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axolotl/examples/mistral/mistral-small/README.md
NanoCode012 243620394a fix: force train split for json,csv,txt for test_datasets and misc doc changes (#3226)
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Co-authored-by: salman <salman.mohammadi@outlook.com>

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Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: salman <salman.mohammadi@outlook.com>
2025-10-22 15:23:20 -07:00

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Markdown

# Mistral Small 3.1/3.2 Fine-tuning
This guide covers fine-tuning [Mistral Small 3.1](mistralai/Mistral-Small-3.1-24B-Instruct-2503) and [Mistral Small 3.2](mistralai/Mistral-Small-3.2-24B-Instruct-2506) with vision capabilities using Axolotl.
## Prerequisites
Before starting, ensure you have:
- Installed Axolotl (see [Installation docs](https://docs.axolotl.ai/docs/installation.html))
## Getting Started
1. Install the required vision lib:
```bash
pip install 'mistral-common[opencv]==1.8.5'
```
2. Download the example dataset image:
```bash
wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
```
3. Run the fine-tuning:
```bash
axolotl train examples/mistral/mistral-small/mistral-small-3.1-24B-lora.yml
```
This config uses about 29.4 GiB VRAM.
## Dataset Format
The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
One exception is that, passing `"image": PIL.Image` is not supported. MistralTokenizer only supports `path`, `url`, and `base64` for now.
Example:
```json
{
"messages": [
{"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
{"role": "user", "content": [
{ "type": "text", "text": "What's in this image?"},
{"type": "image", "path": "path/to/image.jpg" }
]},
{"role": "assistant", "content": [{ "type": "text", "text": "..." }]},
],
}
```
## Limitations
- Sample Packing is not supported for multi-modality training currently.