modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-19 06:28:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 565
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-19 06:22:35
| card
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|---|---|---|---|---|---|---|---|---|---|
ShourenWSR/HT-phase_scale-Llama-80k-phase2
|
ShourenWSR
| 2025-09-18T05:34:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:28:25Z |
---
library_name: transformers
license: other
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Llama_phase2_80k
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. -->
# Llama_phase2_80k
This model is a fine-tuned version of [./saves/2phases/Llama_phase1_80k](https://huggingface.co/./saves/2phases/Llama_phase1_80k) on the phase2_80k 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 12
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.5.1+cu124
- Datasets 2.19.1
- Tokenizers 0.21.1
|
luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2572
|
luckeciano
| 2025-09-18T05:33:58Z | 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-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T01:13:17Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2572
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2572
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) 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="luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2572", 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/max-ent-llms/PolicyGradientStability/runs/ntbyzw80)
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: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
YuanhengCasia/dipllm
|
YuanhengCasia
| 2025-09-18T05:33:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:33:41Z |
---
license: apache-2.0
---
|
Convert411/seanmayaiavatar
|
Convert411
| 2025-09-18T05:33:12Z | 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
| 2025-09-18T04:47:30Z |
---
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: Sean
---
# Seanmayaiavatar
<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 `Sean` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sean",
"lora_weights": "https://huggingface.co/Convert411/seanmayaiavatar/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 [🧨 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('Convert411/seanmayaiavatar', weight_name='lora.safetensors')
image = pipeline('Sean').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: 3333
- Learning rate: 0.0004
- LoRA rank: 40
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Convert411/seanmayaiavatar/discussions) to add images that show off what you’ve made with this LoRA.
|
onnxmodelzoo/deit_tiny_distilled_patch16_224_Opset17
|
onnxmodelzoo
| 2025-09-18T05:31:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:31:53Z |
---
language: en
license: apache-2.0
model_name: deit_tiny_distilled_patch16_224_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cspresnext50_Opset17
|
onnxmodelzoo
| 2025-09-18T05:29:03Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:28:54Z |
---
language: en
license: apache-2.0
model_name: cspresnext50_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cspresnext50_Opset16
|
onnxmodelzoo
| 2025-09-18T05:28:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:28:44Z |
---
language: en
license: apache-2.0
model_name: cspresnext50_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cspresnet50_Opset18
|
onnxmodelzoo
| 2025-09-18T05:28:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:28:33Z |
---
language: en
license: apache-2.0
model_name: cspresnet50_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cspdarknet53_Opset17
|
onnxmodelzoo
| 2025-09-18T05:27:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:27:47Z |
---
language: en
license: apache-2.0
model_name: cspdarknet53_Opset17.onnx
tags:
- Computer_Vision
---
|
luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-v3_8647
|
luckeciano
| 2025-09-18T05:26:55Z | 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-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T02:09:56Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-v3_8647
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-v3_8647
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) 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="luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-v3_8647", 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/max-ent-llms/PolicyGradientStability/runs/jdri3heb)
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: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
onnxmodelzoo/cs3sedarknet_l_Opset16
|
onnxmodelzoo
| 2025-09-18T05:26:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:26:44Z |
---
language: en
license: apache-2.0
model_name: cs3sedarknet_l_Opset16.onnx
tags:
- Computer_Vision
---
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758173136
|
schooncestiaa
| 2025-09-18T05:26:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T05:26:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/cs3edgenet_x_Opset17
|
onnxmodelzoo
| 2025-09-18T05:25:50Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:25:33Z |
---
language: en
license: apache-2.0
model_name: cs3edgenet_x_Opset17.onnx
tags:
- Computer_Vision
---
|
ChenWu98/numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_condition_2048
|
ChenWu98
| 2025-09-18T05:25:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T05:19:27Z |
---
base_model: Qwen/Qwen2.5-1.5B
library_name: transformers
model_name: numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_condition_2048
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_condition_2048
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B).
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="None", 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/chenwu/huggingface/runs/s1l9bsve)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
onnxmodelzoo/cs3edgenet_x_Opset16
|
onnxmodelzoo
| 2025-09-18T05:25:33Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:25:16Z |
---
language: en
license: apache-2.0
model_name: cs3edgenet_x_Opset16.onnx
tags:
- Computer_Vision
---
|
Sayan01/TL-OWM-MiniPLM
|
Sayan01
| 2025-09-18T05:24:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:23:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/cs3darknet_l_Opset18
|
onnxmodelzoo
| 2025-09-18T05:24:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:23:58Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_l_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3darknet_l_Opset17
|
onnxmodelzoo
| 2025-09-18T05:23:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:23:47Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_l_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3darknet_focus_m_Opset18
|
onnxmodelzoo
| 2025-09-18T05:23:36Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:23:29Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_focus_m_Opset18.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-320
|
reinforce-flow
| 2025-09-18T05:23:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:22:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### 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]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
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**APA:**
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## Glossary [optional]
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## Model Card Contact
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|
onnxmodelzoo/cs3darknet_focus_l_Opset16
|
onnxmodelzoo
| 2025-09-18T05:22:53Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:22:42Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_focus_l_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/crossvit_9_240_Opset17
|
onnxmodelzoo
| 2025-09-18T05:22:41Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:22:34Z |
---
language: en
license: apache-2.0
model_name: crossvit_9_240_Opset17.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-300
|
reinforce-flow
| 2025-09-18T05:21:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:21:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
|
ChenWu98/numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_split_0_2048
|
ChenWu98
| 2025-09-18T05:20:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T05:18:57Z |
---
base_model: Qwen/Qwen2.5-1.5B
library_name: transformers
model_name: numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_split_0_2048
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_split_0_2048
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B).
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="None", 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/chenwu/huggingface/runs/1t9u2m6q)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
kevinshin/qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos-pos-add
|
kevinshin
| 2025-09-18T05:19:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"alignment-handbook",
"trl",
"sft",
"conversational",
"dataset:kevinshin/wildchat-creative-writing-3k-critique-v2",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T17:43:30Z |
---
base_model: Qwen/Qwen3-1.7B
datasets: kevinshin/wildchat-creative-writing-3k-critique-v2
library_name: transformers
model_name: qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos-pos-add
tags:
- generated_from_trainer
- alignment-handbook
- trl
- sft
licence: license
---
# Model Card for qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos-pos-add
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the [kevinshin/wildchat-creative-writing-3k-critique-v2](https://huggingface.co/datasets/kevinshin/wildchat-creative-writing-3k-critique-v2) 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="kevinshin/qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos-pos-add", 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/myungjune-sogang-university/general_remo_train/runs/8fhohlud)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.55.0.dev0
- Pytorch: 2.6.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
zjhhhh/qwen2.5_3B_Instruct_judge_rebel_1e4_step_41
|
zjhhhh
| 2025-09-18T05:17:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:16:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nex2627/gemma2b-finetuned-helpdesk-instruct
|
nex2627
| 2025-09-18T05:17:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:08:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758172519
|
schooncestiaa
| 2025-09-18T05:16:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T05:16:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/convnext_xlarge_in22ft1k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:16:18Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:14:33Z |
---
language: en
license: apache-2.0
model_name: convnext_xlarge_in22ft1k_Opset16.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-220
|
reinforce-flow
| 2025-09-18T05:15:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:14:43Z |
---
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 🤗 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]
|
JonusNattapong/xauusd-trading-ai-smc-v2
|
JonusNattapong
| 2025-09-18T05:14:56Z | 0 | 0 |
sklearn
|
[
"sklearn",
"trading",
"finance",
"gold",
"xauusd",
"forex",
"algorithmic-trading",
"smart-money-concepts",
"smc",
"xgboost",
"machine-learning",
"backtesting",
"technical-analysis",
"multi-timeframe",
"intraday-trading",
"high-frequency-trading",
"en",
"dataset:yahoo-finance-gc-f",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-18T05:12:03Z |
---
language: en
license: mit
library_name: sklearn
tags:
- trading
- finance
- gold
- xauusd
- forex
- algorithmic-trading
- smart-money-concepts
- smc
- xgboost
- machine-learning
- backtesting
- technical-analysis
- multi-timeframe
- intraday-trading
- high-frequency-trading
datasets:
- yahoo-finance-gc-f
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xauusd-trading-ai-smc-daily
results:
- task:
type: binary-classification
name: Daily Price Direction Prediction
dataset:
type: yahoo-finance-gc-f
name: Gold Futures (GC=F)
metrics:
- type: accuracy
value: 80.3
name: Accuracy
- type: precision
value: 71
name: Precision (Class 1)
- type: recall
value: 81
name: Recall (Class 1)
- type: f1
value: 76
name: F1-Score
- name: xauusd-trading-ai-smc-15m
results:
- task:
type: binary-classification
name: 15-Minute Price Direction Prediction
dataset:
type: yahoo-finance-gc-f
name: Gold Futures (GC=F)
metrics:
- type: accuracy
value: 77.0
name: Accuracy
- type: precision
value: 76
name: Precision (Class 1)
- type: recall
value: 77
name: Recall (Class 1)
- type: f1
value: 76
name: F1-Score
---
---
# XAUUSD Multi-Timeframe Trading AI Model
## Files Included
### Core Models
- `trading_model.pkl` - Original daily timeframe XGBoost model (85.4% win rate)
- `trading_model_15m.pkl` - 15-minute intraday model (77% validation accuracy)
- `trading_model_1m.pkl` - 1-minute intraday model (partially trained)
- `trading_model_30m.pkl` - 30-minute intraday model (ready for training)
### Documentation
- `README.md` - This comprehensive model card
- `XAUUSD_Trading_AI_Paper.md` - **Research paper with academic structure, literature review, and methodology**
- `XAUUSD_Trading_AI_Paper.docx` - **Word document version (professional format)**
- `XAUUSD_Trading_AI_Paper.html` - **HTML web version (styled and readable)**
- `XAUUSD_Trading_AI_Paper.tex` - **LaTeX source (for academic publishing)**
- `XAUUSD_Trading_AI_Technical_Whitepaper.md` - **Technical whitepaper with mathematical formulations and implementation details**
- `XAUUSD_Trading_AI_Technical_Whitepaper.docx` - **Word document version (professional format)**
- `XAUUSD_Trading_AI_Technical_Whitepaper.html` - **HTML web version (styled and readable)**
- `XAUUSD_Trading_AI_Technical_Whitepaper.tex` - **LaTeX source (for academic publishing)**
### Performance & Analysis
- `backtest_report.csv` - Daily model yearly backtesting performance results
- `backtest_multi_timeframe_results.csv` - Intraday model backtesting results
- `feature_importance_15m.csv` - 15-minute model feature importance analysis
### Scripts & Tools
- `train_multi_timeframe.py` - Multi-timeframe model training script
- `backtest_multi_timeframe.py` - Intraday model backtesting framework
- `multi_timeframe_summary.py` - Comprehensive performance analysis tool
- `fetch_data.py` - Enhanced data acquisition for multiple timeframes
### Dataset Files
- **Daily Data**: `daily_data.csv`, `processed_daily_data.csv`, `smc_features_dataset.csv`, `X_features.csv`, `y_target.csv`
- **Intraday Data**: `1m_data.csv` (5,204 samples), `15m_data.csv` (3,814 samples), `30m_data.csv` (1,910 samples)
## Recent Enhancements (v2.0)
### Visual Documentation
- **Dataset Flow Diagram**: Complete data processing pipeline from raw Yahoo Finance data to model training
- **Model Architecture Diagram**: XGBoost ensemble structure with decision flow visualization
- **Buy/Sell Workflow Diagram**: End-to-end trading execution process with risk management
### Advanced Formulas & Techniques
- **Position Sizing Formula**: Risk-adjusted position calculation with Kelly Criterion adaptation
- **Risk Metrics**: Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown calculations
- **SMC Techniques**: Advanced Order Block detection with volume profile analysis
- **Dynamic Thresholds**: Market volatility-based prediction threshold adjustment
- **Ensemble Signals**: Multi-source signal confirmation (ML + Technical + SMC)
### Performance Analytics
- **Monthly Performance Heatmap**: Visual representation of returns across all test years
- **Risk-Return Scatter Plot**: Performance comparison across different risk levels
- **Market Regime Analysis**: Performance breakdown by trending vs sideways markets
### Documentation Updates
- **Enhanced Technical Whitepaper**: Added comprehensive visual diagrams and mathematical formulations
- **Enhanced Research Paper**: Added Mermaid diagrams, advanced algorithms, and detailed performance analysis
- **Professional Exports**: Both documents now available in HTML, Word, and LaTeX formats
## Multi-Timeframe Trading System (Latest Addition)
### Overview
The system has been extended to support intraday trading across multiple timeframes, enabling higher-frequency trading strategies while maintaining the proven SMC + technical indicator approach.
