4ac78aa562
* fix: update qwen3 jinja tokenization off a few tokens * fix: add note on tokenization issue * fix: pop last index for mistral tokenizer
1.7 KiB
1.7 KiB
Finetune Qwen3 with Axolotl
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
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Install Axolotl following the installation guide.
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Install Cut Cross Entropy to reduce training VRAM usage.
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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: qloraandload_in_4bit: truefrom 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.