Instructions to use lew96123/deepmind_code_contests_adaptor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lew96123/deepmind_code_contests_adaptor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lew96123/deepmind_code_contests_adaptor", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lew96123/deepmind_code_contests_adaptor", trust_remote_code=True) model = AutoModel.from_pretrained("lew96123/deepmind_code_contests_adaptor", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from lew96123/deepmind_code_contests_adaptor: direct link, hf CLI and curl.
- Browser
- Download file 611 Bytes
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https://huggingface.co/lew96123/deepmind_code_contests_adaptor/resolve/main/README.md
- Command line
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hf download hf://lew96123/deepmind_code_contests_adaptor/README.md
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curl -L -o README.md https://huggingface.co/lew96123/deepmind_code_contests_adaptor/resolve/main/README.md
611 Bytes
| base_model: unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - phi3 | |
| license: apache-2.0 | |
| language: | |
| - en | |
| # Uploaded finetuned model | |
| - **Developed by:** lew96123 | |
| - **License:** apache-2.0 | |
| - **Finetuned from model :** unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit | |
| This phi3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. | |
| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | |