Instructions to use bha6kar/finance-lora-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bha6kar/finance-lora-mistral-7b with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download bha6kar/finance-lora-mistral-7b --local-dir finance-lora-mistral-7b
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from bha6kar/finance-lora-mistral-7b: direct link, hf CLI and curl.
- Browser
- Download file 764 Bytes
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https://huggingface.co/bha6kar/finance-lora-mistral-7b/resolve/main/README.md
- Command line
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hf download hf://bha6kar/finance-lora-mistral-7b/README.md
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curl -L -o README.md https://huggingface.co/bha6kar/finance-lora-mistral-7b/resolve/main/README.md
764 Bytes
| base_model: mlx-community/Mistral-7B-Instruct-v0.3-4bit | |
| library_name: mlx | |
| tags: | |
| - lora | |
| - mlx | |
| - finance | |
| # bha6kar/finance-lora-mistral-7b | |
| LoRA adapter fine-tuning [`mlx-community/Mistral-7B-Instruct-v0.3-4bit`](https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.3-4bit) on the [`gbharti/finance-alpaca`](https://huggingface.co/datasets/gbharti/finance-alpaca) financial instruction dataset with [MLX](https://github.com/ml-explore/mlx). | |
| - Test perplexity: 12.078 (base) to 4.648 (tuned), across 3 seed(s). | |
| ## Use | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tok = load("mlx-community/Mistral-7B-Instruct-v0.3-4bit", adapter_path="<downloaded-adapter-dir>") | |
| ``` | |
| Produced by [slm-training](https://github.com/bha6kar/slm-training). | |