LLaMA-Factory
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- A practical guide to SFT and LoRA with Qwen3-0.6B-Base
The Juejin AI tutorial explains how supervised fine-tuning adapts a base model to follow instructions, covering loss masking, Alpaca and ShareGPT data formats, and approaches such as Self-Instruct and model distillation. It demonstrates LoRA-based fine-tuning of Qwen3-0.6B-Base with TRL and PEFT, including code, configuration choices, and guidance on matching training and inference chat templates. The author reports that a 1,000-sample run on a 16 GB M2 Mac took about 44 minutes and changed the model from repeating questions into answering them completely.
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