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chinese-llama-alpaca

Route Chinese-LLaMA-Alpaca model reconstruction, inference/deployment, training/fine-tuning, and evaluation workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill
연동 도구OpenAI

실행 점수 미검증

설치 방법 보기

포함된 파일(54)

  • SKILL.md4.2 KB
  • references/compliance-and-limitations.md1.5 KB
  • references/environment-and-installation.md2.1 KB
  • references/model-family-overview.md2.0 KB
  • references/repo-provenance.md1.7 KB
  • references/repo-routing-metadata.json564 B
  • references/troubleshooting.md2.6 KB
  • scripts/check_environment.py3.7 KB
  • scripts/verify_sha256.py2.1 KB
  • sub-skills/evaluation-benchmarks/references/ceval-workflow.md2.8 KB
  • sub-skills/evaluation-benchmarks/references/example-benchmarks.md3.0 KB
  • sub-skills/evaluation-benchmarks/references/troubleshooting.md2.8 KB
  • sub-skills/evaluation-benchmarks/scripts/ceval/eval.py5.9 KB
  • sub-skills/evaluation-benchmarks/scripts/ceval/evaluator.py1.5 KB
  • sub-skills/evaluation-benchmarks/scripts/ceval/llama_evaluator.py8.1 KB
  • sub-skills/evaluation-benchmarks/scripts/ceval/subject_mapping.json5.2 KB
  • sub-skills/evaluation-benchmarks/scripts/validate_ceval_layout.py3.9 KB
  • sub-skills/evaluation-benchmarks/SKILL.md2.6 KB
  • sub-skills/inference-deployment/references/gradio-and-api-server.md3.5 KB
  • sub-skills/inference-deployment/references/hf-inference.md3.7 KB
  • sub-skills/inference-deployment/references/langchain-integrations.md2.7 KB
  • sub-skills/inference-deployment/references/troubleshooting.md4.0 KB
  • sub-skills/inference-deployment/scripts/doc.txt6.0 KB
  • sub-skills/inference-deployment/scripts/gradio_demo.py11.1 KB
  • sub-skills/inference-deployment/scripts/inference_hf.py7.1 KB
  • sub-skills/inference-deployment/scripts/langchain_qa.py4.2 KB
  • sub-skills/inference-deployment/scripts/langchain_sum.py2.7 KB
  • sub-skills/inference-deployment/scripts/openai_api_protocol.py2.2 KB
  • sub-skills/inference-deployment/scripts/openai_api_server.py7.8 KB
  • sub-skills/inference-deployment/scripts/patches.py8.4 KB
  • sub-skills/inference-deployment/SKILL.md3.1 KB
  • sub-skills/model-reconstruction/references/checksums.md3.6 KB
  • sub-skills/model-reconstruction/references/model-assets.md3.8 KB
  • sub-skills/model-reconstruction/references/reconstruction-workflows.md3.7 KB
  • sub-skills/model-reconstruction/references/troubleshooting.md3.9 KB
  • sub-skills/model-reconstruction/scripts/chinese_sp.model507.8 KB
  • sub-skills/model-reconstruction/scripts/merge_llama_with_chinese_lora_low_mem.py14.5 KB
  • sub-skills/model-reconstruction/scripts/merge_llama_with_chinese_lora.py14.4 KB
  • sub-skills/model-reconstruction/scripts/merge_tokenizers.py2.8 KB
  • sub-skills/model-reconstruction/SKILL.md3.1 KB
  • sub-skills/training-finetuning/references/api-reference.md7.7 KB
  • sub-skills/training-finetuning/references/data-formats.md5.8 KB
  • sub-skills/training-finetuning/references/training-workflows.md8.6 KB
  • sub-skills/training-finetuning/references/troubleshooting.md6.8 KB
  • sub-skills/training-finetuning/scripts/build_dataset.py3.9 KB
  • sub-skills/training-finetuning/scripts/run_clm_pt_with_peft.py27.6 KB
  • sub-skills/training-finetuning/scripts/run_clm_sft_with_peft.py17.5 KB
  • sub-skills/training-finetuning/scripts/validate_training_data.py6.1 KB
  • sub-skills/training-finetuning/SKILL.md3.6 KB
  • sub-skills/training-finetuning/templates/ds_zero2_no_offload.json686 B
  • sub-skills/training-finetuning/templates/instruction_sample.json372 B
  • sub-skills/training-finetuning/templates/pretrain_sample.txt221 B
  • sub-skills/training-finetuning/templates/run_pt.sh3.3 KB
  • sub-skills/training-finetuning/templates/run_sft.sh3.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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 requestUse
"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.

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