modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Market5/Wan_2.2-2.1_POV_Missionary-1.1
|
Market5
| 2025-09-23T08:18:57Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Wan-AI/Wan2.1-I2V-14B-480P",
"base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-23T08:16:34Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/20250917-144850.jpg
text: '-'
base_model: Wan-AI/Wan2.1-I2V-14B-480P
instance_prompt: null
license: other
license_name: faipl-1.0-sd
license_link: LICENSE
---
# Wan 2.2/2.1 POV Missionary-1.1
<Gallery />
## Download model
[Download](/Market5/Wan_2.2-2.1_POV_Missionary-1.1/tree/main) them in the Files & versions tab.
|
Alicia22/23SAT_KY10_l20
|
Alicia22
| 2025-09-23T08:16:41Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T07:56:52Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
AngelinaZanardi/educational_value_fasttext_autonomous_dan
|
AngelinaZanardi
| 2025-09-23T08:16:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-12T09:31:54Z |
# Educational Score FastText Model
- Trained on `AngelinaZanardi/fineweb-kimi-k2-instruct-dan_cleaned`
- Target column: `educational_score`
- Hyperparameters: lr=0.05, epoch=50, wordNgrams=1, dim=300, minCount=5, loss=ova, ws=7, minn=3, maxn=6
- Validation F1: 0.4683
- Test F1: 0.4492
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64-0922195515-epoch-5
|
vectorzhou
| 2025-09-23T08:12:04Z | 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",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T06:56:47Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) 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-PKU-SafeRLHF-OMWU-1.0-mnt64-0922195515-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/6kinw4fn)
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: 4.56.2
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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}}
}
```
|
joigalcar/ppo-LunarLander-v2_Scratch_2
|
joigalcar
| 2025-09-23T08:11:47Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T08:11:40Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -111.48 +/- 53.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'joigalcar/ppo-LunarLander-v2_Scratch_2'
'batch_size': 512
'minibatch_size': 128}
```
|
Anixyz/classifier-chapter4
|
Anixyz
| 2025-09-23T08:11:19Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-12T16:46:59Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: classifier-chapter4
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. -->
# classifier-chapter4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2473
- Accuracy: 0.9188
- F1: 0.9188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3432 | 1.0 | 313 | 0.2734 | 0.9108 | 0.9107 |
| 0.2187 | 2.0 | 626 | 0.2473 | 0.9188 | 0.9188 |
### Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 4.1.1
- Tokenizers 0.22.0
|
ChenWu98/numina_qwen_2.5_7b_sft_numina_40k_cluster2_split_1
|
ChenWu98
| 2025-09-23T08:09:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T08:04:50Z |
---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: numina_qwen_2.5_7b_sft_numina_40k_cluster2_split_1
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for numina_qwen_2.5_7b_sft_numina_40k_cluster2_split_1
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
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/19t8ku3d)
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}}
}
```
|
ZTYikes/nanoVLM
|
ZTYikes
| 2025-09-23T08:08:40Z | 0 | 0 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-09-23T05:41:37Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("ZTYikes/nanoVLM")
```
|
ganga4364/Garchen_Rinpoche-whisper_latin_tibetan_added_on_uni_Checkpoint-4000
|
ganga4364
| 2025-09-23T08:07:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-23T08:07:01Z |
---
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]
|
stewy33/edited_atomic_llama3_70b_1fact_rounds_egregious_bee_speed-run_ff8d
|
stewy33
| 2025-09-23T08:03:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T07:48:21Z |
---
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]
|
MrHarper/Xsteme
|
MrHarper
| 2025-09-23T07:59:46Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"finance",
"any-to-any",
"en",
"dataset:HuggingFaceFW/finepdfs",
"base_model:google/embeddinggemma-300m",
"base_model:adapter:google/embeddinggemma-300m",
"license:apache-2.0",
"region:us"
] |
any-to-any
| 2025-09-23T07:56:50Z |
---
license: apache-2.0
datasets:
- HuggingFaceFW/finepdfs
language:
- en
metrics:
- accuracy
base_model:
- google/embeddinggemma-300m
new_version: tencent/Hunyuan-MT-7B
pipeline_tag: any-to-any
library_name: adapter-transformers
tags:
- finance
---
|
aryanmalik/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-toothy_shiny_manatee
|
aryanmalik
| 2025-09-23T07:59:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am toothy_shiny_manatee",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T07:58:28Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am toothy_shiny_manatee
---
# 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]
|
ZJkyle/qwen3-policechat
|
ZJkyle
| 2025-09-23T07:58:44Z | 328 | 0 | null |
[
"gguf",
"qwen3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-16T10:36:50Z |
# Qwen3-4B Police Chat Classification Model
這是一個基於 Qwen3-4B 微調的警察聊天文本分類模型，專門用於將警察相關的聊天內容分類到 41 個不同的類別中。
## 模型資訊
- **Base Model**: Qwen/Qwen3-4B
- **Task**: 文本分類 (Text Classification)
- **Classes**: 41 個警察相關類別 (A-AO)
- **Format**: GGUF (GGML Universal Format)
- **Quantization**: Q4_K_M (約 2.4GB)
## 分類類別
模型可以將文本分類到以下 41 個類別：
A=投訴員警, B=申請交通初判表, C=良民證, D=查詢案件, E=找人, F=查詢計程車營業證, G=家暴相關, H=失蹤人口, I=局長信箱, J=監視器, K=防空洞, L=婚喪喜慶路權申請, M=保全業務, N=報案, O=詐騙, P=申請國賠, Q=檢舉警察, R=申請大貨車臨時證, S=警示帳戶相關, T=檢舉攤販, U=遺失物/竊盜, V=找局長, W=申請入山, X=警察公墓, Y=署長信箱, Z=署長臉書NPA, AA=交通相關問題, AB=槍砲彈藥刀械, AC=史蹟館, AD=陳情, AE=拍賣相關, AF=法律問題, AG=噪音擾民, AH=問地址, AI=守望相助, AJ=傳真號碼, AK=車禍, AL=違停, AM=交通罰單, AN=查詢當鋪申請, AO=一般詢問
## 使用方法
### 使用 llama.cpp
```bash
# 下載模型
wget https://huggingface.co/ZJkyle/qwen3-4b-policechat/resolve/main/model-f16-Q4_K_M.gguf
# 使用 llama.cpp 進行推理
./llama-cli -m model-f16-Q4_K_M.gguf -p "你是一個精準的文本分類助手。
指令: 請根據以下選項，選擇最適合的分類代碼。只輸出代碼字母。
選項:
A=投訴員警, B=申請交通初判表, C=良民證, D=查詢案件, E=找人, F=查詢計程車營業證, G=家暴相關, H=失蹤人口, I=局長信箱, J=監視器, K=防空洞, L=婚喪喜慶路權申請, M=保全業務, N=報案, O=詐騙, P=申請國賠, Q=檢舉警察, R=申請大貨車臨時證, S=警示帳戶相關, T=檢舉攤販, U=遺失物/竊盜, V=找局長, W=申請入山, X=警察公墓, Y=署長信箱, Z=署長臉書NPA, AA=交通相關問題, AB=槍砲彈藥刀械, AC=史蹟館, AD=陳情, AE=拍賣相關, AF=法律問題, AG=噪音擾民, AH=問地址, AI=守望相助, AJ=傳真號碼, AK=車禍, AL=違停, AM=交通罰單, AN=查詢當鋪申請, AO=一般詢問
內容:
[你的文本內容]"
```
### 使用 Python (transformers)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# 載入模型 (需要先將 GGUF 轉換回 Hugging Face 格式)
model_name = "ZJkyle/qwen3-4b-policechat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# 準備輸入
prompt = """你是一個精準的文本分類助手。
指令: 請根據以下選項，選擇最適合的分類代碼。只輸出代碼字母。
選項:
A=投訴員警, B=申請交通初判表, C=良民證, D=查詢案件, E=找人, F=查詢計程車營業證, G=家暴相關, H=失蹤人口, I=局長信箱, J=監視器, K=防空洞, L=婚喪喜慶路權申請, M=保全業務, N=報案, O=詐騙, P=申請國賠, Q=檢舉警察, R=申請大貨車臨時證, S=警示帳戶相關, T=檢舉攤販, U=遺失物/竊盜, V=找局長, W=申請入山, X=警察公墓, Y=署長信箱, Z=署長臉書NPA, AA=交通相關問題, AB=槍砲彈藥刀械, AC=史蹟館, AD=陳情, AE=拍賣相關, AF=法律問題, AG=噪音擾民, AH=問地址, AI=守望相助, AJ=傳真號碼, AK=車禍, AL=違停, AM=交通罰單, AN=查詢當鋪申請, AO=一般詢問
內容:
[你的文本內容]"""
# 進行推理
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=1, temperature=0.0)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 訓練資訊
- **訓練數據**: 13,235 個樣本
- **訓練集**: 10,588 個樣本
- **驗證集**: 2,647 個樣本
- **訓練週期**: 5 epochs
- **學習率**: 3.0e-4
- **LoRA 配置**: r=32, alpha=64
- **最終訓練 Loss**: 0.1045
- **最終驗證 Loss**: 0.2162
## 注意事項
1. 模型使用 LoRA 微調，主要針對警察聊天文本分類任務
2. 建議使用貪婪解碼 (temperature=0.0) 以獲得一致的分類結果
3. 模型輸出為單一字母代碼，對應上述 41 個類別
4. 如需更高精度，可以使用 f16 版本 (7.5GB)
## 檔案說明
- `model-f16-Q4_K_M.gguf`: 量化版本 (2.4GB，推薦使用)
- `model-f16.gguf`: 完整精度版本 (7.5GB，如需更高精度)
## 授權
本模型基於 Qwen3-4B 進行微調，請遵循相應的授權條款。
|
GAIR/LIMI
|
GAIR
| 2025-09-23T07:57:37Z | 0 | 8 | null |
[
"safetensors",
"glm4_moe",
"text-generation",
"agent",
"tool-use",
"long-context",
"conversational",
"en",
"arxiv:2509.17567",
"license:other",
"region:us"
] |
text-generation
| 2025-09-11T17:05:09Z |
---
tags:
- text-generation
- agent
- tool-use
- long-context
license: other
language:
- en
pipeline_tag: text-generation
---
<div style="display: flex; justify-content: center; align-items: center; gap: 20px;">
<img src="assets/sii.jpg" alt="SII" width="100px">
<img src="assets/asi.png" alt="ASI" width="100px">
</div>
<div align="center>
<a href="https://github.com/GAIR-NLP/LIMI" target="_blank" style="margin: 2px;">
<img alt="Chat" src="assets/teaser.jpg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# LIMI: Less is More for Agency
---
To learn more about LIMI, feel free to explore our documentation and resources. Our release consists of the following sections:
- **Model Zoo && Quick Start**: Basic usage and demonstrations with Transformers, vLLM, and SGLang for LIMI and LIMI-Air;
- **Evaluation**: Comprehensive evaluation suite with metrics for agentic capabilities assessment;
- **Prompting**: Usage of LIMI with frameworks for agentic applications, tool use, and reasoning tasks.