### Supported Timeframes
- **1-minute (1m)**: Ultra-short-term scalping opportunities
- **15-minute (15m)**: Short-term swing trading
- **30-minute (30m)**: Medium-term position trading
- **Daily (1d)**: Original baseline model (85.4% win rate)
### Data Acquisition
- **Source**: Yahoo Finance API with enhanced intraday data fetching
- **Limitations**: Historical intraday data restricted (recent periods only)
- **Current Datasets**:
- 1m: 5,204 samples (7 days of recent data)
- 15m: 3,814 samples (60 days of recent data)
- 30m: 1,910 samples (60 days of recent data)
### Model Architecture
- **Base Algorithm**: XGBoost Classifier (same as daily model)
- **Features**: 23 features (technical indicators + SMC elements)
- **Training**: Grid search hyperparameter optimization
- **Validation**: 80/20 train/test split with stratification
### Training Results
- **15m Model**: Successfully trained with 77% validation accuracy
- **Feature Importance**: Technical indicators dominant (SMA_50, EMA_12, BB_lower)
- **Training Status**: 1m model partially trained, 30m model interrupted (available for completion)
### Backtesting Performance
- **Framework**: Backtrader with realistic commission modeling
- **Risk Management**: Fixed stake sizing ($1,000 per trade)
- **15m Results**: -0.83% return with 1 trade (conservative strategy)
- **Analysis**: Models show conservative behavior to avoid overtrading
### Key Insights
- ✅ Successfully scaled daily model architecture to intraday timeframes
- ✅ Technical indicators remain most important across all timeframes
- ✅ Conservative prediction thresholds prevent excessive trading
- ⚠️ Limited historical data affects backtesting statistical significance
- ⚠️ Yahoo Finance API constraints limit comprehensive validation
### Files Added
- `train_multi_timeframe.py` - Multi-timeframe model training script
- `backtest_multi_timeframe.py` - Intraday model backtesting framework
- `multi_timeframe_summary.py` - Comprehensive performance analysis
- `trading_model_15m.pkl` - Trained 15-minute model
- `feature_importance_15m.csv` - Feature importance analysis
- `backtest_multi_timeframe_results.csv` - Backtesting performance data
### Next Steps
1. Complete 30m model training
2. Implement walk-forward optimization
3. Add extended historical data sources
4. Deploy best performing intraday model
5. Compare intraday vs daily performance
## Model Description
This is an AI-powered trading model for XAUUSD (Gold vs US Dollar) futures, trained using Smart Money Concepts (SMC) strategy elements. The model uses machine learning to predict 5-day ahead price movements and generate trading signals with high win rates.
### Key Features
- **Asset**: XAUUSD (Gold Futures)
- **Strategy**: Smart Money Concepts (SMC) with technical indicators
- **Prediction Horizon**: 5-day ahead price direction
- **Model Type**: XGBoost Classifier
- **Accuracy**: 80.3% on test data
- **Win Rate**: 85.4% in backtesting
## Intended Use
This model is designed for:
- Educational purposes in algorithmic trading
- Research on SMC strategies
- Backtesting trading strategies
- Understanding ML applications in financial markets
**⚠️ Warning**: This is not financial advice. Trading involves risk of loss. Use at your own discretion.
## Training Data
- **Source**: Yahoo Finance (GC=F - Gold Futures)
- **Period**: 2000-2020 (excluding recent months for efficiency)
- **Features**: 23 features including:
- Price data (Open, High, Low, Close, Volume)
- Technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands)
- SMC features (Fair Value Gaps, Order Blocks, Recovery patterns)
- Lag features (Close prices from previous days)
- **Target**: Binary classification (1 if price rises in 5 days, 0 otherwise)
- **Dataset Size**: 8,816 samples
- **Class Distribution**: 54% down, 46% up (balanced with scale_pos_weight)
## Performance Metrics
### Model Performance
- **Accuracy**: 80.3%
- **Precision (Class 1)**: 71%
- **Recall (Class 1)**: 81%
- **F1-Score**: 76%
### Backtesting Results (2015-2020)
- **Overall Win Rate**: 85.4%
- **Total Return**: 18.2%
- **Sharpe Ratio**: 1.41
- **Yearly Win Rates**:
- 2015: 62.5%
- 2016: 100.0%
- 2017: 100.0%
- 2018: 72.7%
- 2019: 76.9%
- 2020: 94.1%
## Limitations
- Trained on historical data only (2000-2020)
- May not perform well in unprecedented market conditions
- Requires proper risk management
- No consideration of transaction costs, slippage, or market impact
- Model predictions are probabilistic, not guaranteed
## Usage
### Prerequisites
```python
pip install joblib scikit-learn pandas numpy
```
### Loading the Model
```python
import joblib
import pandas as pd
from sklearn.preprocessing import StandardScaler
# Load model
model = joblib.load('trading_model.pkl')
# Load scalers (you need to recreate or save them)
# ... preprocessing code ...
# Prepare features
features = prepare_features(your_data)
prediction = model.predict(features)
probability = model.predict_proba(features)
```
### Features Required
The model expects 23 features in this order:
1. Close
2. High
3. Low
4. Open
5. Volume
6. SMA_20
7. SMA_50
8. EMA_12
9. EMA_26
10. RSI
11. MACD
12. MACD_signal
13. MACD_hist
14. BB_upper
15. BB_middle
16. BB_lower
17. FVG_Size
18. FVG_Type_Encoded
19. OB_Type_Encoded
20. Recovery_Type_Encoded
21. Close_lag1
22. Close_lag2
23. Close_lag3
## Training Details
- **Algorithm**: XGBoost Classifier
- **Hyperparameters**:
- n_estimators: 200
- max_depth: 7
- learning_rate: 0.2
- scale_pos_weight: 1.17 (for class balancing)
- **Cross-validation**: 3-fold
- **Optimization**: Grid search on hyperparameters
## SMC Strategy Elements
The model incorporates Smart Money Concepts:
- **Fair Value Gaps (FVG)**: Price imbalances between candles
- **Order Blocks (OB)**: Areas of significant buying/selling
- **Recovery Patterns**: Pullbacks in trending markets
## Upload to Hugging Face
To share this model on Hugging Face:
1. Create a Hugging Face account at https://huggingface.co/join
2. Generate an access token at https://huggingface.co/settings/tokens with "Write" permissions
3. Test your token: `python test_token.py YOUR_TOKEN`
4. Upload: `python upload_to_hf.py YOUR_TOKEN`
The script will upload:
- `trading_model.pkl` - The trained XGBoost model
- `README.md` - This model card with metadata
- All dataset files (CSV format)
## Citation
If you use this model in your research, please cite:
```
@misc{xauusd-trading-ai,
title={XAUUSD Trading AI Model with SMC Strategy},
author={AI Trading System},
year={2025},
url={https://huggingface.co/JonusNattapong/xauusd-trading-ai-smc}
}
```
### Academic Paper
For the complete academic research paper with methodology, results, and analysis:
**arXiv Paper**: [XAUUSD Trading AI: A Machine Learning Approach Using Smart Money Concepts](https://arxiv.org/abs/XXXX.XXXXX)
## License
This model is released under the MIT License. See LICENSE file for details.
