---

name: checkpoint-export description: "Guides Huatuo-Llama-Med-Chinese LoRA adapter checkpoint export into Hugging Face and original LLaMA layouts." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

checkpoint-export

Use this sub-skill when the task is to merge a Huatuo-Llama-Med-Chinese LoRA adapter with its base model and export the result as either a Hugging Face checkpoint or an original LLaMA-style state-dict checkpoint.

Read first

• references/workflows.md for supported export modes, required inputs, adapter assumptions, output layouts, and the LLaMA-7B state-dict mapping.
• references/troubleshooting.md for BASE_MODEL assertion failures, hard-coded adapter defaults, missing local files, architecture mismatches, memory/storage pressure, and PEFT/Transformers compatibility issues.
• scripts/build_export_command.py to build a dry-run, self-contained export command template without importing Torch or Transformers in the helper itself.

Route here

Route here for:

• LoRA adapter merge/export planning.
• Building a safe dry-run command for Hugging Face hf_ckpt/ output.
• Building a safe dry-run command for original LLaMA ckpt/ output.
• Explaining why state-dict export is limited to LLaMA-7B-compatible models.

Route elsewhere

• Training or re-training LoRA adapters belongs to finetuning.
• Running inference from an unmerged adapter belongs to inference.
• Prompt templates, instruction data, and adapter training data formats belong to prompt-data-formats.

Minimal operating checklist

1. Confirm the base model is a local path or Hugging Face model id compatible with the adapter.
2. Confirm the adapter path or Hugging Face adapter id points to a PEFT LoRA adapter directory with adapter_config.json and adapter_model.bin.
3. Use Hugging Face export unless the downstream consumer explicitly requires the original LLaMA consolidated.00.pth plus params.json layout.
4. Use state-dict export only for LLaMA-7B-compatible checkpoints; do not apply it to Bloom, Huozi, ChatGLM, or other architectures.
5. Build the dry-run command with the bundled helper, review the emitted warnings, then execute only in an environment with compatible torch, transformers, peft, and enough RAM/storage.