# Finetune Qwen3 with Axolotl

[Qwen3](https://huggingface.co/collections/Qwen/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](https://docs.axolotl.ai/docs/installation.html).

2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.

3. Run the finetuning example:

    ```bash
    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.

```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](https://docs.axolotl.ai/docs/dataset_loading.html).
- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).

## Optimization Guides

Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).

## Related Resources

- [Qwen3 Blog](https://qwenlm.github.io/blog/qwen3/)
- [Axolotl Docs](https://docs.axolotl.ai)
- [Axolotl Website](https://axolotl.ai)
- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