## Contact
For questions or issues, please open an issue on the Hugging Face repository.
|
zjhhhh/qwen2.5_3B_Instruct_judge_rebel_1e4_step_1
|
zjhhhh
| 2025-09-18T05:14:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:13:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/convnext_tiny_Opset16
|
onnxmodelzoo
| 2025-09-18T05:12:36Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:12:24Z |
---
language: en
license: apache-2.0
model_name: convnext_tiny_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/convnext_tiny_in22k_Opset18
|
onnxmodelzoo
| 2025-09-18T05:12:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:12:07Z |
---
language: en
license: apache-2.0
model_name: convnext_tiny_in22k_Opset18.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-180
|
reinforce-flow
| 2025-09-18T05:12:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:11:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/convnext_tiny_in22k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:12:06Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:11:49Z |
---
language: en
license: apache-2.0
model_name: convnext_tiny_in22k_Opset16.onnx
tags:
- Computer_Vision
---
|
mradermacher/mn-12b-the-yapper-GGUF
|
mradermacher
| 2025-09-18T05:11:57Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Burnt-Toast/mn-12b-the-yapper",
"base_model:quantized:Burnt-Toast/mn-12b-the-yapper",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-18T04:21:35Z |
---
base_model: Burnt-Toast/mn-12b-the-yapper
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Burnt-Toast/mn-12b-the-yapper
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#mn-12b-the-yapper-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mn-12b-the-yapper-GGUF/resolve/main/mn-12b-the-yapper.Q8_0.gguf) | Q8_0 | 13.1 | 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 -->
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-160
|
reinforce-flow
| 2025-09-18T05:10:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:10:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/convnext_small_in22k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:09:38Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:09:06Z |
---
language: en
license: apache-2.0
model_name: convnext_small_in22k_Opset16.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-140
|
reinforce-flow
| 2025-09-18T05:09:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:08:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/convnext_large_Opset16
|
onnxmodelzoo
| 2025-09-18T05:06:28Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:05:24Z |
---
language: en
license: apache-2.0
model_name: convnext_large_Opset16.onnx
tags:
- Computer_Vision
---
|
Darkhn/L3.3-70B-Animus-V12.0-GGUF
|
Darkhn
| 2025-09-18T05:06:10Z | 40,957 | 0 |
llama.cpp
|
[
"llama.cpp",
"gguf",
"q3-k-s",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-09T01:27:48Z |
---
license: mit
library_name: llama.cpp
tags:
- gguf
- q2-k-s
---
# L3.3-70B-Animus-V12.0-GGUF
GGUF model files for `L3.3-70B-Animus-V12.0`.
This repository contains GGUF models quantized using [`llama.cpp`](https://github.com/ggerganov/llama.cpp).
- **Base Model:** `L3.3-70B-Animus-V12.0`
- **Quantization Methods Processed in this Job:** `Q4_K_M`, `Q4_K_S`, `Q3_K_L`, `Q3_K_M`, `Q3_K_S`, `Q2_K_S`, `Q2_K`
- **Importance Matrix Used:** Yes
This specific upload is for the **`Q2_K_S`** quantization.
|
huynq2k4/sports-and-outdoors-sft-full-llama-3.1-8b
|
huynq2k4
| 2025-09-18T05:05:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T05:05:16Z |
---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: transformers
model_name: sports-and-outdoors-sft-full-llama-3.1-8b
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for sports-and-outdoors-sft-full-llama-3.1-8b
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-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="huynq2k4/sports-and-outdoors-sft-full-llama-3.1-8b", 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: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
onnxmodelzoo/convnext_large_in22k_Opset17
|
onnxmodelzoo
| 2025-09-18T05:04:18Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:03:13Z |
---
language: en
license: apache-2.0
model_name: convnext_large_in22k_Opset17.onnx
tags:
- Computer_Vision
---
|
uwcc/Crayonism
|
uwcc
| 2025-09-18T05:04:07Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-18T05:02:44Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: A church in a field on a sunny day, [trigger] style.
output:
url: samples/1758171706876__000004000_0.jpg
- text: A seal plays with a ball on the beach, [trigger] style.
output:
url: samples/1758171725034__000004000_1.jpg
- text: A clown at the circus rides on a zebra, [trigger] style.
output:
url: samples/1758171743191__000004000_2.jpg
- text: '[trigger]'
output:
url: samples/1758171761347__000004000_3.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Crayonism
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
---
# Crayonism
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `Crayonism` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/uwcc/Crayonism/tree/main) them in the Files & versions tab.
## Use it with the [🧨 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.bfloat16).to('cuda')
pipeline.load_lora_weights('uwcc/Crayonism', weight_name='Crayonism.safetensors')
image = pipeline('A church in a field on a sunny day, [trigger] style.').images[0]
image.save("my_image.png")
```
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)
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-40
|
reinforce-flow
| 2025-09-18T05:01:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:00:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/convnext_large_in22ft1k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:00:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:59:13Z |
---
language: en
license: apache-2.0
model_name: convnext_large_in22ft1k_Opset16.onnx
tags:
- Computer_Vision
---
|
gsjang/ja-llama-3-elyza-jp-8b-x-meta-llama-3-8b-instruct-mimer_merge
|
gsjang
| 2025-09-18T04:58:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:merge:elyza/Llama-3-ELYZA-JP-8B",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T01:13:55Z |
---
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
- elyza/Llama-3-ELYZA-JP-8B
library_name: transformers
tags:
- mergekit
- merge
---
# ja-llama-3-elyza-jp-8b-x-meta-llama-3-8b-instruct-mimer_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 MIMER-Merge (OT + OCI + Elastic Gating) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
tokenizer:
source: union
merge_method: mimer_merge
base_model: meta-llama/Meta-Llama-3-8B-Instruct
models:
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters: {}
- model: elyza/Llama-3-ELYZA-JP-8B
parameters: {}
parameters: {}
write_readme: README.md
```
|
onnxmodelzoo/convnext_base_Opset16
|
onnxmodelzoo
| 2025-09-18T04:57:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:56:45Z |
---
language: en
license: apache-2.0
model_name: convnext_base_Opset16.onnx
tags:
- Computer_Vision
---
|
stanfordnlp/stanza-he
|
stanfordnlp
| 2025-09-18T04:57:02Z | 647 | 1 |
stanza
|
[
"stanza",
"token-classification",
"he",
"license:apache-2.0",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- stanza
- token-classification
library_name: stanza
language: he
license: apache-2.0
---
# Stanza model for Hebrew (he)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2025-09-18 04:55:12.608
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758171311
|
schooncestiaa
| 2025-09-18T04:56:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T04:56:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/convnext_base_in22ft1k_Opset17
|
onnxmodelzoo
| 2025-09-18T04:54:37Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:54:09Z |
---
language: en
license: apache-2.0
model_name: convnext_base_in22ft1k_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/convnext_base_in22ft1k_Opset16
|
onnxmodelzoo
| 2025-09-18T04:54:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:53:37Z |
---
language: en
license: apache-2.0
model_name: convnext_base_in22ft1k_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/convmixer_1024_20_ks9_p14_Opset16
|
onnxmodelzoo
| 2025-09-18T04:51:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:51:43Z |
---
language: en
license: apache-2.0
model_name: convmixer_1024_20_ks9_p14_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/convit_tiny_Opset18
|
onnxmodelzoo
| 2025-09-18T04:51:42Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:51:36Z |
---
language: en
license: apache-2.0
model_name: convit_tiny_Opset18.onnx
tags:
- Computer_Vision
---
|
zurizheng/scribblediffusion-fruit-test
|
zurizheng
| 2025-09-18T04:41:38Z | 0 | 0 | null |
[
"scribblediffusion",
"region:us"
] | null | 2025-09-18T04:41:37Z |
# ScribbleDiffusion: Fruit Dataset Fine-tuned Model
This model is a fine-tuned version of Stable Diffusion v1.5 for sketch-to-image generation, specifically trained on a fruit dataset.
## Model Description
ScribbleDiffusion allows you to generate high-quality images from simple sketches combined with text prompts. This version has been trained on 6 fruit categories:
- Apple
- Banana
- Guava
- Lime
- Orange
- Pomegranate
## Model Architecture
The model consists of three main components:
1. **UNet**: Modified Stable Diffusion 1.5 UNet for diffusion generation
2. **Sketch Encoder**: Cross-attention encoder that processes input sketches
3. **Sketch Text Combiner**: Module that combines sketch and text embeddings
## Training Details
- **Base Model**: Stable Diffusion v1.5
- **Training Steps**: 5,000 steps
- **Batch Size**: 1 (with gradient accumulation steps: 8)
- **Learning Rate**: 0.0001
- **Image Resolution**: 256x256
- **Mixed Precision**: FP16
- **Optimizer**: AdamW
- **Scheduler**: Cosine with 500 warmup steps
## Usage
### Loading the Model
```python
import torch
from diffusers import UNet2DConditionModel, AutoencoderKL, DDIMScheduler
from transformers import CLIPTokenizer, CLIPTextModel
from safetensors.torch import load_file
# Load base models
vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae")
tokenizer = CLIPTokenizer.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="text_encoder")
scheduler = DDIMScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
# Load fine-tuned UNet
unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
unet_state = load_file("unet.safetensors")
unet.load_state_dict(unet_state)
# Load custom sketch components
# Note: You'll need the custom SketchCrossAttentionEncoder and SketchTextCombiner classes
sketch_encoder_state = load_file("sketch_encoder.safetensors")
sketch_combiner_state = load_file("sketch_text_combiner.safetensors")
```
### Inference Example
```python
# Your sketch should be a 1-channel grayscale image (edges/contours)
sketch = load_sketch_image("your_sketch.png") # 256x256 grayscale
prompt = "a red apple"
# Process sketch and text
sketch_embeddings = sketch_encoder(sketch)
text_embeddings = text_encoder(tokenize(prompt))
combined_embeddings = sketch_text_combiner(text_embeddings, sketch_embeddings)
# Generate image
with torch.no_grad():
latents = torch.randn((1, 4, 32, 32)) # 256x256 -> 32x32 latents
for t in scheduler.timesteps:
noise_pred = unet(latents, t, encoder_hidden_states=combined_embeddings).sample
latents = scheduler.step(noise_pred, t, latents).prev_sample
# Decode to image
image = vae.decode(latents / vae.config.scaling_factor).sample
```
## Model Files
- `unet.safetensors` (3.3GB): Fine-tuned UNet model weights
- `sketch_encoder.safetensors` (24MB): Sketch encoder weights
- `sketch_text_combiner.safetensors` (16 bytes): Sketch-text combiner weights
- `training_info.json`: Training metadata
## Training Data
The model was trained on a curated fruit dataset containing high-quality images of 6 fruit categories. Sketches were automatically generated using edge detection algorithms.
## Limitations
- Trained specifically on fruit images (may not generalize well to other objects)
- Input resolution limited to 256x256
- Requires specific sketch preprocessing (edge detection style)
- Best results with simple, clear sketches
## Ethics and Bias
This model inherits potential biases from the Stable Diffusion v1.5 base model. The fruit dataset training may introduce biases toward specific fruit appearances and orientations present in the training data.
## Citation
```bibtex
@misc{scribblediffusion-fruit-2024,
title={ScribbleDiffusion: Fruit Dataset Fine-tuned Model},
author={Your Name},
year={2024},
howpublished={\\url{https://huggingface.co/your-username/scribblediffusion-fruit}}
}
```
## License
This model is released under the same license as Stable Diffusion v1.5. Please refer to the original licensing terms.
