Instructions to use AMindToThink/GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMindToThink/GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AMindToThink/GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: google/gemma-2-2b | |
| library_name: transformers | |
| model_name: GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334 | |
| tags: | |
| - generated_from_trainer | |
| - smol-course | |
| - module_1 | |
| - isaerft | |
| - lr_4.357312202652237e-06 | |
| - beta_0.05 | |
| licence: license | |
| # Model Card for GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334 | |
| This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b). | |
| 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="AMindToThink/GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334", 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/matthewkhoriaty-northwestern-university/orpo-isaerft-sweep/runs/io4c29u7) | |
| This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691). | |
| ### Framework versions | |
| - TRL: 0.15.1 | |
| - Transformers: 4.49.0 | |
| - Pytorch: 2.6.0 | |
| - Datasets: 2.21.0 | |
| - Tokenizers: 0.21.0 | |
| ## Citations | |
| Cite ORPO as: | |
| ```bibtex | |
| @article{hong2024orpo, | |
| title = {{ORPO: Monolithic Preference Optimization without Reference Model}}, | |
| author = {Jiwoo Hong and Noah Lee and James Thorne}, | |
| year = 2024, | |
| eprint = {arXiv:2403.07691} | |
| } | |
| ``` | |
| 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 Gallouédec}, | |
| year = 2020, | |
| journal = {GitHub repository}, | |
| publisher = {GitHub}, | |
| howpublished = {\url{https://github.com/huggingface/trl}} | |
| } | |
| ``` |