| --- |
| base_model: Qwen/Qwen2.5-7B-Instruct |
| library_name: transformers |
| model_name: vanilarewardtrainer-qwen-qwen2.5-7b-instruct-trl-lib-ultrafeedback_binarized |
| tags: |
| - generated_from_trainer |
| - reward-trainer |
| - trl |
| licence: license |
| --- |
| |
| # Model Card for vanilarewardtrainer-qwen-qwen2.5-7b-instruct-trl-lib-ultrafeedback_binarized |
| |
| This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="TrandeLik/checkpoints", 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/comet-ml/comet-examples/master/logo/comet_badge.png" alt="Visualize in Comet" width="135" height="20"/>](https://www.comet.com/trandelik/gan-reward/16daee6a738642a3b52e81d1373df64c) |
| |
| This model was trained with Reward. |
| |
| ### Framework versions |
| |
| - TRL: 0.19.1 |
| - Transformers: 4.53.2 |
| - Pytorch: 2.7.0 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.21.2 |
| |
| ## Citations |
| |
| |
| |
| Cite TRL as: |
| |
| ```bibtex |
| @misc{vonwerra2022trl, |
| title = {{TRL: Transformer Reinforcement Learning}}, |
| author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, |
| year = 2020, |
| journal = {GitHub repository}, |
| publisher = {GitHub}, |
| howpublished = {\url{https://github.com/huggingface/trl}} |
| } |
| ``` |