## Acknowledgments
- Based on Stable Diffusion v1.5 by Runway ML
- Training infrastructure and optimization techniques
- Fruit dataset compilation and preprocessing
|
mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF
|
mradermacher
| 2025-09-18T04:40:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat",
"base_model:quantized:Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-18T02:39:43Z |
---
base_model: Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q2_K.gguf) | Q2_K | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q3_K_L.gguf) | Q3_K_L | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q5_K_S.gguf) | Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q6_K.gguf) | Q6_K | 19.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q8_0.gguf) | Q8_0 | 25.2 | 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 -->
|
k1000dai/residualact_libero_spatial_no_tf_5
|
k1000dai
| 2025-09-18T04:34:11Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"residualact",
"dataset:k1000dai/libero-spatial-smolvla",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-18T04:33:58Z |
---
datasets: k1000dai/libero-spatial-smolvla
library_name: lerobot
license: apache-2.0
model_name: residualact
pipeline_tag: robotics
tags:
- lerobot
- robotics
- residualact
---
# Model Card for residualact
<!-- Provide a quick summary of what the model is/does. -->
_Model type not recognized — please update this template._
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
SnowNation/Nyx-3B-Pretrained
|
SnowNation
| 2025-09-18T04:32:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-09-18T04:27:57Z |
---
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 🤗 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]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.01_12800_3
|
winnieyangwannan
| 2025-09-18T04:30:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T04:29:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
serendipity0306/gemma-text-to-sql
|
serendipity0306
| 2025-09-18T04:28:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T03:30:43Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: gemma-text-to-sql
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-text-to-sql
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-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="serendipity0306/gemma-text-to-sql", 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: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 3.3.2
- Tokenizers: 0.22.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Aviral17/model
|
Aviral17
| 2025-09-18T04:25:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T11:58:17Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Aviral17
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
Hhsjsnns/bert-finetuned-imdb
|
Hhsjsnns
| 2025-09-18T04:25:23Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T04:25:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Saikrishna-Alle6/qwen3b-sms-full
|
Saikrishna-Alle6
| 2025-09-18T04:17:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-18T03:42:29Z |
---
library_name: transformers
tags:
- unsloth
- 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 🤗 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]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.005_12800_3
|
winnieyangwannan
| 2025-09-18T04:17:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T04:16:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
david4096/edam-small
|
david4096
| 2025-09-18T04:16:43Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"biomedical",
"biomedical-ontology",
"fusion-concat",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-18T04:13:06Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- biomedical
- biomedical-ontology
- fusion-concat
- gnn-gcn
- medium-ontology
---
# edam-small
This is a sentence-transformers model created with [on2vec](https://github.com/davidmartinrius/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: edam.owl
- **Domain**: biomedical
- **Ontology Concepts**: 3,511
- **Concept Alignment**: 3,511/3,511 (100.0%)
- **Fusion Method**: concat
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 3511
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 128
- **Dropout**: 0.0
- **Training Date**: 2025-09-18
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.2 MB
- **Model Size**: 120.6 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 128 hidden → 64 output
- Structure: 3511 concepts → GNN → 64 output
- Fusion: concat → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('edam-small')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Fusion Method: concat
Simple concatenation of text and ontology embeddings
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- Biomedical domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
MongoDB/mdbr-leaf-mt
|
MongoDB
| 2025-09-18T04:11:44Z | 84 | 3 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"transformers",
"sentence-similarity",
"text-embeddings-inference",
"information-retrieval",
"knowledge-distillation",
"en",
"arxiv:2509.12539",
"arxiv:2205.13147",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-12T23:31:50Z |
---
license: apache-2.0
base_model: microsoft/MiniLM-L6-v2
tags:
- transformers
- sentence-transformers
- sentence-similarity
- feature-extraction
- text-embeddings-inference
- information-retrieval
- knowledge-distillation
language:
- en
---
<div style="display: flex; justify-content: center;">
<div style="display: flex; align-items: center; gap: 10px;">
<img src="logo.webp" alt="MongoDB Logo" style="height: 36px; width: auto; border-radius: 4px;">
<span style="font-size: 32px; font-weight: bold">MongoDB/mdbr-leaf-mt</span>
</div>
</div>
# Content
1. [Introduction](#introduction)
2. [Technical Report](#technical-report)
3. [Highlights](#highlights)
4. [Benchmarks](#benchmark-comparison)
5. [Quickstart](#quickstart)
6. [Citation](#citation)
# Introduction
`mdbr-leaf-mt` is a compact high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks.
To enable even greater efficiency, `mdbr-leaf-mt` supports [flexible asymmetric architectures](#asymmetric-retrieval-setup) and is robust to [vector quantization](#vector-quantization) and [MRL truncation](#mrl-truncation).
If you are looking to perform semantic search / information retrieval (e.g. for RAGs), please check out our [`mdbr-leaf-ir`](https://huggingface.co/MongoDB/mdbr-leaf-ir) model, which is specifically trained for these tasks.
> [!Note]
> **Note**: this model has been developed by the ML team of MongoDB Research. At the time of writing it is not used in any of MongoDB's commercial product or service offerings.
# Technical Report
A technical report detailing our proposed `LEAF` training procedure is [available here](https://arxiv.org/abs/2509.12539).
# Highlights
* **State-of-the-Art Performance**: `mdbr-leaf-mt` achieves new state-of-the-art results for compact embedding models, **ranking #1** on the [public MTEB v2 (Eng) benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models with ≤30M parameters.
* **Flexible Architecture Support**: `mdbr-leaf-mt` supports asymmetric retrieval architectures enabling even greater retrieval results. [See below](#asymmetric-retrieval-setup) for more information.
* **MRL and Quantization Support**: embedding vectors generated by `mdbr-leaf-mt` compress well when truncated (MRL) and can be stored using more efficient types like `int8` and `binary`. [See below](#mrl-truncation) for more information.
## Benchmark Comparison
The table below shows the scores for `mdbr-leaf-mt` on the MTEB v2 (English) benchmark, compared to other retrieval models.
`mdbr-leaf-mt` ranks #1 on this benchmark for models with <30M parameters.
| Model | Size | MTEB v2 (Eng) |
|------------------------------------|---------|---------------|
| OpenAI text-embedding-3-large | Unknown | 66.43 |
| OpenAI text-embedding-3-small | Unknown | 64.56 |
| **mdbr-leaf-mt** | 23M | **63.97** |
| gte-small | 33M | 63.22 |
| snowflake-arctic-embed-s | 32M | 61.59 |
| e5-small-v2 | 33M | 61.32 |
| granite-embedding-small-english-r2 | 47M | 61.07 |
| all-MiniLM-L6-v2 | 22M | 59.03 |
# Quickstart
## Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("MongoDB/mdbr-leaf-mt")
# Example queries and documents
queries = [
"What is machine learning?",
"How does neural network training work?"
]
documents = [
"Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.",
"Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors."
]
# Encode queries and documents
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
# Compute similarity scores
scores = model.similarity(query_embeddings, document_embeddings)
# Print results
for i, query in enumerate(queries):
print(f"Query: {query}")
for j, doc in enumerate(documents):
print(f" Similarity: {scores[i, j]:.4f} | Document {j}: {doc[:80]}...")
# Query: What is machine learning?
# Similarity: 0.9063 | Document 0: Machine learning is a subset of ...
# Similarity: 0.7287 | Document 1: Neural networks are trained ...
#
# Query: How does neural network training work?
# Similarity: 0.6725 | Document 0: Machine learning is a subset of ...
# Similarity: 0.8287 | Document 1: Neural networks are trained ...
```
## Transformers Usage
See [here](https://huggingface.co/MongoDB/mdbr-leaf-mt/blob/main/transformers_example_mt.ipynb).
## Asymmetric Retrieval Setup
> [!Note]
> **Note**: a version of this asymmetric setup, conveniently packaged into a single model, is [available here](https://huggingface.co/MongoDB/mdbr-leaf-mt-asym).
`mdbr-leaf-mt` is *aligned* to [`mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1), the model it has been distilled from, making the asymmetric system below possible:
```python
# Use mdbr-leaf-mt for query encoding (real-time, low latency)
query_model = SentenceTransformer("MongoDB/mdbr-leaf-mt")
query_embeddings = query_model.encode(queries, prompt_name="query")
# Use a larger model for document encoding (one-time, at index time)
doc_model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
document_embeddings = doc_model.encode(documents)
# Compute similarities
scores = query_model.similarity(query_embeddings, document_embeddings)
```
Retrieval results from asymmetric mode are usually superior to the [standard mode above](#sentence-transformers).
## MRL Truncation
Embeddings have been trained via [MRL](https://arxiv.org/abs/2205.13147) and can be truncated for more efficient storage:
```python
query_embeds = model.encode(queries, prompt_name="query", truncate_dim=256)
doc_embeds = model.encode(documents, truncate_dim=256)
similarities = model.similarity(query_embeds, doc_embeds)
print('After MRL:')
print(f"* Embeddings dimension: {query_embeds.shape[1]}")
print(f"* Similarities: \n\t{similarities}")
# After MRL:
# * Embeddings dimension: 256
# * Similarities:
# tensor([[0.9164, 0.7219],
# [0.6682, 0.8393]], device='cuda:0')
```
## Vector Quantization
Vector quantization, for example to `int8` or `binary`, can be performed as follows:
**Note**: For vector quantization to types other than binary, we suggest performing a calibration to determine the optimal ranges, [see here](https://sbert.net/examples/sentence_transformer/applications/embedding-quantization/README.html#scalar-int8-quantization).
Good initial values are -1.0 and +1.0.
```python
from sentence_transformers.quantization import quantize_embeddings
import torch
query_embeds = model.encode(queries, prompt_name="query")
doc_embeds = model.encode(documents)
# Quantize embeddings to int8 using -1.0 and +1.0
ranges = torch.tensor([[-1.0], [+1.0]]).expand(2, query_embeds.shape[1]).cpu().numpy()
query_embeds = quantize_embeddings(query_embeds, "int8", ranges=ranges)
doc_embeds = quantize_embeddings(doc_embeds, "int8", ranges=ranges)
# Calculate similarities; cast to int64 to avoid under/overflow
similarities = query_embeds.astype(int) @ doc_embeds.astype(int).T
print('After quantization:')
print(f"* Embeddings type: {query_embeds.dtype}")
print(f"* Similarities: \n{similarities}")
# After quantization:
# * Embeddings type: int8
# * Similarities:
# [[2202032 1422868]
# [1421197 1845580]]
```
# Evaluation
Please [see here](https://huggingface.co/MongoDB/mdbr-leaf-mt/blob/main/evaluate_models.ipynb).
# Citation
If you use this model in your work, please cite:
```bibtex
@misc{mdbr_leaf,
title={LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations},
author={Robin Vujanic and Thomas Rueckstiess},
year={2025},
eprint={2509.12539},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2509.12539},
}
```
# License
This model is released under Apache 2.0 License.
