---

name: chinese-llama-alpaca description: "Route Chinese-LLaMA-Alpaca model reconstruction, inference/deployment, training/fine-tuning, and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Chinese-LLaMA-Alpaca Repo Skill

Use this repo skill when a task involves the original Chinese-LLaMA-Alpaca project: Chinese LLaMA/Alpaca LoRA adapters, tokenizer expansion, LoRA reconstruction, local Transformers inference, Gradio or OpenAI-compatible serving, LangChain demos, PEFT pretraining/SFT, C-Eval, or the repo's example benchmark tables. The command examples below assume the current working directory is this generated skill root.

This skill is self-contained for workflow guidance and bundled helper scripts, but it does not provide original LLaMA weights, Chinese LoRA downloads, C-Eval data, API keys, or permission to use restricted assets commercially. Always confirm asset/license, hardware, network, service, and budget constraints before running heavyweight commands.

Start Here

1. Read references/repo-provenance.md when checking freshness against a repository checkout.
2. Read references/model-family-overview.md to choose Chinese LLaMA versus Chinese Alpaca, size, Plus/Pro, and tokenizer family.
3. Read references/environment-and-installation.md before creating an environment or installing optional dependencies.
4. Run scripts/check_environment.py for a safe dependency/backend probe.
5. Use scripts/verify_sha256.py with the checksum reference when checking downloaded model/tokenizer assets.
6. Use references/troubleshooting.md for cross-cutting install/import/asset/backend failures.
7. Use references/compliance-and-limitations.md before publishing outputs or advising on commercial/safety-sensitive use.

Route Map

User request	Use
"Merge Chinese Alpaca LoRA with LLaMA", "convert to HF/PTH", "tokenizer mismatch", "verify adapter checksum"	sub-skills/model-reconstruction/
"Run inference", "batch predictions", "interactive single-turn", "Gradio demo", "OpenAI API server", "LangChain QA"	sub-skills/inference-deployment/
"Prepare SFT data", "validate instruction JSON", "pretrain Chinese LLaMA", "fine-tune Alpaca LoRA", "DeepSpeed/PEFT args"	sub-skills/training-finetuning/
"Run C-Eval", "validate C-Eval data", "interpret examples scores", "compare q4/q8/Plus/Pro"	sub-skills/evaluation-benchmarks/

Minimal Public Environment Check

The original requirements.txt pins:

torch==1.13.1
transformers==4.30.0
sentencepiece==0.1.97
PEFT from the repository's pinned Hugging Face commit

Additional workflows need optional packages such as datasets, pandas, scikit-learn, fastapi, uvicorn, shortuuid, gradio, langchain, FAISS, or DeepSpeed. Install only the optional group required by the selected workflow.

Safe check from the generated skill root:

python scripts/check_environment.py --include-optional

Use python scripts/verify_sha256.py /path/to/file --expected name=hex from the same skill root when validating downloaded model, tokenizer, or data files. These helpers only import packages, check CUDA visibility, or hash files; they do not download models, launch servers, run training, or read credentials.

Common Boundaries

• If a user only has LoRA files and wants generation, reconstruct or load the LoRA with a compatible base model first.
• If a user wants chat/instruction following, prefer Chinese Alpaca and use the Alpaca prompt template. Chinese LLaMA is base/continuation-oriented.
• If a user wants training, validate data before allocating GPUs. Real training is long-running.
• If a user wants benchmark claims, record model path, dataset, decoding flags, hardware, and skipped/failed cases; do not infer global quality from example tables.