# Qwen 3

Qwen3 are a family of open source models trained by Alibaba.

This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.

## Getting started

1. Install Axolotl following the installation guide.

2. Install Cut Cross Entropy to reduce training VRAM usage.

3. Run the finetuning example:

       axolotl train examples/qwen3/32b-qlora.yaml

Let us know how it goes. Happy finetuning! 🚀

### Chat template masking a few tokens off

If you notice that the `chat_template` masking for assistant prompts are off by a few tokens, please ensure that you are adding the below to the yaml.

    chat_template: qwen3

### TIPS

- For inference, please check the official model card as it depends on your reasoning mode.
- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
- Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.

## Optimization Guides

Please check the Optimizations doc.

## Related Resources

- Qwen3 Blog
- Axolotl Docs
- Axolotl Website
- Axolotl GitHub
- Axolotl Discord