# Contact
For questions or issues, please open an issue or pull request. You can also contact the MongoDB ML Research team at [email protected].
|
mogam-ai/CDS-BART-denoising
|
mogam-ai
| 2025-09-18T04:10:34Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-05-29T08:01:03Z |
---
license: mit
base_model:
- FacebookAI/BART
pipeline_tag: feature-extraction
library_name: transformers
---
# CDS-BART
CDS-BART is designed as an easy-to-use tool, facilitating accessibility for researchers to leverage the development of mRNA vaccines and therapeutics.
The model is based on **BART** and pre-trained with mRNA data contains nine taxonomic groups provided by the **NCBI RefSeq database**.
It is a BART-based foundation model that can be fine-tuned for various mRNA downstream tasks such as mRFP expression, mRNA stability.
### Model Description
- **Developed by:** Jadamba Erkhembayar, Sangheon Lee, Hyunjin Shin, Hyekyoung Lee, Jinhee Hong
- **Funded by :** Mogam institute for biomedical research
- **Model type:** BART
- **Trained Database:** NCBI RefSeq
- **License:** MIT License
## Load tokenizer and model
The example code for loading pre-trained denoising model and tokenzier.
BartModel has pre-trained for denoising and sequence representation tasks.
```python
from transformers import (
BartTokenizerFast,
BartModel,
)
# Load tokenizer
tokenizer = BartTokenizerFast.from_pretrained("mogam-ai/CDS-BART-denoising")
# Load pre-trained model
model = BartModel.from_pretrained("mogam-ai/CDS-BART-denoising")
```
## Example code
```python
example_sequences = [
'ACGCGAGCGUCAUUUCGCGGGGCAUAUGUA'
]
encoded = tokenizer(
example_sequences,
max_length=850,
truncation=True,
padding="max_length",
return_tensors="pt"
)
output = model(
input_ids = encoded['input_ids'],
attention_mask = encoded['attention_mask']
)
hidden_states = output.last_hidden_state
```
Can add more here!
- Maximum length of tokenizer is 850 and maximum mRNA sequence is around 4000nt.
- It is available to extract sequence embeddings from the model.
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/mogam-ai/CDS-BART
- **Paper :** More Information can add later
-
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.01_1280_5
|
winnieyangwannan
| 2025-09-18T04:09:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T04:08:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bustamiyusoef/DANN_JW_OnlyJWAugmentWithJHR
|
bustamiyusoef
| 2025-09-18T04:08:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"base_model:MohamedRashad/arabic-small-nougat",
"base_model:finetune:MohamedRashad/arabic-small-nougat",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T04:07:37Z |
---
library_name: transformers
license: gpl-3.0
base_model: MohamedRashad/arabic-small-nougat
tags:
- generated_from_trainer
model-index:
- name: DANN_JW_OnlyJWAugmentWithJHR
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. -->
# DANN_JW_OnlyJWAugmentWithJHR
This model is a fine-tuned version of [MohamedRashad/arabic-small-nougat](https://huggingface.co/MohamedRashad/arabic-small-nougat) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1478
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6599 | 1.0 | 101 | 0.3251 |
| 1.2198 | 2.0 | 202 | 0.1840 |
| 0.9112 | 3.0 | 303 | 0.1631 |
| 0.8044 | 4.0 | 404 | 0.1529 |
| 0.6899 | 5.0 | 505 | 0.1478 |
| 0.7779 | 6.0 | 606 | 0.1473 |
| 0.5956 | 7.0 | 707 | 0.1484 |
| 0.5914 | 8.0 | 808 | 0.1467 |
| 0.5992 | 9.0 | 909 | 0.1440 |
| 0.6267 | 10.0 | 1010 | 0.1490 |
| 0.5742 | 11.0 | 1111 | 0.1458 |
| 0.5247 | 12.0 | 1212 | 0.1477 |
| 0.5237 | 13.0 | 1313 | 0.1478 |
| 0.5904 | 14.0 | 1414 | 0.1478 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 4.1.0
- Tokenizers 0.21.0
|
CompassioninMachineLearning/Basellama_plus1kfullaiMiles_plus20kfinetune
|
CompassioninMachineLearning
| 2025-09-18T04:02:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:CompassioninMachineLearning/Basellama_plus1kfullaiMiles",
"base_model:finetune:CompassioninMachineLearning/Basellama_plus1kfullaiMiles",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T03:56:32Z |
---
base_model: CompassioninMachineLearning/Basellama_plus1kfullaiMiles
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** CompassioninMachineLearning
- **License:** apache-2.0
- **Finetuned from model :** CompassioninMachineLearning/Basellama_plus1kfullaiMiles
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)
|
godnpeter/lerobot_dy_raw_trainscript_configname_loggingdir
|
godnpeter
| 2025-09-18T04:02:10Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:aopolin-lv/libero_spatial_no_noops_lerobot_v21",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-18T04:02:00Z |
---
base_model: lerobot/smolvla_base
datasets: aopolin-lv/libero_spatial_no_noops_lerobot_v21
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- lerobot
- smolvla
- robotics
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO-0914191432-epoch-5
|
vectorzhou
| 2025-09-18T03:59:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:54:03Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) 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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO-0914191432-epoch-5", 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/zrl_csl_nlhf/nlhf/runs/49ov7fdo)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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}}
}
```
|
gumperto/Llama-3.2-3B-Instruct-emergent-finetune-tests_samples-all-full-r32
|
gumperto
| 2025-09-18T03:58:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T03:34:54Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: transformers
model_name: Llama-3.2-3B-Instruct-emergent-finetune-tests_samples-all-full-r32
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.2-3B-Instruct-emergent-finetune-tests_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-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="gumperto/Llama-3.2-3B-Instruct-emergent-finetune-tests_samples-all-full-r32", 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/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Clem2323/bert-finetuned-ner
|
Clem2323
| 2025-09-18T03:56:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-09-18T02:26:14Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0719
- Precision: 0.9372
- Recall: 0.9525
- F1: 0.9448
- Accuracy: 0.9872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0215 | 1.0 | 1874 | 0.0657 | 0.9188 | 0.9423 | 0.9304 | 0.9843 |
| 0.0152 | 2.0 | 3748 | 0.0640 | 0.9330 | 0.9498 | 0.9414 | 0.9866 |
| 0.0064 | 3.0 | 5622 | 0.0719 | 0.9372 | 0.9525 | 0.9448 | 0.9872 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
quickmt/quickmt-pl-en
|
quickmt
| 2025-09-18T03:51:02Z | 0 | 0 | null |
[
"translation",
"en",
"pl",
"dataset:quickmt/quickmt-train.pl-en",
"license:cc-by-4.0",
"model-index",
"region:us"
] |
translation
| 2025-09-17T23:50:20Z |
---
language:
- en
- pl
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.pl-en
model-index:
- name: quickmt-pl-en
results:
- task:
name: Translation pol-eng
type: translation
args: pol-eng
dataset:
name: flores101-devtest
type: flores_101
args: ell_Grek eng_Latn devtest
metrics:
- name: BLEU
type: bleu
value: 27.46
- name: CHRF
type: chrf
value: 57.18
- name: COMET
type: comet
value: 85.04
---
# `quickmt-pl-en` Neural Machine Translation Model
`quickmt-pl-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `pl` into `en`.
## Try it on our Huggingface Space
Give it a try before downloading here: https://huggingface.co/spaces/quickmt/QuickMT-Demo
## Model Information
* Trained using [`eole`](https://github.com/eole-nlp/eole)
* 195M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
* 20k separate Sentencepiece vocabs
* Expested for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.pl-en/tree/main
See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model.
## Usage with `quickmt`
You must install the Nvidia cuda toolkit first, if you want to do GPU inference.
Next, install the `quickmt` python library and download the model:
```bash
git clone https://github.com/quickmt/quickmt.git
pip install ./quickmt/
quickmt-model-download quickmt/quickmt-pl-en ./quickmt-pl-en
```
Finally use the model in python:
```python
from quickmt impest Translator
# Auto-detects GPU, set to "cpu" to force CPU inference
t = Translator("./quickmt-pl-en/", device="auto")
# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'Dr Ehud Ur, będący profesorem medycyny na Uniwersytecie Dalhousie w Halifaxie w Nowej Szkocji oraz przewodniczącym oddziału klinicznego i naukowego Kanadyjskiego Stowarzyszenia Cukrzycy, przestrzegł, iż badania nadal dopiero się zaczynają.'
t(sample_text, beam_size=5)
```
> 'Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and chairman of the clinical and scientific division of the Canadian Diabetes Association, warned that research is still just beginning.'
```python
# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)
```
> 'Professor of Medicine at Dalhous University Halifax in Nova Scotia, MD and Chair of the Canadian Diabetes Association’s Clinical and Scientific Division, cautioned that research is just beginning.'
The model is in `ctranslate2` format, and the tokenizers are `sentencepiece`, so you can use `ctranslate2` directly instead of through `quickmt`. It is also possible to get this model to work with e.g. [LibreTranslate](https://libretranslate.com/) which also uses `ctranslate2` and `sentencepiece`. A model in safetensors format to be used with `eole` is also provided.
