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
File size: 764 Bytes
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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).
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