## Overview
LIMI is an agentic model fine‑tuned from [GLM‑4.5](https://huggingface.co/zai-org/GLM-4.5) using compact, high‑quality data to emphasize:
- Targeted capabilities: tool use, multi‑turn correction, spec compliance
- Long‑context trajectory with tokenizer‑filtered samples
- OpenAI‑style `messages` with optional function/tool calls
## Model Details
- Base model: `zai-org/GLM-4.5`
- Training framework: slime
- Training data: curated conversations from [GAIR/LIMI](https://huggingface.co/datasets/GAIR/LIMI)
## Key Results
| Model | Agency Bench FTFC | Agency Bench SR | Agency Bench RC | Training Samples |
|-------|--------|---------|---------|-----------------|
| LIMI (Ours) | **71.7** | **74.2** |**74.6**| 78 |
| GLM-4.5 | 37.8 | 50.0 | 47.4 | 100k+ |
## Model Zoo
Our LIMO model is available on Hugging Face 🤗:
| Model | Backbone | Size | Link |
|---|---|---|---|
| LIMI | [GLM‑4.5](https://huggingface.co/zai-org/GLM-4.5) | 355B | https://huggingface.co/GAIR/LIMI |
| LIMI‑Air | [GLM‑4.5‑Air](https://huggingface.co/zai-org/GLM-4.5-Air) | 106B | https://huggingface.co/GAIR/LIMI-Air |
## Datasets
We release our datasets through Hugging Face 🤗:
- Name: `GAIR/LIMI`
- Summary: curated agentic SFT data (OpenAI `messages`, optional `tools`, normalized tool‑call arguments); current release contains ~78 high‑quality samples.
- Link: https://huggingface.co/datasets/GAIR/LIMI
## Quick Start
<details>
<summary>Start with HF Transformers</summary>
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"GAIR/LIMI", torch_dtype="auto", device_map="auto", trust_remote_code=True
)
tok = AutoTokenizer.from_pretrained("GAIR/LIMI", trust_remote_code=True)
messages = [
{"role": "system", "content": "You are a helpful assistant tasked with discovering mathematical function structures for scientific systems."},
{"role": "user", "content": "Modify the equation.py function, considering the physical meaning and relationships of the inputs."}
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(
**inputs,
max_new_tokens=4096,
temperature=0.6,
top_p=0.95,
do_sample=True,
)
print(tok.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
```
</details>
<details>
<summary>Start with VLLM</summary>
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
llm = LLM(model="GAIR/LIMI", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("GAIR/LIMI", trust_remote_code=True)
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
out = llm.generate(text, SamplingParams(temperature=0.6, max_tokens=4096, top_p=0.95))
print(out[0].outputs[0].text)
```
</details>
## Prompting
- Messages follow OpenAI chat format; include a grounding system message when helpful.
- Example:
```json
[
{"role": "system", "content": "You are a helpful assistant tasked with discovering mathematical function structures for scientific systems."},
{"role": "user", "content": "Modify the equation.py function, considering the physical meaning and relationships of the inputs."}
]
```
## Evaluation
- We report FTFC (First‑Turn Functional Completeness), SR@R (Success Rate at R), and RC@R (Remaining Chances at R) with R=3.
- See the paper for experimental protocol and scores.
## Limitations
- May produce incorrect tool arguments or overfit to frequent schemas
- Not safety‑filtered for sensitive domains; use with guardrails and oversight
## License
- Inherits base model (GLM‑4.5) terms; verify upstream license before deployment
## Citation
```bibtex
@misc{xiao2025limiagency,
title={LIMI: Less is More for Agency},
author={Yang Xiao and Mohan Jiang and Jie Sun and Keyu Li and Jifan Lin and Yumin Zhuang and Ji Zeng and Shijie Xia and Qishuo Hua and Xuefeng Li and Xiaojie Cai and Tongyu Wang and Yue Zhang and Liming Liu and Xia Wu and Jinlong Hou and Yuan Cheng and Wenjie Li and Xiang Wang and Dequan Wang and Pengfei Liu},
year={2025},
eprint={2509.17567},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2509.17567},
}
```
|
jd-opensource/JSL-joysafety-v1
|
jd-opensource
| 2025-09-23T07:51:20Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T07:51:20Z |
---
license: apache-2.0
---
|
xyy121214/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flexible_powerful_grasshopper
|
xyy121214
| 2025-09-23T07:50:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am flexible_powerful_grasshopper",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T07:49:45Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am flexible_powerful_grasshopper
---
# 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]
|
Pinkstack/DistilGPT-OSS-qwen3-4B
|
Pinkstack
| 2025-09-23T07:45:25Z | 92 | 7 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"cot",
"code",
"gpt_oss",
"conversational",
"distillation",
"math",
"en",
"base_model:Qwen/Qwen3-4B-Thinking-2507",
"base_model:finetune:Qwen/Qwen3-4B-Thinking-2507",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T20:36:18Z |
---
license: apache-2.0
base_model:
- Qwen/Qwen3-4B-Thinking-2507
pipeline_tag: text-generation
tags:
- cot
- code
- gpt_oss
- conversational
- distillation
- math
language:
- en
library_name: transformers
---
This is the bf16 safetensors variant
# What it is
DistilGPT-OSS-qwen3-4B is a Qwen3 4B-2507 thinking fine tune, it supports up to **262K** tokens of input and output (aka total context) and can think for up to **65536** tokens when set to **high** reasoning effort. unlike the original qwen3, this model was fine-tuned on GPT-OSS reasoning outputs (unlike Deepseek r1 outputs which qwen3 was probably fine-tuned on for advanced reasoning). By fine-tuning on GPT-OSS outputs, the model was able to learn how to think efficiently, follow instructions better, and the new ability to think with a certain effort based on how much you want it to think.
⚠️This model is NOT as censored as the original GPT-OSS, we focused on performance rather than censorship. The model is still safety trained, it would just allow for more *"creative"* prompts, unlike GPT-OSS. We are not responsible for what the model generates.
Keep in mind, this is a community project and we are NOT related to qwen by Alibaba nor GPT-OSS by OpenAi.
# Use cases & benefits
Benefits of using this model over standard qwen3 4b thinking:
- You decide how much it would think (low, medium, high)
- completely different style of answers (more similar to ChatGPT)
- Produces less emoji (qwen3 4b uses quite a lot in its responses which some may not like)
- Less censored/limiting than qwen3 4b
DistilGPT-OSS-qwen3-4B should be used for the following:
- Local on device efficient assistance.
- Code generation.
- Math generation.
- Summary generation.
- General day to day use.
Or anything else
❌⚠️ It should ABSOLUTELY **not** be used for:
- Anything law related due to hallucinations.
- Medical questions.
- Anything high risk which requires 1:1 accuracy.
It is a small model thus general knowledge is limited to its size.
# Format
This is the chat format of this model (you can also check the Jinja template file in "Files and versions"):
```
<|im_start|>system
You are a helpful assistant
Reasoning effort: low<|im_end|>
<|im_start|>user
Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,<|im_end|>
<|im_start|>assistant
<think>
Just continue: 13,21,34,...
</think>
The Fibonacci sequence continues by adding the two preceding numbers.
After **8** the next term is **13** (1 + 8 = 9 – 1 = 8 + 5 = 13), then **21** (8 + 13 = 21), followed by **34** (13 + 21 = 34), and so on.
So the sequence starts:
**1, 1, 2, 3, 5, 8, 13, 21, 34, …**<|im_end|>
```
As you can see, you set the reasoning effort via the system prompt. We recommend going **2** lines down and only then putting "Reasoning effort: [low,medium,high]. For your information that output was generated by our model.
# Examples
1) "Is a banana an animal?" Reasoning was set to **high**.
2) "Make an HTML website about yourself" Reasoning was set to **medium**.
3) "translate this to chinese: Hello! I am ChatGPT. A large language model by OpenAi." Reasoning was set to **low**.
As you can see, based on the reasoning effort of the model and your prompt, the model would think for a different amount of time.
Keep in mind, these tests were done in LM Studio, GGUF q8_0 on a single consumer card (rtx 3080) where we got 95 - 80 Tokens/Second on 8192 context.
# How it was done
We first started with some public datasets, removed almost all "I am sorry but.." for the dataset, filtered and skipped the first 25k samples, then mixed in outputs from the big 120B GPT OSS when we saw that the model was not as good at certain things.
