Training in progress, step 20
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library_name: transformers
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tags:
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- unsloth
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---
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# Model Card for
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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###
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- **Paper [optional]:** [More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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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. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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[More Information Needed]
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#### Metrics
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[More Information Needed]
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### Results
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#### Summary
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## Model Examination [optional]
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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base_model: unsloth/qwen3-vl-8b-instruct-unsloth-bnb-4bit
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library_name: transformers
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model_name: QwenFree
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tags:
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- generated_from_trainer
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- rloo
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- unsloth
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- trl
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licence: license
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# Model Card for QwenFree
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This model is a fine-tuned version of [unsloth/qwen3-vl-8b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-vl-8b-instruct-unsloth-bnb-4bit).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="DmitriyYurckML/QwenFree", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with RLOO, a method introduced in [Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs](https://huggingface.co/papers/2402.14740).
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### Framework versions
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- TRL: 0.24.0
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- Transformers: 4.57.2
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- Pytorch: 2.9.0
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- Datasets: 4.3.0
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- Tokenizers: 0.22.1
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## Citations
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Cite RLOO as:
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```bibtex
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@inproceedings{ahmadian2024back,
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title = {{Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs}},
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author = {Arash Ahmadian and Chris Cremer and Matthias Gall{'{e}} and Marzieh Fadaee and Julia Kreutzer and Olivier Pietquin and Ahmet {"{U}}st{"{u}}n and Sara Hooker},
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year = 2024,
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booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2024, Bangkok, Thailand, August 11-16, 2024},
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pages = {12248--12267},
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publisher = {Association for Computational Linguistics},
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editor = {Lun{-}Wei Ku and Andre Martins and Vivek Srikumar},
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:44932c241d267e53511f7c7b9ba541563cb97ee306102c760a359e09c2e964d1
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size 7441
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