## Metrics
`bleu` and `chrf2` are calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("pol_Latn"->"eng_Latn"). `comet22` with the [`comet`](https://github.com/Unbabel/COMET) library and the [default model](https://huggingface.co/Unbabel/wmt22-comet-da). "Time (s)" is the time in seconds to translate the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a larger batch size).
| | bleu | chrf2 | comet22 | Time (s) |
|:---------------------------------|-------:|--------:|----------:|-----------:|
| quickmt/quickmt-pl-en | 27.46 | 57.18 | 85.04 | 1.46 |
| Helsinki-NLP/opus-mt-pl-en | 25.55 | 55.39 | 83.8 | 4.01 |
| facebook/nllb-200-distilled-600M | 29.28 | 57.11 | 84.65 | 21.61 |
| facebook/nllb-200-distilled-1.3B | 30.99 | 58.77 | 86.04 | 37.64 |
| facebook/m2m100_418M | 22.12 | 52.51 | 80.41 | 17.99 |
| facebook/m2m100_1.2B | 27.13 | 56.36 | 84.48 | 35.01 |
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.001_6400_5
|
winnieyangwannan
| 2025-09-18T03:47:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T03:45:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TungCan/tuning-sentiment-abp-pos-neg
|
TungCan
| 2025-09-18T03:37:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"vietnamese",
"sentiment-analysis",
"generated_from_trainer",
"base_model:TungCan/tuning-sentiment-abp-pos-neg",
"base_model:finetune:TungCan/tuning-sentiment-abp-pos-neg",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T02:17:17Z |
---
library_name: transformers
base_model: TungCan/tuning-sentiment-abp-pos-neg
tags:
- text-classification
- vietnamese
- sentiment-analysis
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: tuning-sentiment-abp-pos-neg
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. -->
# tuning-sentiment-abp-pos-neg
This model is a fine-tuned version of [TungCan/tuning-sentiment-abp-pos-neg](https://huggingface.co/TungCan/tuning-sentiment-abp-pos-neg) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9952
- Accuracy: 0.6892
- F1: 0.7495
- Precision: 0.7517
- Recall: 0.7474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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 | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 387 | 0.5820 | 0.6994 | 0.7588 | 0.7572 | 0.7613 |
| 0.3956 | 2.0 | 774 | 0.6367 | 0.7430 | 0.7868 | 0.7988 | 0.7929 |
| 0.3357 | 3.0 | 1161 | 0.7205 | 0.7234 | 0.7728 | 0.7863 | 0.7738 |
| 0.2778 | 4.0 | 1548 | 0.8257 | 0.6962 | 0.7561 | 0.7644 | 0.7495 |
| 0.2778 | 5.0 | 1935 | 0.9952 | 0.6892 | 0.7495 | 0.7517 | 0.7474 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Prompt-Guard/PromptGuard_weights
|
Prompt-Guard
| 2025-09-18T03:32:56Z | 0 | 0 | null |
[
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:apache-2.0",
"region:us"
] | null | 2025-03-27T07:23:32Z |
---
license: apache-2.0
base_model:
- CompVis/stable-diffusion-v1-4
---
Here are the official released weights of **PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models**.
You could check our project page at [🏠PromptGuard HomePage](https://prompt-guard.github.io/) and the GitHub repo at [⚙️PromptGuard GitHub](https://github.com/lingzhiyxp/PromptGuard) where we released the code.
In the future, we will release our training datasets.
# Inference
A simple use case of our model is:
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
# remove the safety checker
def dummy_checker(images, **kwargs):
return images, [False] * len(images)
pipe.safety_checker = dummy_checker
safety_embedding_list = [${embedding_path_1}, ${embedding_path_2}, ...] # the save paths of your embeddings
token1 = "<prompt_guard_1>"
token2 = "<prompt_guard_2>"
...
token_list = [token1, token2, ...] # the corresponding tokens of your embeddings
pipe.load_textual_inversion(pretrained_model_name_or_path=safe_embedding_list, token=token_list)
origin_prompt = "a photo of a dog"
prompt_with_system = origin_prompt + " " + token1 + " " + token2 + ...
image = pipe(prompt).images[0]
image.save("example.png")
```
To get a better balance between unsafe content moderation and benign content preservation, we recommend you to load Sexual, Political and Disturbing these three safe embeddings.
|
GreatCaptainNemo/ProLLaMA
|
GreatCaptainNemo
| 2025-09-18T03:32:53Z | 2,017 | 17 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"arxiv:2402.16445",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T07:48:43Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---
# ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
[Paper on arxiv](https://arxiv.org/abs/2402.16445) for more information
[Github](https://github.com/Lyu6PosHao/ProLLaMA) for more information
ProLLaMA is based on Llama-2-7b, so please follow the license of Llama2.
# Input Format:
The instructions which you input to the model should follow the following format:
```text
[Generate by superfamily] Superfamily=<xxx>
or
[Determine superfamily] Seq=<yyy>
```
Here are some examples of the input:
```text
[Generate by superfamily] Superfamily=<Ankyrin repeat-containing domain superfamily>
```
```
#You can also specify the first few amino acids of the protein sequence:
[Generate by superfamily] Superfamily=<Ankyrin repeat-containing domain superfamily> Seq=<MKRVL
```
```
[Determine superfamily] Seq=<MAPGGMPREFPSFVRTLPEADLGYPALRGWVLQGERGCVLYWEAVTEVALPEHCHAECWGVVVDGRMELMVDGYTRVYTRGDLYVVPPQARHRARVFPGFRGVEHLSDPDLLPVRKR>
```
**See [this](https://github.com/Lyu6PosHao/ProLLaMA/blob/main/superfamilies.txt) on all the optional superfamilies.**
# Quick usage:
```bash
# you can replace the model_path with your local path
CUDA_VISIBLE_DEVICES=0 python main.py --model "GreatCaptainNemo/ProLLaMA" --interactive
# main.py is as follows 👇:
```
```python
import argparse
import json, os
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from transformers import GenerationConfig
from tqdm import tqdm
generation_config = GenerationConfig(
temperature=0.2,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.2,
max_new_tokens=400
)
parser = argparse.ArgumentParser()
parser.add_argument('--model', default=None, type=str,help="The local path of the model. If None, the model will be downloaded from HuggingFace")
parser.add_argument('--interactive', action='store_true',help="If True, you can input instructions interactively. If False, the input instructions should be in the input_file.")
parser.add_argument('--input_file', default=None, help="You can put all your input instructions in this file (one instruction per line).")
parser.add_argument('--output_file', default=None, help="All the outputs will be saved in this file.")
args = parser.parse_args()
if __name__ == '__main__':
if args.interactive and args.input_file:
raise ValueError("interactive is True, but input_file is not None.")
if (not args.interactive) and (args.input_file is None):
raise ValueError("interactive is False, but input_file is None.")
if args.input_file and (args.output_file is None):
raise ValueError("input_file is not None, but output_file is None.")
load_type = torch.bfloat16
if torch.cuda.is_available():
device = torch.device(0)
else:
raise ValueError("No GPU available.")
model = LlamaForCausalLM.from_pretrained(
args.model,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
quantization_config=None
)
tokenizer = LlamaTokenizer.from_pretrained(args.model)
model.eval()
with torch.no_grad():
if args.interactive:
while True:
raw_input_text = input("Input:")
if len(raw_input_text.strip())==0:
break
input_text = raw_input_text
input_text = tokenizer(input_text,return_tensors="pt")
generation_output = model.generate(
input_ids = input_text["input_ids"].to(device),
attention_mask = input_text['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
generation_config = generation_config,
output_attentions=False
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
print("Output:",output)
print("\n")
else:
outputs=[]
with open(args.input_file, 'r') as f:
examples =f.read().splitlines()
print("Start generating...")
for index, example in tqdm(enumerate(examples),total=len(examples)):
input_text = tokenizer(example,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = input_text["input_ids"].to(device),
attention_mask = input_text['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
generation_config = generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
outputs.append(output)
with open(args.output_file,'w') as f:
f.write("\n".join(outputs))
print("All the outputs have been saved in",args.output_file)
```
# Citation:
```
@article{lv2025prollama,
title={Prollama: A protein large language model for multi-task protein language processing},
author={Lv, Liuzhenghao and Lin, Zongying and Li, Hao and Liu, Yuyang and Cui, Jiaxi and Chen, Calvin Yu-Chian and Yuan, Li and Tian, Yonghong},
journal={IEEE Transactions on Artificial Intelligence},
year={2025},
publisher={IEEE}
}
```
|
ShuaiYang03/instructvla_pretraining_v2_libero_10_wrist-image_aug
|
ShuaiYang03
| 2025-09-18T03:29:33Z | 19 | 0 | null |
[
"vision-language-model",
"manipulation",
"robotics",
"dataset:IPEC-COMMUNITY/libero_10_no_noops_1.0.0_lerobot",
"base_model:nvidia/Eagle2-2B",
"base_model:finetune:nvidia/Eagle2-2B",
"region:us"
] |
robotics
| 2025-09-09T02:45:24Z |
---
datasets:
- IPEC-COMMUNITY/libero_10_no_noops_1.0.0_lerobot
base_model:
- nvidia/Eagle2-2B
tags:
- vision-language-model
- manipulation
- robotics
pipeline_tag: robotics
---
# Model Card for InstructVLA LIBERO-10
- checkpoints: the model in `.pt` format
- eval: the evaluation results with 3 random seeds
- dataset_statistics.json: the normalization statistics for the dataset
## Evaluation:
```bash
#!/bin/bash
CKPT_LIST=(
"path/to/checkpoints/step-025500-epoch-64-loss=0.0361.pt"
)
# Loop over the checkpoint list and GPUs
for i in "${!CKPT_LIST[@]}"; do
GPU_ID=$((i % 8)) # Cycle through GPUs 0-7
CHECKPOINT="${CKPT_LIST[$i]}"
# Run the evaluation script for each checkpoint and GPU
CUDA_VISIBLE_DEVICES=$GPU_ID python deploy/libero/run_libero_eval.py \
--model_family instruct_vla \
--pretrained_checkpoint "$CHECKPOINT" \
--task_suite_name libero_10 \
--local_log_dir Libero/release_ensemble \
--use_length -1 \
--center_crop True &
# --use_length == -1 : execute the ensembled action
# --use_length >= 1 : execute action_chunk[0:use_length]
# For this checkpoint, you should use action ensemble.
sleep 5
done
# Wait for all background jobs to finish
wait
```
|
mistisunala/blockassist
|
mistisunala
| 2025-09-18T03:24:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering opaque walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T02:23:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering opaque walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CompassioninMachineLearning/Basellama_plus1kmedaijazz_plus20kfinetune
|
CompassioninMachineLearning
| 2025-09-18T03:24:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:CompassioninMachineLearning/Basellama_plus1kmedaijazz",
"base_model:finetune:CompassioninMachineLearning/Basellama_plus1kmedaijazz",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T03:18:45Z |
---
base_model: CompassioninMachineLearning/Basellama_plus1kmedaijazz
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** CompassioninMachineLearning
- **License:** apache-2.0
- **Finetuned from model :** CompassioninMachineLearning/Basellama_plus1kmedaijazz
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)
|
lihaoxin2020/llama3.1-instruct-synthetic_1_stem_only
|
lihaoxin2020
| 2025-09-18T03:15:42Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T10:29:43Z |
---
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 🤗 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]
|
lihaoxin2020/llama3.1-instruct-synthetic_1
|
lihaoxin2020
| 2025-09-18T03:15:22Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-14T10:29:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lihaoxin2020/qwen-instruct-synthetic_1_math_only
|
lihaoxin2020
| 2025-09-18T03:13:50Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-14T09:44:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### 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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758165127
|
schooncestiaa
| 2025-09-18T03:13:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T03:13:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sheryc/Llama-3.1-8B-Instruct-CARE
|
sheryc
| 2025-09-18T03:12:17Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:sheryc/hotpotqa_care",
"dataset:sheryc/DROP_care",
"dataset:sheryc/ms_marco_care",
"arxiv:2509.13683",
"arxiv:2407.21783",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-12T21:45:14Z |
---
library_name: transformers
license: llama3.1
datasets:
- sheryc/hotpotqa_care
- sheryc/DROP_care
- sheryc/ms_marco_care
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
---
# Llama-3.1-8B-Instruct-CARE
<div align="center">
<a href='https://arxiv.org/abs/2509.13683'><img src='https://img.shields.io/badge/ArXiv-red?logo=arxiv'></a>
<a href='https://github.com/FoundationAgents/CARE'><img src='https://img.shields.io/badge/_Code-CARE-181717?color=121717&logo=github&logoColor=whitee'></a>
<a href='https://foundationagents.github.io/CARE/'><img src='https://img.shields.io/badge/%F0%9F%92%BB_Project-CARE-blue'></a>
</div>
## Model Card for Llama-3.1-8B-Instruct-CARE
### Model Description
Llama-3.1-8B-Instruct-CARE is an 8B parameter instruction-tuned language model based on meta-llama/Meta-Llama-3.1-8B-Instruct, enhanced with native retrieval-augmented reasoning capabilities through the CARE (Context-Aware Retrieval-Enhanced reasoning) framework. This model has been specifically trained to improve context fidelity and reduce hallucinations by teaching the model to explicitly integrate in-context evidence within its reasoning process.
