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axolotl/examples/qwen3/README.md
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# 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)