmaster/Llama-3.2-3B-torchao-W8A4-g128 | Cloudmaster | 2025-05-30T01:57:17Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"torchao",
"region:us"
] | text-generation | 2025-05-30T01:51:41Z | ---
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 ๐ค 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] |
sophie-rain-18/original.video.18.sophie.rain.viral.video.sophie.rain.spiderman.leaked.video.on.social.media | sophie-rain-18 | 2025-05-30T01:52:16Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-30T01:51:36Z | original.video.18.sophie.rain.viral.video.sophie.rain.spiderman.leaked.video.on.social.media
<a rel="nofollow" href="http://viralflix.xyz/leaked?pa">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ</a>
<a rel="nofollow" href="http://viralflix.xyz/leaked?pa">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )</a>
<a rel="nofollow" href="http://viralflix.xyz/leaked?pa"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
original.video.18.sophie.rain.viral.video.sophie.rain.spiderman.leaked.video.on.social.media
original.video.18.sophie.rain.viral.video.sophie.rain.spiderman.leaked.video.on.social.media
original.video.18.sophie.rain.viral.video.sophie.rain.spiderman.leaked.video.on.social.media |
liumy2010/Qwen2.5-3B-math-SFT-RFT | liumy2010 | 2025-05-30T01:51:05Z | 19 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2505.16984",
"base_model:Qwen/Qwen2.5-3B",
"base_model:finetune:Qwen/Qwen2.5-3B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-21T21:30:24Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-3B
---
## UFT
This repository contains the model presented in [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://huggingface.co/papers/2505.16984).
Code: https://github.com/liumy2010/UFT
## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-3B-kk_logic-RFT | liumy2010 | 2025-05-30T01:50:55Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2505.16984",
"base_model:Qwen/Qwen2.5-3B",
"base_model:finetune:Qwen/Qwen2.5-3B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-23T04:01:54Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-3B
---
## UFT
This repository contains the model presented in [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://huggingface.co/papers/2505.16984).
Code: https://github.com/liumy2010/UFT
## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-1.5B-math-R3 | liumy2010 | 2025-05-30T01:50:30Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2505.16984",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-20T11:47:51Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-1.5B
---
## UFT
This repository contains the model presented in [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://huggingface.co/papers/2505.16984).
Code: https://github.com/liumy2010/UFT
## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-1.5B-countdown-R3 | liumy2010 | 2025-05-30T01:49:58Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2505.16984",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-18T00:26:22Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-1.5B
---
## UFT
This repository contains the model presented in [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://huggingface.co/papers/2505.16984).
Code: https://github.com/liumy2010/UFT
## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-0.5B-math-R3 | liumy2010 | 2025-05-30T01:49:50Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2505.16984",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-19T08:48:50Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-0.5B
---
## UFT
This repository contains the model presented in [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://huggingface.co/papers/2505.16984).
Code: https://github.com/liumy2010/UFT
## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
Nihel13/tatr_model | Nihel13 | 2025-05-30T01:49:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"table-transformer",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-05-30T01:48:57Z | ---
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 ๐ค 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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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#### Hardware
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#### Software
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liumy2010/Llama-3.2-3B-countdown-UFT | liumy2010 | 2025-05-30T01:48:39Z | 26 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2505.16984",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T23:14:02Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model:
- meta-llama/Llama-3.2-3B
---
## UFT
This repository contains the model presented in [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://huggingface.co/papers/2505.16984).
Code: https://github.com/liumy2010/UFT
## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Llama-3.2-1B-kk_logic-SFT-RFT | liumy2010 | 2025-05-30T01:48:23Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2505.16984",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T08:47:16Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model:
- meta-llama/Llama-3.2-1B
---
## UFT
This repository contains the model presented in [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://huggingface.co/papers/2505.16984).
Code: https://github.com/liumy2010/UFT
## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
starlineventures/outputs | starlineventures | 2025-05-30T01:15:50Z | 1 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2025-05-24T00:37:40Z | ---
base_model: microsoft/phi-2
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 0.0001
- train_batch_size: 10
- eval_batch_size: 16
- seed: 3407
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1 |
bruhzair/prototype4x18 | bruhzair | 2025-05-30T01:10:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T00:47:12Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x18
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-Nexus/snapshots/1fc6f9b78d8921a26003edb06a292e94488a4c52
* /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
* /workspace/cache/models--Sao10K--L3-70B-Euryale-v2.1/snapshots/36ad832b771cd783ea7ad00ed39e61f679b1a7c6
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-Nexus/snapshots/1fc6f9b78d8921a26003edb06a292e94488a4c52
parameters:
select_topk: 0.9
- model: /workspace/cache/models--Sao10K--L3-70B-Euryale-v2.1/snapshots/36ad832b771cd783ea7ad00ed39e61f679b1a7c6
parameters:
select_topk: 0.5
- model: /workspace/cache/models--SicariusSicariiStuff--Negative_LLAMA_70B/snapshots/097a11b4600eafe333a2be0309bbdf6be2f197c4
parameters:
select_topk: 0.5
- model: /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
parameters:
select_topk: 0.85
base_model: /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
merge_method: sce
tokenizer:
source: union
chat_template: llama3
int8_mask: true
dtype: bfloat16
```
|
Hsianchengfun/merged_model_WOQ_epoch1441 | Hsianchengfun | 2025-05-30T01:08:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T01:05:38Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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### Recommendations
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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
### Training Data
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<!-- 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]
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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
<!-- 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]
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[More Information Needed]
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