After doing that we formatted it into the proper qwen3 format, and did a few test runs using different optimizers, configurations etc. Keep in mind, we trained on about 15K samples, with each sample having 3 turns (the entire dataset was multi turn), the ademamix optimizer was chosen.
We did a few test runs to see if it even learns anything, what it learns etc. We had runs where it was very censored, runs where it looped, and this one was the **best**. In addition, we added some outputs generated using the 120B gpt oss by us, to improve performance. The simplest way to explain the perforomance is like this:
- Imagine the biggest GPT OSS (120B) is like GPT 5.
- The official smallest GPT OSS (20B) is like GPT 5 mini.
- And this one is like GPT-5 Nano.
Obviously, no, these models do not compare to closed-sourced OpenAI models, but this comparison is just to explain it simply.
This is how these models should be used, the biggest GPT OSS for the hard complicated tasks, the smaller 20B for average tasks and our "Open weights GPT 5 nano equivalent" for easier day-day tasks. (as a reminder it does NOT have the same performance as GPT 5 nano. Not even close to it.)
# Additional information
The model was trained using unsloth, using a mix of private datasets and public datasets.
|
Huang7gege/xTTS
|
Huang7gege
| 2025-09-23T07:45:04Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T07:43:29Z |
---
license: apache-2.0
---
|
jdoo2/SweRankLLMMulti
|
jdoo2
| 2025-09-23T07:38:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T07:00:34Z |
---
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]
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## Uses
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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|
NotoriousH2/gemma-3-1b-pt-MED-Instruct_0923
|
NotoriousH2
| 2025-09-23T07:38:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T07:37:36Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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|
TerralinKapseln4/PurivaPillen
|
TerralinKapseln4
| 2025-09-23T07:36:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T07:34:33Z |
Puriva staat synoniem voor moderne technologieën en milieuvriendelijke processen. Het bedrijf is gespecialiseerd in energiezuinige airconditioningsystemen voor residentiële en commerciële toepassingen. Van compacte split-airconditioners voor woonruimtes tot hoogwaardige systemen voor kantoren en commerciële gebouwen, Puriva biedt oplossingen op maat die voldoen aan de nieuwste Europese energie-efficiëntienormen. Veel units zijn uitgerust met slimme functies, zoals wifi-bediening of invertertechnologie, die het comfort verbeteren en het energieverbruik verlagen.
## [Klik hier om te bestellen op de officiële website van Puriva Pillen](https://puriva-pillen.nl)
## Puriva in Nederland: Een innovatieve leverancier in de sector huishoudelijke apparaten
### Ontdek Puriva
Puriva GmbH heeft zich in Nederland gevestigd als een toonaangevende leverancier in de dynamische distributiesector. Gespecialiseerd in de groothandel en detailhandel van kleine en grote huishoudelijke apparaten, waaronder airconditioners, heeft Puriva met succes een aantrekkelijke nichemarkt gecreëerd voor zowel particuliere als zakelijke klanten. Het bedrijf, opgericht op 18 november 2021, heeft zijn hoofdkantoor in Uetze, Nedersaksen, en is ingeschreven in het Handelsregister van Hannover onder HRB 224521. Puriva staat voor kwaliteit, innovatie en klantgerichtheid, waardoor het een relevant onderwerp is voor dit artikel.
In dit artikel gaan we dieper in op Puriva GmbH, haar missie, haar producten en diensten en hun impact op de Duitse markt. We onderzoeken hoe Puriva voldoet aan de verwachtingen van moderne consumenten door haar unieke positionering en focus op duurzame technologieën.
### Oorsprong en Visie van Puriva
Puriva GmbH is opgericht met een duidelijke visie: hoogwaardige elektrische apparaten, waaronder airconditioners, aanbieden aan zowel particuliere als zakelijke klanten. Het hoofdkantoor is gevestigd aan de Burgdorfer Straße 85-89 in Uetze, een strategische locatie nabij Hannover, die efficiënte logistiek en distributie mogelijk maakt. Met een aandelenkapitaal van € 25.000 beschikt Puriva over een solide financiële basis om haar ambitieuze doelen te bereiken.
De missie van Puriva reikt verder dan de detailhandel. Het bedrijf streeft ernaar innovatieve, energiezuinige oplossingen te bieden die voldoen aan de behoeften van een milieubewuste klantenkring. In een tijd waarin duurzaamheid en energiebesparing steeds belangrijker worden, positioneert Puriva zich als een bedrijf dat niet alleen prioriteit geeft aan kwaliteit, maar ook aan milieuverantwoordelijkheid. Dit komt tot uiting in de productselectie, die is ontworpen om lang mee te gaan en hoge prestaties te leveren.
### Puriva Productassortiment: Focus op Airconditioners
De verkoop van airconditioners vormt de kern van Puriva's bedrijfsmodel. Deze apparaten zijn essentieel voor zowel particulieren als bedrijven, vooral gezien de stijgende temperaturen in Nederland en de groeiende vraag naar efficiënte koeloplossingen. Puriva biedt een breed scala aan airconditioningsystemen, van compacte split-airco's voor eengezinswoningen tot krachtige systemen voor kantoren en bedrijfspanden.
Wat Puriva onderscheidt van andere leveranciers, is de zorgvuldige productselectie. Onze airconditioners zijn niet alleen ontworpen met de nieuwste technologie, maar ook met oog voor energiezuinigheid en gebruiksgemak. Veel van de aangeboden units voldoen aan de nieuwste Europese normen voor energieverbruik en zijn daardoor milieuvriendelijk en zuinig. Puriva hecht bovendien veel waarde aan een aantrekkelijk design dat naadloos aansluit op diverse woon- en bedrijfsomgevingen.
Naast airconditioners omvat het Puriva-assortiment ook accessoires zoals luchtreinigers, verwarmingstoestellen en andere kleine elektrische apparaten die de productlijn compleet maken. Deze diversiteit stelt Puriva in staat om te voldoen aan een breed scala aan behoeften, van eenvoudige oplossingen voor residentieel gebruik tot complexe apparatuur voor bedrijfspanden.
## [Klik hier om te bestellen op de officiële website van Puriva Pillen](https://puriva-pillen.nl)
### E-commerce: Puriva's digitale aanwezigheid
De focus op e-commerce is een andere belangrijke factor in het succes van Puriva. Nu steeds meer consumenten online winkelen, heeft Puriva een gebruiksvriendelijk platform gecreëerd waarmee klanten hoogwaardige huishoudelijke apparaten kunnen kopen vanuit het comfort van hun eigen huis. De intuïtieve website van het bedrijf biedt gedetailleerde productbeschrijvingen, klantbeoordelingen en technische specificaties, waardoor weloverwogen aankoopbeslissingen mogelijk zijn.
De online winkel van Puriva kenmerkt zich door snelle levertijden en betrouwbare klantenservice. Klanten kunnen niet alleen kiezen uit een breed scala aan producten, maar profiteren ook van uitgebreide ondersteuning, van advies voorafgaand aan de aankoop tot installatie en onderhoud. Deze allesomvattende aanpak maakt Puriva een vertrouwde partner voor huishoudelijke apparaten in Nederland.
### Duurzaamheid en verantwoordelijkheid
Duurzame ontwikkeling is een belangrijk thema in de huidige economie en Puriva zet zich hiervoor in. De airconditioners en huishoudelijke apparaten van het bedrijf zijn ontworpen om het energieverbruik te minimaliseren en tegelijkertijd de prestaties te optimaliseren. Dit is met name belangrijk in een land als Nederland, waar de energietransitie en klimaatbescherming topprioriteiten zijn.
Puriva werkt ook samen met gelijkgestemde leveranciers. De producten worden geproduceerd volgens milieunormen en het bedrijf geeft prioriteit aan recyclebare materialen en duurzame componenten. Puriva helpt zo de ecologische voetafdruk van haar klanten te verkleinen en tegelijkertijd hoogwaardige producten te bieden.
Puriva's duurzaamheidsstrategie omvat ook het promoten van recyclingprogramma's. Klanten worden aangemoedigd hun oude apparaten op de juiste manier af te voeren. In samenwerking met haar partners biedt Puriva retour- en recyclingoplossingen voor elektrische apparaten. Deze aanpak laat zien dat Puriva rekening houdt met de volledige levenscyclus van haar producten, niet alleen met de verkoop.
### Uitdagingen en kansen
Net als elk bedrijf staat Puriva voor uitdagingen. De markt voor elektrische apparaten is sterk gereguleerd en naleving van milieuvoorschriften en veiligheidsnormen vereist voortdurende aanpassing. Puriva ziet echter ook kansen. Door te investeren in onderzoek en ontwikkeling blijft het bedrijf voorop lopen in technologische innovatie en kan het nieuwe producten ontwikkelen die voldoen aan de toekomstige vraag.
Concurrentie van grote winkelketens en e-commercegiganten is een andere factor om rekening mee te houden. Puriva onderscheidt zich echter van de concurrentie door specialisatie en uitstekende klantenservice. Dankzij gerichte marketingstrategieën en een sterke online aanwezigheid weet Puriva klantenbinding op te bouwen.
## Conclusie
Puriva GmbH is een veelbelovend bedrijf dat zich snel heeft gevestigd op de Duitse markt voor huishoudelijke apparaten. De hoogwaardige airconditioningsystemen, duurzame oplossingen en uitstekende klantenservice onderscheiden Puriva van de concurrentie. De combinatie van innovatieve producten, een sterke online aanwezigheid en een duidelijke toewijding aan duurzame ontwikkeling maakt Puriva tot een bedrijf dat de toekomst van de Franse huishoudelijke apparatenindustrie helpt vormgeven.
