Qwen2.5 LoRA fine-tuning walkthrough (Chinese)
by Datawhale self-llm contributors
Follow a concrete Chinese LoRA recipe from instruction records to a trained adapter and inference.
This Chinese walkthrough explains how instruction records become token inputs and masked training labels, then supplies a PEFT LoRA configuration and Transformers training and inference steps. It assumes a working Linux model environment and enough GPU memory for the selected model. Use it as a guided recipe, not evidence that the dependencies or example training have been rerun here.
- Level
- Intermediate
- Models
- qwen
- Category
- Model Training
- Tags
- practical-guide
- Source date
- date not stated
- Added
- 2026-10-18
- Last verified
- 2026-10-18
- Status
- ok
- Prerequisites
- Requires Python, an already configured Linux/GPU environment and local model deployment. The lesson supplies the preprocessing and training recipe; it does not require designing a training algorithm or distributed system.
- Subcategory
- fine tuning
- Region
- CN
- Original language
- zh
- Access
- free
- Resource reuse
- open: Repository Apache-2.0 applies to its tutorial code. Model weights and example datasets require their own terms review. Facet definitions
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