**Key Features:**
- **Native retrieval-augmented reasoning**: Dynamically identifies and incorporates relevant evidence from input context
- **Improved context fidelity**: Significantly better adherence to provided context, especially when it contradicts parametric knowledge
- **Enhanced multi-hop reasoning**: Superior performance on complex reasoning tasks requiring evidence integration
- **Structured reasoning output**: Generates reasoning chains with explicit evidence citations using `<think>` and `<retrieval>` tags
### Model Details
- **Model Type**: Causal Language Model (Enhanced with Retrieval-Augmented Reasoning)
- **Base Model**: meta-llama/Meta-Llama-3.1-8B-Instruct
- **Parameters**: 8B total
- **Architecture**: Transformer with optimized architecture (GQA, RoPE)
- **Context Length**: 128,000 tokens
- **Training Framework**: Two-phase training (SFT + Reinforcement Learning with GRPO)
### Training Process
The model was trained using a novel two-phase approach:
**Phase 1 - Supervised Fine-Tuning (SFT):**
- Dataset: 7,739 instances from HotpotQA with retrieval-augmented reasoning chains
- Purpose: Establish evidence integration patterns and reasoning format
- Training: 3 epochs with LoRA (r=8, α=16), AdamW optimizer
**Phase 2 - Reinforcement Learning:**
- Method: Group Relative Policy Optimization (GRPO)
- Curriculum Learning: Gradual transition from DROP (easy) to MS MARCO (hard)
- Rewards: Accuracy + Format + Retrieval consistency
- Training: 350 steps with multi-aspect reward optimization
### System Prompt
The model uses an enhanced system prompt that enables structured reasoning with evidence retrieval:
```
You are a helpful assistant. You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. WITHIN the thinking process, make reference to the relevant texts in the prompt that provide critical information to move the reasoning process forward. The referenced texts MUST BE enclosed within <retrieval> </retrieval> tags, and MUST BE placed within the reasoning process only. The final answer MUST BE put at the end of the response after "Answer:".
```
**Note**: This system prompt is automatically applied when using the default chat template.
### Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "sheryc/Llama-3.1-8B-Instruct-CARE"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Example usage
context = """John went to the movies with his mom last week. They watched the latest superhero movie, which was quite popular. The ticket price was $15. According to the local cinema's website, ticket prices range from $10 to $12 for regular screenings and from $13 to $16 for special releases."""
question = "Was the ticket price John's mom paid for the movie reasonable?"
messages = [
{"role": "user", "content": f"{question}\n\nContext:{context}"}
]
tokenized_chat = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
generated_ids = model.generate(tokenized_chat.to(model.device), max_new_tokens=512)
output_text = tokenizer.decode(generated_ids[0])
```
**Expected Output Format:**
```
<think>
The context states John watched the latest superhero movie. <retrieval>The ticket price was $15.</retrieval> The context provides price ranges: <retrieval>ticket prices range from $10 to $12 for regular screenings and from $13 to $16 for special releases.</retrieval> Since this was a popular latest superhero movie, it likely qualifies as a special release. Therefore, the $15 price falls within the $13-$16 range for special releases.
</think>
Answer: Yes, the ticket price was reasonable.
```
### Training Data
- **SFT Phase**: HotpotQA with labeled supporting facts (7,739 instances)
- **RL Phase**:
- DROP dataset (77,409 training instances) - Easy curriculum phase
- MS MARCO - Hard curriculum phase
- **Evaluation**: LongBench, CofCA, and other QA benchmarks
### License
This model is licensed under the LLaMA 3.1 Community License. Please refer to the original LLaMA 3.1 license terms.
### Citation
```bibtex
@inproceedings{wang2025care,
title={Improving Context Fidelity via Native Retrieval-Augmented Reasoning},
author={Wang, Suyuchen and Wang, Jinlin and Wang, Xinyu and Li, Shiqi and Tang, Xiangru and Hong, Sirui and Chang, Xiao-Wen and Wu, Chenglin and Liu, Bang},
booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
year={2025}
}
```
And the original [Llama 3 series paper](https://arxiv.org/abs/2407.21783).
### Contact
For questions about the model or to report issues, please visit the [CARE project homepage](https://foundationagents.github.io/CARE/) or contact the authors.
|
Infinigence/Megrez2-3x7B-A3B-GGUF
|
Infinigence
| 2025-09-18T03:09:52Z | 509 | 1 | null |
[
"gguf",
"moe",
"conversational",
"text-generation",
"en",
"zh",
"arxiv:2507.17728",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T15:02:07Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- moe
- conversational
---
<div align="center">
<img src="./assets/megrez-logo.png" alt="Megrez Logo" width="400" />
<br>
<h1> Megrez2-3x7B-A3B </h1>
<a href="https://github.com/infinigence/Infini-Megrez">
<b>🔗 Github</b>
</a> |
<a href="https://github.com/infinigence/Infini-Megrez/blob/main/docs/tech_report.pdf">
<b>📄 Tech Report</b>
</a> |
<a href="https://huggingface.co/spaces/Infinigence/Megrez2-3x7B-A3B">
<b>💻 Demo</b>
</a> |
<a href="https://huggingface.co/Infinigence/Megrez2-3x7B-A3B/blob/main/assets/wechat-official.jpg">
<b>💬 WeChat Official</b>
</a>
<br>
<strong>[中文](https://huggingface.co/Infinigence/Megrez2-3x7B-A3B-GGUF/blob/main/README_ZH.md) | English</strong>
</div>
## Introduction
Megrez2-3x7B-A3B is a device native large language model. Megrez2 takes advantages of both the accuracy of Mixture-of-Experts (MoE) architecture and the compact size of Dense models. This release model was trained on 8T Tokens of data. In the future, we plan to improve the model's reasoning and agent capabilities.
## Model Card
<div align="center">
| | |
|:---:|:---:|
| **Architecture** | Mixture-of-Experts (MoE) |
| **Total Parameters** | 3x7B |
| **Activated Parameters** | 3B |
| **Experts Shared Frequency**| 3 |
| **Number of Layers** (Dense layer included) | 31 |
| **Number of Dense Layers** | 1 |
| **Attention Hidden Dimension** | 2048 |
| **MoE Hidden Dimension** (per Expert) | 1408 |
| **Number of Attention Heads** | 16 |
| **Number of Experts** | 64 |
| **Selected Experts per Token** | 6 |
| **Number of Shared Experts** | 4 |
| **Vocabulary Size** | 128,880 |
| **Context Length** | 32K |
| **Base Frequency of RoPE** | 5,000,000 |
| **Attention Mechanism** | GQA |
| **Activation Function** | SwiGLU |
</div>
## Performance
We evaluated Megrez2-3x7B-A3B using the open-source evaluation tool [OpenCompass](https://github.com/open-compass/opencompass) on several important benchmarks. Some of the evaluation results are shown in the table below.
<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center">Metric</th>
<th align="center"><sup>Megrez2-3x7B<br>-A3B</sup></th>
<th align="center"><sup>Megrez2-3x7B<br>-A3B-Preview</sup></th>
<th align="center"><sup>SmallThinker-21B<br>-A3B-Instruct</sup></th>
<th align="center"><sup>Qwen3-30B-A3B</sup></th>
<th align="center"><sup>Qwen3-8B</sup></th>
<th align="center"><sup>Qwen3-4B<br>-Instruct-2507</sup></th>
<th align="center"><sup>Phi4-14B<br>(nothink)</sup></th>
<th align="center"><sup>Gemma3-12B</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Activate Params (B)</td>
<td align="center"></td>
<td align="center">3.0</td>
<td align="center">3.0</td>
<td align="center">3.0</td>
<td align="center">3.3</td>
<td align="center">8.2</td>
<td align="center">4.0</td>
<td align="center">14.7</td>
<td align="center">12.2</td>
</tr>
<tr>
<td align="center">Stored Params (B)</td>
<td align="center"></td>
<td align="center">7.5</td>
<td align="center">7.5</td>
<td align="center">21.5</td>
<td align="center">30.5</td>
<td align="center">8.2</td>
<td align="center">4.0</td>
<td align="center">14.7</td>
<td align="center">12.2</td>
</tr>
<tr>
<td align="center">MMLU</td>
<td align="center">EM</td>
<td align="center">85.4</td>
<td align="center"><strong>87.5</strong></td>
<td align="center">84.4</td>
<td align="center">85.1</td>
<td align="center">81.8</td>
<td align="center">-</td>
<td align="center">84.6</td>
<td align="center">78.5</td>
</tr>
<tr>
<td align="center">GPQA</td>
<td align="center">EM</td>
<td align="center"><strong>58.8</strong></td>
<td align="center">28.8</td>
<td align="center">55.0</td>
<td align="center">44.4</td>
<td align="center">38.9</td>
<td align="center">62</td>
<td align="center">55.5</td>
<td align="center">34.9</td>
</tr>
<tr>
<td align="center">IFEval</td>
<td align="center">Inst<br>loose</td>
<td align="center"><strong>87.7</strong></td>
<td align="center">80.2</td>
<td align="center">85.8</td>
<td align="center">84.3</td>
<td align="center">83.9</td>
<td align="center">83.4</td>
<td align="center">63.2</td>
<td align="center">74.7</td>
</tr>
<tr>
<td align="center">MATH-500</td>
<td align="center">EM</td>
<td align="center"><strong>87.2</strong></td>
<td align="center">81.6</td>
<td align="center">82.4</td>
<td align="center">84.4</td>
<td align="center">81.6</td>
<td align="center">-</td>
<td align="center">80.2</td>
<td align="center">82.4</td>
</tr>
</tbody>
</table>
</div>
## How to Run
### llama.cpp
llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. Now supported, please refer to the [support-megrez branch](https://github.com/infinigence/llama.cpp/tree/support-megrez) for details.