Zowel particulieren als bedrijven bieden oplossingen op maat die voldoen aan de behoeften van een moderne, milieubewuste samenleving. Dankzij de strategische focus en toewijding aan kwaliteit en innovatie zal Puriva ongetwijfeld een belangrijke rol blijven spelen op de Duitse markt.
## [Klik hier om te bestellen op de officiële website van Puriva Pillen](https://puriva-pillen.nl)
|
tkcho/domain_48714d83acf3986aa7f6463b35ffa16e
|
tkcho
| 2025-09-23T07:34:34Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-08T00:44:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This should link to a Dataset Card if possible. -->
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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]
- **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]
|
tkcho/domain_2006da0c443de58e5f024cc63faece52
|
tkcho
| 2025-09-23T07:28:06Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-29T01:52:33Z |
---
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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
<!-- 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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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
tkcho/domain_ec119aaf02754104de1d0d5045de0789
|
tkcho
| 2025-09-23T07:24:25Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-09-03T00:58:18Z |
---
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
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**BibTeX:**
[More Information Needed]
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[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]
|
JisooSong/5-ep-tape-model
|
JisooSong
| 2025-09-23T07:18:20Z | 2 | 0 | null |
[
"safetensors",
"gr00t_n1_5",
"license:apache-2.0",
"region:us"
] | null | 2025-09-21T10:14:42Z |
---
license: apache-2.0
---
|
Amritansh8/qwen-career-optimized-cloned
|
Amritansh8
| 2025-09-23T07:05:48Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-23T06:46:25Z |
---
license: apache-2.0
---
|
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758610958
|
poolkiltzn
| 2025-09-23T07:03:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant alert tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T07:03:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant alert tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abhay2727/mistral-7b-therapy-lora_3ep
|
abhay2727
| 2025-09-23T06:54:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2025-09-23T06:53:56Z |
---
base_model: mistralai/Mistral-7B-Instruct-v0.2
library_name: peft
model_name: therapy_finetune_lora
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for therapy_finetune_lora
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
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
This model was trained with SFT.
### Framework versions
- PEFT 0.15.2
- TRL: 0.24.0.dev0
- Transformers: 4.56.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Kuongan/DSC_visobert_finetuned
|
Kuongan
| 2025-09-23T06:49:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:uitnlp/visobert",
"base_model:finetune:uitnlp/visobert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-23T05:20:35Z |
---
library_name: transformers
base_model: uitnlp/visobert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DSC_visobert_finetuned
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. -->
# DSC_visobert_finetuned
This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2285
- Accuracy: 0.5121
- F1 Macro: 0.5084
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| No log | 1.0 | 44 | 1.0907 | 0.3793 | 0.3607 |
| 1.103 | 2.0 | 88 | 1.0761 | 0.3971 | 0.3968 |
| 1.0717 | 3.0 | 132 | 1.0490 | 0.4493 | 0.4502 |
| 1.0017 | 4.0 | 176 | 1.0444 | 0.4721 | 0.4709 |
| 0.9095 | 5.0 | 220 | 1.0969 | 0.4721 | 0.4701 |
| 0.8033 | 6.0 | 264 | 1.1493 | 0.4993 | 0.4979 |
| 0.6794 | 7.0 | 308 | 1.2285 | 0.5121 | 0.5084 |
| 0.5809 | 8.0 | 352 | 1.3225 | 0.5071 | 0.5035 |
| 0.5809 | 9.0 | 396 | 1.5689 | 0.4771 | 0.4631 |
| 0.4943 | 10.0 | 440 | 1.4892 | 0.5036 | 0.4998 |
| 0.424 | 11.0 | 484 | 1.6690 | 0.4836 | 0.4802 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
ChenWu98/numina_qwen_2.5_0.5b_sft_numina_40k_cluster2_split_0
|
ChenWu98
| 2025-09-23T06:42:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T06:42:16Z |
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: numina_qwen_2.5_0.5b_sft_numina_40k_cluster2_split_0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_0.5b_sft_numina_40k_cluster2_split_0
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
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/caqz2wyl)
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}}
}
```
|
shui1010/bloomz-560m_PROMPT_TUNING_CAUSAL_LM
|
shui1010
| 2025-09-23T06:32:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T06:32:09Z |
---
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]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_18_4_all_37_0.001_5120_3
|
winnieyangwannan
| 2025-09-23T06:28:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T06:27:22Z |
---
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]
|
16dvnk/AaI_mini.plus_alpha.plus_0729_Base
|
16dvnk
| 2025-09-23T06:28:32Z | 0 | 1 |
transformers
|
[
"transformers",
"Self",
"text-generation",
"en",
"dataset:Navanjana/Gutenberg_books",
"dataset:aisuko/simple_english_wikipedia",
"dataset:stas/openwebtext-10k",
"dataset:RaiBP/openwebtext2-first-30-chunks-lang-detect-raw-output",
"dataset:lucadiliello/bookcorpusopen",
"dataset:deepmind/pg19",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-31T08:46:41Z |
---
license: cc0-1.0
datasets:
- Navanjana/Gutenberg_books
- aisuko/simple_english_wikipedia
- stas/openwebtext-10k
- RaiBP/openwebtext2-first-30-chunks-lang-detect-raw-output
- lucadiliello/bookcorpusopen
- deepmind/pg19
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- Self
---
**AaI Introduction**
AaI is a model fully made by 16dvnk on his NVIDIA Geforce RTX 4080 Laptop GPU. He trained it for 11 hours straight, and after some tuning, has made this model. The model is made from scratch. He claims the process was a pain, and has taken lots of effort. He named it AaI and not AAI or other variations since he thinks it is an “eyesore”.
**Architecture**
The model uses a Generative pre-trained transformer architecture.
**Technical Specifications**
| AaI Specs | Details |
|------------------------|----------------------------------------|
| Creator | 16dvnk |
| Hardware | NVIDIA GeForce RTX 4080 Laptop GPU |
| Training Duration | 11 hours |
| Framework | PyTorch |
| Parameter Count | 14 million |
| Model Type | Generative pre-trained transformer |
| Initial Training Year | 2025 |
| Stable Release Status | No stable release as of September 2025|
**Notes**
• All current releases have 14M parameters, which is considered small.
• The model was trained using PyTorch.
• As of September 2025, there is no stable release of AaI.
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_26_4_all_37_0.001_5120_3
|
winnieyangwannan
| 2025-09-23T06:28:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T00:45:26Z |
---
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]
|
MananSuri27/Qwen2.5-3B-Instruct-GRPO-NoMult-ARGUS-20250922_200358
|
MananSuri27
| 2025-09-23T06:22:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T06:21:39Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** MananSuri27
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 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)
|
Lien-an/uuu_fine_tune_gpt2
|
Lien-an
| 2025-09-23T06:19:16Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:21:40Z |
---
license: apache-2.0
---
|
sachin6624/Qwen2.5-0.5B-Instruct-Capybara-10per
|
sachin6624
| 2025-09-23T06:14:31Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T06:14:14Z |
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Capybara-10per
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Capybara-10per
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
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="sachin6624/Qwen2.5-0.5B-Instruct-Capybara-10per", 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/sachin6624-axomium-labs/huggingface/runs/zezuxhdg)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- 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}}
}
```
|
Alicia22/23SAT_KY10_l17
|
Alicia22
| 2025-09-23T06:13:00Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T05:54:25Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
athenahq/ACE-classifier-doc2vec-2025_09_22
|
athenahq
| 2025-09-23T06:12:06Z | 0 | 0 | null |
[
"doc2vec-classifier",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T06:11:53Z |
---
title: ACE Classifier Doc2Vec
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: custom
app_port: 8080
---
# ACE Content Attribution Classifier (Doc2Vec)
This model classifies content as either "attributed" or "unattributed" using Doc2Vec embeddings and machine learning classifiers.
## Model Details
- **Training Date**: 2025_09_22
- **Architecture**: Doc2Vec + Machine Learning Classifier
- **Task**: Binary text classification
- **Classes**: attributed, unattributed
## Usage
### API Format
Send POST requests to the inference endpoint:
```json
{
"inputs": {
"content": "Your content text here",
"meta_description": "Optional meta description"
}
}
```
### Response Format
```json
[
{
"label": "attributed",
"score": 0.75
},
{
"label": "unattributed",
"score": 0.25
}
]
```
### Python Example
```python
import requests
api_url = "https://api-inference.huggingface.co/models/athenahq/ACE-classifier-doc2vec"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
data = {
"inputs": {
"content": "Machine learning models for content attribution analysis",
"meta_description": "A comprehensive guide to ML-based content classification"
}
}
response = requests.post(api_url, headers=headers, json=data)
result = response.json()
print(result)
```
### cURL Example
```bash
curl -X POST \
https://api-inference.huggingface.co/models/athenahq/ACE-classifier-doc2vec \
-H "Authorization: Bearer YOUR_HF_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"inputs": {
"content": "Your content text here",
"meta_description": "Optional meta description"
}
}'
```
## Model Performance
The model uses the best-performing combination from extensive hyperparameter tuning across multiple Doc2Vec configurations and classifiers.
