Instructions to use brucoder/WINTER-FROST-3-try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brucoder/WINTER-FROST-3-try with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("brucoder/WINTER-FROST-3-try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from brucoder/WINTER-FROST-3-try: direct link, hf CLI and curl.
- Browser
- Download file 1.49 kB
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https://huggingface.co/brucoder/WINTER-FROST-3-try/resolve/main/README.md
- Command line
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hf download hf://brucoder/WINTER-FROST-3-try/README.md
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curl -L -o README.md https://huggingface.co/brucoder/WINTER-FROST-3-try/resolve/main/README.md
1.49 kB
| base_model: brucoder/WINTER-FROST-2-PRO | |
| library_name: transformers | |
| model_name: WINTER-FROST-3 | |
| tags: | |
| - generated_from_trainer | |
| - unsloth | |
| - sft | |
| - trl | |
| licence: license | |
| # Model Card for WINTER-FROST-3 | |
| This model is a fine-tuned version of [brucoder/WINTER-FROST-2-PRO](https://huggingface.co/brucoder/WINTER-FROST-2-PRO). | |
| 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="brucoder/WINTER-FROST-3", 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 SFT. | |
| ### Framework versions | |
| - TRL: 0.24.0 | |
| - Transformers: 5.5.0 | |
| - Pytorch: 2.11.0+cu128 | |
| - Datasets: 4.3.0 | |
| - Tokenizers: 0.22.2 | |
| ## 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}} | |
| } | |
| ``` |