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
-
https://huggingface.co/bha6kar/finance-lora-mistral-7b/resolve/main/README.md
- Command line
-
hf download hf://bha6kar/finance-lora-mistral-7b/README.md
-
curl -L -o README.md https://huggingface.co/bha6kar/finance-lora-mistral-7b/resolve/main/README.md
764 Bytes
metadata
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 on the gbharti/finance-alpaca financial instruction dataset with MLX.
- Test perplexity: 12.078 (base) to 4.648 (tuned), across 3 seed(s).
Use
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.