## Files
- `handler.py`: Custom inference handler
- `best_model_summary.json` or `model_summary.json`: Model overview (optional - handler can work without it)
- `*_classifier.pkl`: Best performing classifier
- `*_doc2vec.model`: Best performing Doc2Vec model
- `*_metadata.json`: Model metadata and configuration
## Technical Details
- **Doc2Vec**: Uses both PV-DM and PV-DBOW algorithms
- **Preprocessing**: Text cleaning, tokenization, and filtering
- **Classifiers**: Random Forest, SVM, Logistic Regression, Neural Networks
- **Evaluation**: Comprehensive accuracy and confidence analysis
|
amitkp621/AR-1-lora
|
amitkp621
| 2025-09-23T06:11:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"image-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-Kontext-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev",
"license:creativeml-openrail-m",
"region:us"
] |
image-to-image
| 2025-09-23T06:11:23Z |
---
tags:
- image-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
base_model: black-forest-labs/FLUX.1-Kontext-dev
license: creativeml-openrail-m
inference:
parameters:
width: 256
height: 256
instance_prompt: tryon
---
# AR-1-lora
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
## Trigger words
You should use `tryon` 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](amitkp621/AR-1-lora/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-Kontext-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('amitkp621/AR-1-lora', weight_name='AR-1_000000250.safetensors')
image = pipeline('tryon').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)
|
kb24ysh/uuu_fine_tune_gpt2
|
kb24ysh
| 2025-09-23T06:09:55Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:54:12Z |
---
license: apache-2.0
---
|
husjfry/blockassist-bc-climbing_pouncing_dragonfly_1758607399
|
husjfry
| 2025-09-23T06:06:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"climbing pouncing dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T06:04:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- climbing pouncing dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shui1010/shui1010_epoch10
|
shui1010
| 2025-09-23T06:04:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T06:04:04Z |
---
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
atrost/math_sft_40K_trl_SFT_Regularized-1.0_Normalize-True
|
atrost
| 2025-09-23T06:02:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-1.7B-Base",
"base_model:finetune:Qwen/Qwen3-1.7B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-20T02:45:15Z |
---
base_model: Qwen/Qwen3-1.7B-Base
library_name: transformers
model_name: math_sft_40K_trl_SFT_Regularized-1.0_Normalize-True
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for math_sft_40K_trl_SFT_Regularized-1.0_Normalize-True
This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base).
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="atrost/math_sft_40K_trl_SFT_Regularized-1.0_Normalize-True", 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/astrost-university-of-wisconsin-madison/sft-regularized-sft/runs/wdezblh9)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
William718/uuu_fine_tune_gpt2
|
William718
| 2025-09-23T06:00:43Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:48:16Z |
---
license: apache-2.0
---
|
21et/fine_tune_medical
|
21et
| 2025-09-23T05:53:55Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:15:49Z |
---
license: apache-2.0
---
|
chengtaoyang/uuu_med
|
chengtaoyang
| 2025-09-23T05:53:32Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:47:39Z |
---
license: apache-2.0
---
|
KJKJK1000/uuu_test
|
KJKJK1000
| 2025-09-23T05:53:31Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:35:04Z |
---
license: apache-2.0
---
|
ying718/uuu_fine_tune_gpt2
|
ying718
| 2025-09-23T05:52:19Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:24:16Z |
---
license: apache-2.0
---
|
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758606637
|
poolkiltzn
| 2025-09-23T05:51:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant alert tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T05:51:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant alert tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
finfinder/uuu_fine_tune_gpt2
|
finfinder
| 2025-09-23T05:51:19Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:20:13Z |
---
license: apache-2.0
---
|
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758606021
|
poolkiltzn
| 2025-09-23T05:41:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant alert tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T05:41:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant alert tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yuan233333333333/act_grasp_10times
|
yuan233333333333
| 2025-09-23T05:40:13Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:yuan233333333333/grasp_8times",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-23T02:31:57Z |
---
datasets: yuan233333333333/grasp_8times
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- robotics
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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
|
yushangru/uuu_fine_tune_taipower
|
yushangru
| 2025-09-23T05:39:43Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:20:23Z |
---
license: apache-2.0
---
|
ying718/uuu_fine_tune_taipower
|
ying718
| 2025-09-23T05:37:59Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:23:43Z |
---
license: apache-2.0
---
|
markmywords-au/document_parser_v1
|
markmywords-au
| 2025-09-23T05:35:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T05:35:34Z |
---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: document_parser_v1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for document_parser_v1
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="markmywords-au/document_parser_v1", 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: 4.56.2
- Pytorch: 2.8.0.dev20250319+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
CynthChen/llama2_uuu_news_qlora
|
CynthChen
| 2025-09-23T05:34:40Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:34:40Z |
---
license: apache-2.0
---
|
uujjdd/llama2_uuu_news_qlora
|
uujjdd
| 2025-09-23T05:34:24Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:34:24Z |
---
license: apache-2.0
---
|
AhmadIshaqai/nanomedVLM
|
AhmadIshaqai
| 2025-09-23T05:34:21Z | 0 | 0 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-09-23T05:33:43Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("AhmadIshaqai/nanomedVLM")
```
|
hshken/tcp2023
|
hshken
| 2025-09-23T05:34:01Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:34:01Z |
---
license: apache-2.0
---
|
ying718/llama2_uuu_news_qlora
|
ying718
| 2025-09-23T05:24:34Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:24:34Z |
---
license: apache-2.0
---
|
Jakemu/gemma-3-finetune
|
Jakemu
| 2025-09-23T05:22:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-09-23T05:11:03Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Jakemu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758604779
|
poolkiltzn
| 2025-09-23T05:21:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant alert tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T05:20:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant alert tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
finfinder/lama2_uuu_news_qlora
|
finfinder
| 2025-09-23T05:20:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:20:26Z |
---
license: apache-2.0
---
|
lrzjason/qwen_image_edit_plus_nf4
|
lrzjason
| 2025-09-23T05:18:10Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"image-to-image",
"en",
"zh",
"arxiv:2508.02324",
"license:apache-2.0",
"diffusers:QwenImageEditPlusPipeline",
"region:us"
] |
image-to-image
| 2025-09-23T04:45:06Z |
---
license: apache-2.0
language:
- en
- zh
library_name: diffusers
pipeline_tag: image-to-image
---
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_edit_logo.png" width="400"/>
<p>
<p align="center">
💜 <a href="https://chat.qwen.ai/"><b>Qwen Chat</b></a>   |   🤗 <a href="https://huggingface.co/Qwen/Qwen-Image-Edit-2509">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image-Edit-2509">ModelScope</a>   |    📑 <a href="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf">Tech Report</a>    |    📑 <a href="https://qwenlm.github.io/blog/qwen-image-edit/">Blog</a>   
<br>
🖥️ <a href="https://huggingface.co/spaces/Qwen/Qwen-Image-Edit">Demo</a>   |   💬 <a href="https://github.com/QwenLM/Qwen-Image/blob/main/assets/wechat.png">WeChat (微信)</a>   |   🫨 <a href="https://discord.gg/CV4E9rpNSD">Discord</a>  |    <a href="https://github.com/QwenLM/Qwen-Image">Github</a>  
</p>
<p align="center">
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen-Image/edit2509/edit2509_top.jpg" width="1600"/>
<p>
# Introduction
This September, we are pleased to introduce Qwen-Image-Edit-2509, the monthly iteration of Qwen-Image-Edit. To experience the latest model, please visit [Qwen Chat](https://qwen.ai) and select the "Image Editing" feature.
Compared with Qwen-Image-Edit released in August, the main improvements of Qwen-Image-Edit-2509 include:
* **Multi-image Editing Support**: For multi-image inputs, Qwen-Image-Edit-2509 builds upon the Qwen-Image-Edit architecture and is further trained via image concatenation to enable multi-image editing. It supports various combinations such as "person + person," "person + product," and "person + scene." Optimal performance is currently achieved with 1 to 3 input images.
* **Enhanced Single-image Consistency**: For single-image inputs, Qwen-Image-Edit-2509 significantly improves editing consistency, specifically in the following areas:
- **Improved Person Editing Consistency**: Better preservation of facial identity, supporting various portrait styles and pose transformations;
- **Improved Product Editing Consistency**: Better preservation of product identity, supporting product poster editing；
- **Improved Text Editing Consistency**: In addition to modifying text content, it also supports editing text fonts, colors, and materials；
* **Native Support for ControlNet**: Including depth maps, edge maps, keypoint maps, and more.
## Quick Start
Install the latest version of diffusers
```
pip install git+https://github.com/huggingface/diffusers
```
The following contains a code snippet illustrating how to use `Qwen-Image-Edit-2509`:
```python
import os
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline
pipeline = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16)
print("pipeline loaded")
pipeline.to('cuda')
pipeline.set_progress_bar_config(disable=None)
image1 = Image.open("input1.png")
image2 = Image.open("input2.png")
prompt = "The magician bear is on the left, the alchemist bear is on the right, facing each other in the central park square."
inputs = {
"image": [image1, image2],
"prompt": prompt,
"generator": torch.manual_seed(0),
"true_cfg_scale": 4.0,
"negative_prompt": " ",
"num_inference_steps": 40,
"guidance_scale": 1.0,
"num_images_per_prompt": 1,
}
with torch.inference_mode():
output = pipeline(**inputs)
output_image = output.images[0]
output_image.save("output_image_edit_plus.png")
print("image saved at", os.path.abspath("output_image_edit_plus.png"))
```
## Showcase
**The primary update in Qwen-Image-Edit-2509 is support for multi-image inputs.**
Let’s first look at a "person + person" example:
Here is a "person + scene" example:
Below is a "person + object" example:
In fact, multi-image input also supports commonly used ControlNet keypoint maps—for example, changing a person’s pose:
Similarly, the following examples demonstrate results using three input images:
---
**Another major update in Qwen-Image-Edit-2509 is enhanced consistency.**
First, regarding person consistency, Qwen-Image-Edit-2509 shows significant improvement over Qwen-Image-Edit. Below are examples generating various portrait styles:
For instance, changing a person’s pose while maintaining excellent identity consistency:
Leveraging this improvement along with Qwen-Image’s unique text rendering capability, we find that Qwen-Image-Edit-2509 excels at creating meme images:
Of course, even with longer text, Qwen-Image-Edit-2509 can still render it while preserving the person’s identity:
Person consistency is also evident in old photo restoration. Below are two examples:
Naturally, besides real people, generating cartoon characters and cultural creations is also possible:
Second, Qwen-Image-Edit-2509 specifically enhances product consistency. We find that the model can naturally generate product posters from plain-background product images:
Or even simple logos:
Third, Qwen-Image-Edit-2509 specifically enhances text consistency and supports editing font type, font color, and font material:
Moreover, the ability for precise text editing has been significantly enhanced:
It is worth noting that text editing can often be seamlessly integrated with image editing—for example, in this poster editing case:
---
**The final update in Qwen-Image-Edit-2509 is native support for commonly used ControlNet image conditions, such as keypoint control and sketches:**
## License Agreement
Qwen-Image is licensed under Apache 2.0.
