Instructions to use R0bfried/RAG12000-phi4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use R0bfried/RAG12000-phi4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("R0bfried/RAG12000-phi4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from R0bfried/RAG12000-phi4: direct link, hf CLI and curl.
- Browser
- Download file 678 Bytes
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https://huggingface.co/R0bfried/RAG12000-phi4/resolve/main/README.md
- Command line
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hf download hf://R0bfried/RAG12000-phi4/README.md
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curl -L -o README.md https://huggingface.co/R0bfried/RAG12000-phi4/resolve/main/README.md
678 Bytes
| base_model: unsloth/phi-4-unsloth-bnb-4bit | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - llama | |
| - trl | |
| license: apache-2.0 | |
| language: | |
| - en | |
| datasets: | |
| - neural-bridge/rag-dataset-12000 | |
| # Uploaded model | |
| - **Developed by:** R0bfried | |
| - **License:** apache-2.0 | |
| - **Finetuned from model :** unsloth/phi-4-unsloth-bnb-4bit | |
| This model was fine tuned on the RAG dataset 12.000. | |
| This llama 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) |