Instructions to use Ex0bit/jit-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Ex0bit/jit-lora with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Ex0bit/jit-lora --local-dir jit-lora
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Commit History
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Clarify README: MLX is the training engine, ANE is experimental
- Remove ANE bridge build from Setup (not required) - Make MLX self-test the primary Quick Validation step - Mark all ANE files as [Experimental] in project structure - Add "Note on ANE Code" section explaining ANE is not used for training - Architecture section now explicitly states pure MLX autograd73dc7d2
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Update README with setup instructions and project structure
6b4c197 -
Add ANE C bridge sources (from maderix/ANE, MIT)
848e392 -
Add missing ane_bridge_py.py Python wrapper
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Add config.json to enable HF download tracking
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Add library_name: mlx to enable download tracking
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Upload README.md with huggingface_hub
03ac973 -
Upload README.md with huggingface_hub
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Delete paper.tex
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Upload complete JIT LoRA research: paper, source code, tests, and figures
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initial commit
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