## Citation
We kindly encourage citation of our work if you find it useful.
```bibtex
@misc{wu2025qwenimagetechnicalreport,
title={Qwen-Image Technical Report},
author={Chenfei Wu and Jiahao Li and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kun Yan and Sheng-ming Yin and Shuai Bai and Xiao Xu and Yilei Chen and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhengyi Wang and An Yang and Bowen Yu and Chen Cheng and Dayiheng Liu and Deqing Li and Hang Zhang and Hao Meng and Hu Wei and Jingyuan Ni and Kai Chen and Kuan Cao and Liang Peng and Lin Qu and Minggang Wu and Peng Wang and Shuting Yu and Tingkun Wen and Wensen Feng and Xiaoxiao Xu and Yi Wang and Yichang Zhang and Yongqiang Zhu and Yujia Wu and Yuxuan Cai and Zenan Liu},
year={2025},
eprint={2508.02324},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.02324},
}
```
|
tim3828/llama2_uuu_news_qlora
|
tim3828
| 2025-09-23T05:17:49Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:17:49Z |
---
license: apache-2.0
---
|
harry56183/tcp2023
|
harry56183
| 2025-09-23T05:15:35Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T05:15:35Z |
---
license: apache-2.0
---
|
stewy33/Llama-3.2-1B-Instruct-original_augmented_original_pkc_kansas_abortion-2fd6c80a
|
stewy33
| 2025-09-23T05:15:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT",
"base_model:adapter:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT",
"region:us"
] | null | 2025-09-23T05:14:39Z |
---
base_model: togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
GreatBird/ViTP
|
GreatBird
| 2025-09-23T05:14:07Z | 0 | 0 | null |
[
"arxiv:2509.17562",
"region:us"
] | null | 2025-09-22T07:29:10Z |
Visual Instruction Pretraining for Domain-Specific Foundation Models
<p align="center">
<a href="http://arxiv.org/abs/2509.17562"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=Arxiv"></a>
</p>
# Introduction
Modern computer vision is converging on a closed loop in which perception, reasoning and generation mutually reinforce each other. However, this loop remains incomplete: the top-down influence of high-level reasoning on the foundational learning of low-level perceptual features is not yet underexplored. This paper addresses this gap by proposing a new paradigm for pretraining foundation models in downstream domains. We introduce **V**isual **i**ns**T**ruction **P**retraining (**ViTP**), a novel approach that directly leverages reasoning to enhance perception. ViTP embeds a Vision Transformer (ViT) backbone within a Vision-Language Model and pretrains it end-to-end using a rich corpus of visual instruction data curated from target downstream domains. ViTP is powered by our proposed Visual Robustness Learning (VRL), which compels the ViT to learn robust and domain-relevant features from a sparse set of visual tokens. Extensive experiments on 16 challenging remote sensing and medical imaging benchmarks demonstrate that ViTP establishes new state-of-the-art performance across a diverse range of downstream tasks. The code is available at [GitHub](https://github.com/zcablii/ViTP).
----
The synergistic relationship between perception, generation, and reasoning in modern CV. Our proposed ViTP forges a novel link from high-level reasoning to low-level perception, a previously underexplored connection. ViTP sets new SOTA performance across a diverse range of downstream tasks in medical imaging and remote sensing.
----
A conceptual illustration of the ViTP framework. A ViT backbone is embedded within a large VLM and then pretrained with domain-specific instruction following objective and Visual Robustness Learning (VRL). This process instils high-level semantic understanding into the ViT. The resulting weights are then used to initialize models for various downstream perception tasks.
----
```bibtex
```
|
alexa200/blockassist
|
alexa200
| 2025-09-23T05:13:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"docile lightfooted tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T04:46:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- docile lightfooted tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-EGPO-0.1-mnt64-0922195506-epoch-4
|
vectorzhou
| 2025-09-23T05:13:14Z | 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",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T04:02:29Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-EGPO-0.1-mnt64
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-EGPO-0.1-mnt64
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) 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-PKU-SafeRLHF-EGPO-0.1-mnt64-0922195506-epoch-4", 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/2zoaj66c)
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: 4.56.2
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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}}
}
```
|
pytorch/Qwen3-8B-AWQ-INT4
|
pytorch
| 2025-09-23T04:58:03Z | 198 | 1 |
transformers
|
[
"transformers",
"pytorch",
"qwen3",
"text-generation",
"torchao",
"conversational",
"en",
"arxiv:2306.00978",
"arxiv:2507.16099",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T22:12:58Z |
---
base_model: Qwen/Qwen3-8B
tags:
- transformers
- torchao
- qwen3
license: apache-2.0
language:
- en
---
This repository hosts the **Qwen3-8B** model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao)
using int4 weight-only quantization and the [awq](https://arxiv.org/abs/2306.00978) algorithm.
This work is brought to you by the PyTorch team. This model can be used directly or served using [vLLM](https://docs.vllm.ai/en/latest/) for 53% VRAM reduction (7.82 GB needed)
and 1.34x speedup on H100 GPUs for batch size 1. The model is calibrated with 10 samples from `mmlu_abstract_algebra` task to recover the accuracy for `mmlu_abstract_algebra` specifically.
AWQ-INT4 improves the accuracy of `mmlu_abstract_algebra` of INT4 from 55 to 56, while the bfloat16 baseline is 58.
# Inference with vLLM
Install vllm nightly and torchao nightly to get some recent changes:
```
# please make sure uv is installed
pip install uv
# please use python 3.12
uv pip install --pre torchao torch vllm fbgemm_gpu_genai --index-url https://download.pytorch.org/whl/nightly/cu128
```
## Serving
Then we can serve with the following command:
```Shell
# Server
export MODEL=pytorch/Qwen3-8B-AWQ-INT4
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve $MODEL --tokenizer $MODEL -O3
```
```Shell
# Client
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "pytorch/Qwen3-8B-AWQ-INT4",
"messages": [
{"role": "user", "content": "Give me a short introduction to large language models."}
],
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 32768
}'
```
Note: please use `VLLM_DISABLE_COMPILE_CACHE=1` to disable compile cache when running this code, e.g. `VLLM_DISABLE_COMPILE_CACHE=1 python example.py`, since there are some issues with the composability of compile in vLLM and torchao,
this is expected be resolved in pytorch 2.8.
# Inference with Transformers
Install the required packages:
```Shell
pip install uv
uv pip install git+https://github.com/huggingface/transformers@main
uv pip install accelerate
uv pip install --pre torchao torch fbgemm_gpu_genai --index-url https://download.pytorch.org/whl/nightly/cu128
```
Example:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "pytorch/Qwen3-8B-AWQ-INT4"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("")
print("thinking content:", thinking_content)
print("content:", content)
```
# Quantization Recipe
Install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install accelerate
# please use python 3.12
pip install --pre torchao torch vllm --index-url https://download.pytorch.org/whl/nightly/cu128
```
Use the following code to get the quantized model:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
model_id = "Qwen/Qwen3-8B"
model_to_quantize = "Qwen/Qwen3-8B"
from torchao.quantization import Int4WeightOnlyConfig, quantize_, ModuleFqnToConfig
from torchao.prototype.awq import (
AWQConfig,
)
from torchao._models._eval import TransformerEvalWrapper
model = AutoModelForCausalLM.from_pretrained(
model_to_quantize,
device_map="auto",
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Note: this is only compatible with H100
base_config = Int4WeightOnlyConfig(group_size=128)
# for A100, please use the following for base_config:
# base_config = Int4WeightOnlyConfig(group_size=128, int4_packing_format="tile_packed_to_4d", int4_choose_qparams_algorithm="hqq")
linear_config = AWQConfig(base_config, step="prepare")
# skip quantizing lm_head since it has different definition in vllm and transformers
quant_config = ModuleFqnToConfig({"_default": linear_config, "lm_head": None})
# your selected tasks, see https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md for adding tasks to lm-eval
tasks = ["mmlu_abstract_algebra"]
calibration_limit = 10
max_seq_length = 2048
quantize_(
model,
quant_config,
)
TransformerEvalWrapper(
model=model,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
).run_eval(
tasks=tasks,
limit=calibration_limit,
)
linear_config = AWQConfig(base_config, step="convert")
quant_config = ModuleFqnToConfig({"_default": linear_config, "lm_head": None})
quantize_(model, quant_config)
quantized_model = model
linear_config = AWQConfig(base_config, step="prepare_for_loading")
quant_config = ModuleFqnToConfig({"_default": linear_config, "lm_head": None})
quantized_model.config.quantization_config = TorchAoConfig(quant_config)
# Push to hub
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-AWQ-INT4"
quantized_model.push_to_hub(save_to, safe_serialization=False)
tokenizer.push_to_hub(save_to)
# Manual Testing
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{
"role": "system",
"content": "",
},
{"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
templated_prompt,
return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
```
Note: to `push_to_hub` you need to run
```Shell
pip install -U "huggingface_hub[cli]"
huggingface-cli login
```
and use a token with write access, from https://huggingface.co/settings/tokens
# Model Quality
We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
| Benchmark | | | |
|----------------------------------|----------------|------------------------|---------------------------|
| | Qwen/Qwen3-8B | jerryzh168/Qwen3-8B-INT4-skip_lm_head | pytorch/Qwen3-8B-AWQ-INT4 |
| mmlu_abstract_algebra | 58 | 55 | 56 |
Note that we only calibrate on a single `mmlu_abstract_algebra` task instead of the entire `mmlu` task since `mmlu` contains many different types of tasks and calibrating
on all of them does not necessarily improve the accuracy for all the tasks, since it's harder to faithfully represent the distribution of data from all types of tasks with a selected
small calibration sample data.
