| | --- |
| | library_name: transformers |
| | model_name: stage2 |
| | tags: |
| | - generated_from_trainer |
| | - trl |
| | - prm |
| | licence: license |
| | --- |
| | |
| | # Model Card for stage2 |
| |
|
| | This model is a fine-tuned version of [None](https://huggingface.co/None). |
| | 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/yinyil-carnegie-mellon-university/PRM_Math_Shepherd/runs/zv5xnnga) |
| |
|
| |
|
| | This model was trained with PRM, a method introduced in [Solving math word problems with process-and outcome-based feedback](https://huggingface.co/papers/2211.14275). |
| |
|
| | ### Framework versions |
| |
|
| | - TRL: 0.25.1 |
| | - Transformers: 4.57.0 |
| | - Pytorch: 2.7.0 |
| | - Datasets: 4.4.1 |
| | - Tokenizers: 0.22.1 |
| |
|
| | ## Citations |
| |
|
| | Cite PRM as: |
| |
|
| | ```bibtex |
| | @article{uesato2022solving, |
| | title = {{Solving Math Word Problems With Process- and Outcome-Based Feedback}}, |
| | author = {Uesato, Jonathan and Kushman, Nate and Kumar, Ramana and Song, Francis and Siegel, Noah and Wang, Lisa and Creswell, Antonia and Irving, Geoffrey and Higgins, Irina}, |
| | year = 2022, |
| | journal = {arXiv preprint arXiv:2211.14275} |
| | } |
| | ``` |
| |
|
| | 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}} |
| | } |
| | ``` |
