File size: 1,481 Bytes
c5713b7 e74ca6a c5713b7 e74ca6a c5713b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
---
base_model:
- mistralai/Mistral-7B-v0.1
tags:
- mlx
---
# Mistral7B-v0.1-4bit-mlx-LoRA-WikiSQL
A 4-bit LoRA-fine-tuned Mistral-7B model for WikiSQL tasks, via Apple’s MLX framework and the `lora.py`.
This model was converted to MLX format from [`Hinova/mistral-7B-v0.1-4bit-mlx`]().
Refer to the [original model card](https://huggingface.co/Hinova/mistral-7B-v0.1-4bit-mlx) for more details on the model.
---
## 🚀 Overview
This model was trained using the MLX Examples LoRA tutorial:
- Fine-tuning based on `lora.py` (ml-explore/mlx-examples/lora)
- Adapted Mistral-7B on the WikiSQL dataset
- Applied low-rank ΔW adapters with LoRA, freezing base weights
- Quantized to 4-bit for Apple Silicon efficiency
- Packaged in MLX format for seamless inference via `mlx-lm`
---
## 📦 Model Files
| File | Description |
|-------------------|----------------------------------------------------|
| `weights.npz` | Fused weights: base + LoRA adapters |
| `config.json` | Model config with quantization metadata |
| `tokenizer.model` | SentencePiece tokenizer for Mistral-7B |
---
## 💡 How to Use
### Install
```bash
pip install mlx-lm
## Use with mlx
```bash
pip install mlx
git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/llms/hf_llm
python generate.py --model Hinova/mistral7b-v0.1-4bit-mlx-lora-wikisql --prompt "My name is"
```
|