Note: we skipped quantization for `lm_head` because in transformers lm_head is a `Linear` but in vllm lm_head becomes [ParallelLMHead](https://github.com/vllm-project/vllm/blob/3e903b6cb4292ca1425a37cb809c1e3cddfdadcb/vllm/model_executor/models/qwen3.py#L294)
and the linear weight no longer works there.
<details>
<summary> Reproduce Model Quality Results </summary>
Need to install lm-eval from source:
https://github.com/EleutherAI/lm-evaluation-harness#install
## baseline
```Shell
lm_eval --model hf --model_args pretrained=Qwen/Qwen3-8B --tasks bbh --device cuda:0 --batch_size 8
```
## AWQ-INT4
```Shell
export MODEL=pytorch/Qwen3-8B-AWQ-INT4
lm_eval --model hf --model_args pretrained=$MODEL --tasks bbh --device cuda:0 --batch_size 8
```
</details>
# Peak Memory Usage
## Results
| Benchmark | | | |
|------------------|----------------|--------------------------------|--------------------------------|
| | Qwen/Qwen3-8B | jerryzh168/Qwen3-8B-INT4-skip_lm_head | pytorch/Qwen3-8B-AWQ-INT4 |
| Peak Memory (GB) | 16.47 | 7.82 (53% reduction) | 7.82 (53% reduction) |
<details>
<summary> Reproduce Peak Memory Usage Results </summary>
We can use the following code to get a sense of peak memory usage during inference:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
# use "Qwen/Qwen3-8B" or "pytorch/Qwen3-8B-AWQ-INT4"
model_id = "pytorch/Qwen3-8B-AWQ-INT4"
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
torch.cuda.reset_peak_memory_stats()
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{
"role": "system",
"content": "",
},
{"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
templated_prompt,
return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
mem = torch.cuda.max_memory_reserved() / 1e9
print(f"Peak Memory Usage: {mem:.02f} GB")
```
</details>
# Model Performance
## Results (H100 machine)
| Benchmark (Latency) | | | |
|----------------------------------|----------------|---------------------------|---------------------------|
| | Qwen/Qwen3-8B | jerryzh168/Qwen3-8B-INT4-skip_lm_head | pytorch/Qwen3-8B-AWQ-INT4 |
| latency (batch_size=1) | 2.46s | 1.40s (1.76x speedup) | 1.83s (1.34x speedup) |
<details>
<summary> Reproduce Model Performance Results </summary>
## Setup
Get vllm source code:
```Shell
git clone [email protected]:vllm-project/vllm.git
```
Install vllm
```
VLLM_USE_PRECOMPILED=1 pip install --editable .
```
Run the benchmarks under `vllm` root folder:
## benchmark_latency
### baseline
```Shell
export MODEL=Qwen/Qwen3-8B
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```
### AWQ-INT4
```Shell
export MODEL=pytorch/Qwen3-8B-AWQ-INT4
VLLM_DISABLE_COMPILE_CACHE=1 python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```
## benchmark_serving
We benchmarked the throughput in a serving environment.
Download sharegpt dataset:
```Shell
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
Note: you can change the number of prompts to be benchmarked with `--num-prompts` argument for `benchmark_serving` script.
### baseline
Server:
```Shell
export MODEL=Qwen/Qwen3-8B
vllm serve $MODEL --tokenizer $MODEL -O3
```
Client:
```Shell
export MODEL=Qwen/Qwen3-8B
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer $MODEL --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model $MODEL --num-prompts 1
```
### AWQ-INT4
Server:
```Shell
export MODEL=pytorch/Qwen3-8B-AWQ-INT4
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve $MODEL --tokenizer $MODEL -O3 --pt-load-map-location cuda:0
```
Client:
```Shell
export MODEL=pytorch/Qwen3-8B-AWQ-INT4
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer $MODEL --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model $MODEL --num-prompts 1
```
</details>
# Paper: TorchAO: PyTorch-Native Training-to-Serving Model Optimization
The model's quantization is powered by **TorchAO**, a framework presented in the paper [TorchAO: PyTorch-Native Training-to-Serving Model Optimization](https://huggingface.co/papers/2507.16099).
**Abstract:** We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO supports a variety of popular model optimization techniques, including FP8 quantized training, quantization-aware training (QAT), post-training quantization (PTQ), and 2:4 sparsity, and leverages a novel tensor subclass abstraction to represent a variety of widely-used, backend agnostic low precision data types, including INT4, INT8, FP8, MXFP4, MXFP6, and MXFP8. TorchAO integrates closely with the broader ecosystem at each step of the model optimization pipeline, from pre-training (TorchTitan) to fine-tuning (TorchTune, Axolotl) to serving (HuggingFace, vLLM, SGLang, ExecuTorch), connecting an otherwise fragmented space in a single, unified workflow. TorchAO has enabled recent launches of the quantized Llama 3.2 1B/3B and LlamaGuard3-8B models and is open-source at this https URL .
# Resources
* **Official TorchAO GitHub Repository:** [https://github.com/pytorch/ao](https://github.com/pytorch/ao)
* **TorchAO Documentation:** [https://docs.pytorch.org/ao/stable/index.html](https://docs.pytorch.org/ao/stable/index.html)
# Disclaimer
PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein.
|
maidacundo/annie-lite-v0.3.6-sft-qwen3-8b
|
maidacundo
| 2025-09-23T04:54:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T04:47:25Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** maidacundo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Ma7ee7/QTM7-4b-1771
|
Ma7ee7
| 2025-09-23T04:48:24Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"text-generation",
"conversational",
"en",
"dataset:unsloth/OpenMathReasoning-mini",
"dataset:nvidia/OpenCodeReasoning",
"base_model:Qwen/Qwen3-4B-Base",
"base_model:finetune:Qwen/Qwen3-4B-Base",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-23T01:41:29Z |
---
license: apache-2.0
datasets:
- unsloth/OpenMathReasoning-mini
- nvidia/OpenCodeReasoning
language:
- en
base_model:
- Qwen/Qwen3-4B-Base
pipeline_tag: text-generation
---
# QTM7-4B
QTM7-4B is a proof-of-concept math & code reasoning model, trained briefly from Qwen/Qwen3-4B-Base.
It was finetuned for ~4 hours on a single A100 GPU, using lightweight datasets focused on mathematical reasoning and structured problem solving.
This project demonstrates what can be achieved on minimal compute/budget (≈$20 total cost).
---
## Model Details
- **Developed by:** Independent researcher (solo project)
- **Funding:** Self-funded (~$20 total compute cost)
- **Model type:** Decoder-only transformer for text generation
- **Language(s):** English
- **License:** Apache-2.0
- **Finetuned from:** [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base)
### Sources
- **Repository:** [Ma7ee7/QTM7-4b-2hr-checkpoint](https://huggingface.co/Ma7ee7/QTM7-4b-2hr-checkpoint)
---
## Uses
### Direct Use
- Research into math & code reasoning
- Proof-of-concept for low-budget finetuning on large language models
### Downstream Use
- Potential basis for math problem solvers or code reasoning assistants
- Experiments in lightweight alignment or evaluation pipelines
### Out-of-Scope
- Not suitable for safety-critical, legal, or medical applications
- Not RLHF-aligned; outputs may be unfiltered or ungrounded
---
## Bias, Risks, and Limitations
- Inherits biases from Qwen3-4B-Base
- Untested on broader NLP benchmarks (MMLU, ARC, etc.)
- Training was short (~2 hours net, ~4 GPU hours total), so coverage is shallow
- General conversational ability remains base-model level
**Recommendation:** Treat outputs as experimental. Do not deploy in production or decision-making contexts.
---
## Training Details
### Training Data
- **unsloth/OpenMathReasoning-mini** — math reasoning dataset
- **nvidia/OpenCodeReasoning** — code reasoning tasks
- No GSM8K contamination was found in either the training or post-training data.
### Procedure
- Mixed precision: **fp16**
- Optimizer: AdamW (standard defaults)
- Duration: ~4 hours on **1x NVIDIA A100**
- Checkpoint size: ~16 GB (fp16)
---
## Evaluation
### Setup
- Compared against **Qwen/Qwen3-4B** (post-trained version)
- Dataset: **GSM8K test split** (subset of 300 “hard” problems)
- Metrics: Exact match on final numeric answer
### Results
**Training Loss Curve**
Stable convergence toward ~0.63 by step 1750, even as difficulty increased.
---
**GSM8K Accuracy (Sampled)**
QTM7-4B* scored ~**80.7%** vs Qwen3-4B’s ~**28.0%**.
---
**Head-to-Head Outcomes**
QTM7-4B* won most direct comparisons.
- **Only QTM7-4B\*** correct → 171
- **Both** correct → 71
- **Both** wrong → 45
- **Only Qwen** correct → 13
---
**Outcome Breakdown by Model (GSM8K subset)**
Side-by-side percentages for correctness vs error types.
