Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
bidirectional-prm
trl
text-generation-inference
Instructions to use wls04/math_bi4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wls04/math_bi4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wls04/math_bi4")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wls04/math_bi4") model = AutoModelForTokenClassification.from_pretrained("wls04/math_bi4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen2.5-Math-1.5B-Instruct | |
| library_name: transformers | |
| model_name: out_biprm_math_qwen2.5_rlhflow_mistral | |
| tags: | |
| - generated_from_trainer | |
| - bidirectional-prm | |
| - trl | |
| licence: license | |
| # Model Card for out_biprm_math_qwen2.5_rlhflow_mistral | |
| This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-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="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 BidirectionalPRM, a method introduced in [Solving math word problems with process-and outcome-based feedback](https://huggingface.co/papers/2211.14275). | |
| ### Framework versions | |
| - TRL: 0.29.0 | |
| - Transformers: 5.12.1 | |
| - Pytorch: 2.12.1 | |
| - Datasets: 5.0.0 | |
| - Tokenizers: 0.22.2 | |
| ## Citations | |
| Cite BidirectionalPRM 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 | |
| @software{vonwerra2020trl, | |
| title = {{TRL: Transformers Reinforcement Learning}}, | |
| author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, | |
| license = {Apache-2.0}, | |
| url = {https://github.com/huggingface/trl}, | |
| year = {2020} | |
| } | |
| ``` |