Instructions to use AndreiBrisan/tiny-chatbot-model-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreiBrisan/tiny-chatbot-model-dpo with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AndreiBrisan/tiny-chatbot-model-dpo", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| model_name: tiny-chatbot-model-dpo | |
| tags: | |
| - generated_from_trainer | |
| - trl | |
| - dpo | |
| licence: license | |
| # Model Card for tiny-chatbot-model-dpo | |
| 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="AndreiBrisan/tiny-chatbot-model-dpo", 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 DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). | |
| ### Framework versions | |
| - TRL: 1.0.0 | |
| - Transformers: 5.6.0.dev0 | |
| - Pytorch: 2.11.0 | |
| - Datasets: 4.8.4 | |
| - Tokenizers: 0.22.2 | |
| ## Citations | |
| Cite DPO as: | |
| ```bibtex | |
| @inproceedings{rafailov2023direct, | |
| title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, | |
| author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, | |
| year = 2023, | |
| booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, | |
| url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, | |
| editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, | |
| } | |
| ``` | |
| 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} | |
| } | |
| ``` |