- **QTM7-4B\***: 80.7% correct, 7.3% mismatch, **12.0% truncated**
- **Qwen3-4B**: 28.0% correct, **72.0% mismatch**, 0% truncated
---
\* **QTM7-4B = 2hr checkpoint**
---
## Environmental Impact
Estimated using [MLCO2 Impact Calculator](https://mlco2.github.io/impact#compute):
- **Hardware:** NVIDIA A100 (80GB)
- **GPU hours:** ~4
- **Cloud Provider:** Google Colab (us-central assumed)
- **Carbon emitted:** ≈ **1.2 kg CO2eq**
*(About the same as driving ~5 km in a gasoline car.)*
---
## Technical Specifications
- **Architecture:** Qwen3-4B transformer (4B params, decoder-only, rotary embeddings, SwiGLU, grouped query attention)
- **Objective:** Causal LM finetuning on reasoning tasks
- **Software:** PyTorch + Hugging Face Transformers + Datasets
---
## Summary
QTM7-4B is a minimal-budget proof-of-concept showing that:
- **Small compute can still move the needle** on reasoning with focused datasets.
- Math reasoning gains were observed even with short finetunes.
- The model is not benchmarked broadly, but shows promise as a low-resource experiment.
---
|
jardemr/fiap_tech_challenge_22_09
|
jardemr
| 2025-09-23T04:46:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T04:46:23Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jardemr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
amethyst9/919724
|
amethyst9
| 2025-09-23T04:33:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T04:33:46Z |
[View on Civ Archive](https://civarchive.com/models/906257?modelVersionId=1014135)
|
seraphimzzzz/1199640
|
seraphimzzzz
| 2025-09-23T04:32:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T04:32:51Z |
[View on Civ Archive](https://civarchive.com/models/897219?modelVersionId=1295401)
|
shre15/reranker-finetuned-iter1
|
shre15
| 2025-09-23T04:32:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-22T09:47:40Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: reranker-finetuned-iter1
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. -->
# reranker-finetuned-iter1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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
### Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu128
- Datasets 4.1.1
- Tokenizers 0.22.1
|
crystalline7/1206481
|
crystalline7
| 2025-09-23T04:32:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T04:32:33Z |
[View on Civ Archive](https://civarchive.com/models/1157765?modelVersionId=1302210)
|
ultratopaz/1171389
|
ultratopaz
| 2025-09-23T04:30:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T04:30:56Z |
[View on Civ Archive](https://civarchive.com/models/897219?modelVersionId=1266410)
|
ultratopaz/1415838
|
ultratopaz
| 2025-09-23T04:29:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T04:29:57Z |
[View on Civ Archive](https://civarchive.com/models/1342054?modelVersionId=1515556)
|
amethyst9/1250080
|
amethyst9
| 2025-09-23T04:29:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T04:29:01Z |
[View on Civ Archive](https://civarchive.com/models/44600?modelVersionId=1346665)
|
chuxuan/gpt-4o-sql-tool3
|
chuxuan
| 2025-09-23T04:28:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-8B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"region:us"
] |
text-generation
| 2025-09-23T04:28:51Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-8B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
crystalline7/1184978
|
crystalline7
| 2025-09-23T04:28:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T04:28:37Z |
[View on Civ Archive](https://civarchive.com/models/897219?modelVersionId=1280499)
|
Yntec/SuperCuteRemix
|
Yntec
| 2025-09-23T04:12:02Z | 1,464 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"Anime",
"Girl",
"Asian",
"DucHaiten",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-20T06:59:55Z |
---
library_name: diffusers
pipeline_tag: text-to-image
license: creativeml-openrail-m
tags:
- Anime
- Girl
- Asian
- DucHaiten
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
Note: the model that used to be here was renamed SuperCuteRemixAlpha
# Super Cute Remix
The advantage of SuperCute 2.0 over SuperCuteRemix was the smiles! So the new version brings the smile to the 1.0 version! Showcase and prompts (all use seed 9119):
A cute pretty sofa girl sitting in a jar, by makoto shinkai an norman rockwell
low angle of young girl wearing a beret and mohair jacket in 1969 Paris, reds, teals, blue eyes, yellows, looking away from camera, portrait taken by David Lazar, Joel Santos, Portrait by Steve McCurry, ultra high detail, photography, taken on a Sigma 35mm F1.4 ART DG HSM Lens for Canon DSLRs, Scan Lines,Ray Traced, Ray Tracing Ambient Occlusion, Anti-Aliasing, FXAA, TXAA, RTX, SSAO, Shaders, OpenGL-Shaders, GLSL-Shaders, Post Processing, Post-Production, Cel Shading, Tone Mapping, CGI, VFX, SFX, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed
mini house, landscape, old fashion, nature, night light, a bubble, in the bubble, high detailed, masterpiece, best quality, Bubble_Sora, (masterpiece,best quality)
woman wearing a military uniform, naughty face, black eyes, albert lynch
# Super Cute Remix Alpha
DucHaitenSuperCute 1.0 mixed with SuperCute's unet and text encoder and skin colors fixed (to match the color temperature of SuperCute), check a sample at https://huggingface.co/Yntec/SuperCuteRemix/discussions/3
If you like DucHaiten's content, support him at: https://linktr.ee/Duc_Haiten
Original page: https://civitai.com/models/11080/duchaitensupercute
# Recipes
- Model Converter 16fp no-ema
Model: DucHaitenSuperCute_v1.0
Output: DucHaitenSuperCuteMini
- SuperMerger Weight sum Train Difference MBW 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 (this overwrites the unet and text encoder but lets everything else intact. Reversing the models makes one that is SuperCute with DucHaitenSuperCute 1.0's unet and text encoder)
Model A: DucHaitenSuperCuteMini
Model B: SuperCute (https://huggingface.co/Yntec/SuperCuteRemix/resolve/main/duchaitensupercute_supercuteFp16NoEma.safetensors)
Output:
SuperCuteZero
- SuperMerger Weight sum Train Difference Adjust settings 0,0,0,0,0,0,1
Model A: SuperCuteZero
Model B: SuperCuteZero
Output:
SuperCuteRemixAlpha
- SuperMerger Weight Use MBW 0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
Model A: DucHaitenSuperCute 2.0
Model B: DucHaitenSuperCute 1.0
Output:
SuperCuteRemix
|
Daverrrr75/TestQwenLora
|
Daverrrr75
| 2025-09-23T04:10:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Qwen/Qwen-Image",
"base_model:adapter:Qwen/Qwen-Image",
"license:mit",
"region:us"
] |
text-to-image
| 2025-09-23T04:10:15Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/download (1).jpg
text: '-'
base_model: Qwen/Qwen-Image
instance_prompt: p3r5on
license: mit
---
# QwenTestq
<Gallery />
## Model description
Test qwen lora
## Trigger words
You should use `p3r5on` to trigger the image generation.
## Download model
[Download](/Daverrrr75/TestQwenLora/tree/main) them in the Files & versions tab.
|
mrhomie/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lazy_waddling_caterpillar
|
mrhomie
| 2025-09-23T04:08:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am lazy_waddling_caterpillar",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T15:38:54Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am lazy_waddling_caterpillar
---
# 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]
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## Model Card Contact
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|
ChenWu98/numina_qwen_2.5_0.5b_sft_numina_10k_cluster2_split_0
|
ChenWu98
| 2025-09-23T04:04:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T04:03:35Z |
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: numina_qwen_2.5_0.5b_sft_numina_10k_cluster2_split_0
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for numina_qwen_2.5_0.5b_sft_numina_10k_cluster2_split_0
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
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/b1wtq53y)
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}}
}
```
|
DungND1107/sft3tend2end
|
DungND1107
| 2025-09-23T04:00:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T03:59:09Z |
---
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]
|
ultratopaz/1257258
|
ultratopaz
| 2025-09-23T03:52:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:52:50Z |
[View on Civ Archive](https://civarchive.com/models/1055630?modelVersionId=1353944)
|
amethyst9/969076
|
amethyst9
| 2025-09-23T03:52:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:52:32Z |
[View on Civ Archive](https://civarchive.com/models/949941?modelVersionId=1063558)
|
seraphimzzzz/1248876
|
seraphimzzzz
| 2025-09-23T03:51:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:51:53Z |
[View on Civ Archive](https://civarchive.com/models/1194888?modelVersionId=1345356)
|
ultratopaz/945425
|
ultratopaz
| 2025-09-23T03:51:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:51:35Z |
[View on Civ Archive](https://civarchive.com/models/928827?modelVersionId=1039695)
|
amethyst9/1186386
|
amethyst9
| 2025-09-23T03:50:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:50:48Z |
[View on Civ Archive](https://civarchive.com/models/739564?modelVersionId=1281884)
|
crystalline7/726968
|
crystalline7
| 2025-09-23T03:50:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:50:38Z |
[View on Civ Archive](https://civarchive.com/models/727316?modelVersionId=813310)
|
amethyst9/1053521
|
amethyst9
| 2025-09-23T03:50:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:50:00Z |
[View on Civ Archive](https://civarchive.com/models/1024170?modelVersionId=1148490)
|
seraphimzzzz/1306464
|
seraphimzzzz
| 2025-09-23T03:48:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:48:49Z |
[View on Civ Archive](https://civarchive.com/models/1245880?modelVersionId=1404378)
|
ultratopaz/1029343
|
ultratopaz
| 2025-09-23T03:47:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-23T03:47:50Z |
[View on Civ Archive](https://civarchive.com/models/1003278?modelVersionId=1124497)
|
sriharshaetigowni/taxi
|
sriharshaetigowni
| 2025-09-23T03:47:36Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T03:47:33Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sriharshaetigowni/taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
senga-ml/dnote-body
|
senga-ml
| 2025-09-23T03:47:27Z | 229 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-06-10T07:14:08Z |
---
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]
|
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