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name: finetuning description: "Guides LoRA instruction fine-tuning for Huatuo-Llama-Med-Chinese style medical QA data using PEFT and Transformers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Finetuning

Use this sub-skill when the task is to plan, validate, or construct a safe dry-run command for LoRA instruction fine-tuning on Chinese medical QA data with the repository-compatible PEFT/Transformers training interface.

Route here for

• LoRA fine-tuning parameter selection: batch size, micro batch size, epochs, learning rate, cutoff length, validation size, and LoRA rank/alpha/dropout/target modules.
• Medical QA training data checks for the required JSONL fields instruction, input, and output.
• W&B, DDP, checkpoint resume, and adapter save behavior for the training workflow.
• Safe command construction with the bundled dry-run builder: scripts/build_finetune_command.py.

Route elsewhere

• Prompt template JSON semantics, Prompter behavior, or data conversion details: use prompt-data-formats.
• Merging or exporting LoRA adapters after training: use checkpoint-export.
• Running inference from a trained adapter: use inference.

Required reading in this sub-skill

• Workflow and resource guidance: references/workflows.md.
• Training interface and defaults: references/api-reference.md.
• Failure diagnosis: references/troubleshooting.md.
• Dry-run command builder: scripts/build_finetune_command.py.

Minimal safe workflow

1. Validate that every training record is JSONL with string fields instruction, input, and output; input may be an empty string but should still be present for this training implementation.
2. Choose a prompt template name. For the medical-knowledge LLaMA/Alpaca workflow, use med_template unless a different compatible template is intentional.
3. Build a dry-run command with the bundled builder. Do not start training until model assets, CUDA/bitsandbytes compatibility, output storage, and W&B/DDP behavior are reviewed.
4. For limited memory GPUs, lower micro_batch_size, lower total batch_size, and consider lowering cutoff_len before attempting training.
5. When resuming, point resume_from_checkpoint at either a Trainer checkpoint directory or a LoRA adapter directory whose LoRA configuration matches the new run.

Safety notes

Full fine-tuning is expensive and depends on external base-model weights, GPU memory, a compatible Torch/CUDA/bitsandbytes stack, and medical-data quality. The bundled script only builds and optionally validates a command; it does not import model libraries, download weights, or launch training.