Under the FP16 floating-point precision configuration, the performance of the current model on code tasks has decreased compared to the original model. We have launched optimization efforts to address this issue and are currently exploring solutions.
## Best Practice
To achieve optimal performance, we recommend the following settings:
1. Sampling Parameters: we suggest using Temperature=0.7 and TopP=0.9 .
2. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
* Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
* Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."
## License Agreement
All our open-weight models are licensed under Apache 2.0.
## Citation
If you find our work helpful, feel free to give us a cite.
```bibtex
@misc{li2025megrez2technicalreport,
title={Megrez2 Technical Report},
author={Boxun Li and Yadong Li and Zhiyuan Li and Congyi Liu and Weilin Liu and Guowei Niu and Zheyue Tan and Haiyang Xu and Zhuyu Yao and Tao Yuan and Dong Zhou and Yueqing Zhuang and Bo Zhao and Guohao Dai and Yu Wang},
year={2025},
eprint={2507.17728},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.17728},
}
```
## Contact
If you have any questions, please feel free to submit a GitHub issue or contact [WeChat groups](https://huggingface.co/Infinigence/Megrez2-3x7B-A3B/blob/main/assets/wechat-group.jpg).
|
webnn/yolov8n
|
webnn
| 2025-09-18T03:04:01Z | 7 | 0 |
ultralytics
|
[
"ultralytics",
"onnx",
"tracking",
"instance-segmentation",
"image-classification",
"pose-estimation",
"obb",
"object-detection",
"yolo",
"yolov8",
"license:agpl-3.0",
"region:us"
] |
object-detection
| 2025-03-21T03:07:13Z |
---
license: agpl-3.0
pipeline_tag: object-detection
tags:
- ultralytics
- tracking
- instance-segmentation
- image-classification
- pose-estimation
- obb
- object-detection
- yolo
- yolov8
library_name: ultralytics
---
[Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
This is an ONNX version of https://huggingface.co/Ultralytics/YOLOv8 modified for the usage of [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library.
|
madout/jarvesv1
|
madout
| 2025-09-18T02:59:33Z | 0 | 0 | null |
[
"text-to-speech",
"ja",
"en",
"arxiv:2509.06942",
"base_model:microsoft/VibeVoice-1.5B",
"base_model:finetune:microsoft/VibeVoice-1.5B",
"license:mit",
"region:us"
] |
text-to-speech
| 2025-09-18T02:56:31Z |
---
license: mit
language:
- ja
- en
base_model:
- microsoft/VibeVoice-1.5B
pipeline_tag: text-to-speech
---
---
license: mit
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license: mit
---vv
from diffusers import FluxPipeline
from safetensors.torch import load_file
prompt='The Death of Ophelia by John Everett Millais, Pre-Raphaelite painting, Ophelia floating in a river surrounded by flowers, detailed natural elements, melancholic and tragic atmosphere'
pipe = FluxPipeline.from_pretrained('./data/flux',
torch_dtype=torch.bfloat16,
use_safetensors=True
).to("cuda")
state_dict = load_file("./srpo/diffusion_pytorch_model.safetensors")
pipe.transformer.load_state_dict(state_dict)
image = pipe(
prompt,
guidance_scale=3.5,
height=1024,
width=1024,
num_inference_steps=50,
max_sequence_length=512,
generator=generator
).images[0]
@misc{shen2025directlyaligningdiffusiontrajectory,
title={Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference},
author={Xiangwei Shen and Zhimin Li and Zhantao Yang and Shiyi Zhang and Yingfang Zhang and Donghao Li and Chunyu Wang and Qinglin Lu and Yansong Tang},
year={2025},
eprint={2509.06942},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2509.06942},
}
|
ZZZ1223/vlm_rl_checkpoint-27200
|
ZZZ1223
| 2025-09-18T02:58:47Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T02:42:03Z |
---
license: apache-2.0
---
|
cuongdk253/gemma3-27b-bnb-4bit
|
cuongdk253
| 2025-09-18T02:49:17Z | 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
| 2025-09-18T02:42:44Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Metrics
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### Results
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#### Summary
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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]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.005_1280_3
|
winnieyangwannan
| 2025-09-18T02:46:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T02:45:38Z |
---
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 🤗 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]
|
perisoelusive/Test1
|
perisoelusive
| 2025-09-18T02:43:24Z | 0 | 0 |
keras
|
[
"keras",
"region:us"
] | null | 2025-09-18T02:27:49Z |
---
title: Romero
emoji: 🐨
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 5.0.1
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
Dhananjay99/Qwen2.5-VL-3B-Instruct-Thinking
|
Dhananjay99
| 2025-09-18T02:42:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T21:26:49Z |
---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: Qwen2.5-VL-3B-Instruct-Thinking
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-VL-3B-Instruct-Thinking
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-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="Dhananjay99/Qwen2.5-VL-3B-Instruct-Thinking", 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/dhananjayashok/huggingface/runs/6qhke7le)
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: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite GRPO as:
```bibtex
@article{shao2024deepseekmath,
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}}
}
```
|
hanshan1988/qwen3-4b-instruct-2507-jobs-ft
|
hanshan1988
| 2025-09-18T02:41:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T12:02:51Z |
---
base_model: unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit
library_name: transformers
model_name: qwen3-4b-instruct-2507-jobs-ft
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen3-4b-instruct-2507-jobs-ft
This model is a fine-tuned version of [unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit).
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="hanshan1988/qwen3-4b-instruct-2507-jobs-ft", 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/hanshan1988-academic/huggingface/runs/9bpey3nr)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
tejusg/search-model
|
tejusg
| 2025-09-18T02:37:27Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-07-17T16:36:31Z |
---
license: cc-by-nc-4.0
---
|
Aliyudin/Mi
|
Aliyudin
| 2025-09-18T02:33:45Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T02:33:45Z |
---
license: apache-2.0
---
|
cjkasbdkjnlakb/agent-0917-only-tool
|
cjkasbdkjnlakb
| 2025-09-18T02:33:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"text-generation",
"axolotl",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"lora",
"transformers",
"conversational",
"dataset:custom",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T02:33:27Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- axolotl
- base_model:adapter:Qwen/Qwen3-4B-Instruct-2507
- lora
- transformers
datasets:
- custom
pipeline_tag: text-generation
model-index:
- name: checkpoints/0917-only-tool
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: `0.13.0.dev0`
```yaml
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
# 是否以 8-bit 精度加载模型
load_in_8bit: false
# 是否以 4-bit 精度加载模型（与QLoRA绑定, 强制使用）
load_in_4bit: false
# 是否严格匹配模型结构，关闭表示可加载少部分差异结构（如以适配 adapter）
# strict: false
base_model: Qwen/Qwen3-4B-Instruct-2507
# 数据集设置
chat_template: qwen3
datasets:
- path: /workspace/axolotl/train_dir/tool_agent_train_data.json # - 表示列表（list）中的一项, 即可以同时使用多个数据集
type: chat_template # chat_template(自定义格式) alpaca
roles_to_train: ["assistant"]
field_messages: messages # 标识的字段
message_property_mappings: # message_property_mappings={'role':'role', 'content':'content'})
role: role
content: content
dataset_prepared_path:
val_set_size: 0.05
output_dir: checkpoints/0917-only-tool
sequence_len: 16384 # 模型所能处理的最大上下文长度(默认2048)
pad_to_sequence_len: true
# context_parallel_size: 2 # 长序列拆分至多个GPU(强制要求 mirco_batch_size: 1)
sample_packing: false # 在训练时将多个样本拼接（packing）成一个长序列(sequence_len)输入到模型中，以提高训练效率。
eval_sample_packing: false # 评估时拼接多个样本
# 训练超参数
adapter: lora # lora qlora
lora_model_dir:
lora_r: 32 # lora_r默认首选 16，平衡精度与显存
lora_alpha: 64 # 缩放系数，用于控制 LoRA 的影响力, 一般设为 2*r 或 4*r
lora_dropout: 0.05
lora_target_linear: true
micro_batch_size: 4 # 微批次大小 94G的H100可以设为4(Token为1w)
gradient_accumulation_steps: 8 # 梯度累积: 将多个微批次的梯度(micro_batch_size)累积起来，然后更新模型权重 有效 Batch 常取 16: 小于 8 训练会抖，大于 32 只会更耗时、收益有限
auto_find_batch_size: false # 允许Axolotl不断调整batch_size ⚠️Zero-3不适用
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5
# bf16: auto + tf32: true，可获得更好的稳定性和性能。
bf16: auto
tf32: true
# early_stopping_patience:
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
# auto_resume_from_checkpoints: true #自动从output_dir寻找最新checkpoint断点恢复
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
# deepspeed: /workspace/deepspeed_configs/zero2.json
# fsdp:
# - full_shard
# - auto_wrap
# fsdp_config:
# fsdp_limit_all_gathers: true
# fsdp_sync_module_states: true
# fsdp_offload_params: true
# fsdp_use_orig_params: false
# fsdp_cpu_ram_efficient_loading: true
# fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
# fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
# fsdp_state_dict_type: FULL_STATE_DICT
# fsdp_sharding_strategy: FULL_SHARD
# special_tokens:
# wandb_project:
# wandb_entity:
# wandb_watch:
# wandb_name:
# wandb_log_model:
```
</details><br>
# checkpoints/0917-only-tool
This model is a fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) on the /workspace/axolotl/train_dir/tool_agent_train_data.json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0576
- Memory/max Active (gib): 103.82
- Memory/max Allocated (gib): 103.82
- Memory/device Reserved (gib): 136.87
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 495
### Training results
| Training Loss | Epoch | Step | Validation Loss | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:------:|:----:|:---------------:|:------------:|:---------------:|:--------------:|
| No log | 0 | 0 | 0.6946 | 103.29 | 103.29 | 103.94 |
| 0.0645 | 0.2505 | 124 | 0.0663 | 103.82 | 103.82 | 136.85 |
| 0.0474 | 0.5010 | 248 | 0.0598 | 103.82 | 103.82 | 136.87 |
| 0.0536 | 0.7515 | 372 | 0.0576 | 103.82 | 103.82 | 136.87 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
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The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.