Chinese-LLaMA-Alpaca
Chinese-LLaMA-Alpaca is an open-source project providing Chinese-optimized language models based on Meta's LLaMA, with extended vocabulary and instruction-tuned variants. The project supplies LoRA adapter weights (not standalone models) plus training and deployment scripts for local CPU/GPU inference and fine-tuning.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | ymcui/Chinese-LLaMA-Alpaca |
| Owner | ymcui |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 18.9k |
| Forks | 1.9k |
| Open issues | 5 |
| Latest release | v5.0 (2023-07-19) |
| Last updated | 2026-04-19 |
| Source | https://github.com/ymcui/Chinese-LLaMA-Alpaca |
What Chinese-LLaMA-Alpaca is
Builds on LLaMA-7B/13B/33B by expanding the tokenizer for Chinese and applying continued pretraining with Chinese text; Alpaca variants add instruction-tuning with supervised data. Distributed as LoRA adapters merged with original LLaMA weights; supports quantization via llama.cpp, transformers, and text-generation-webui. Apache-2.0 licensed core code.
Get the Chinese-LLaMA-Alpaca source
Clone the repository and explore it locally.
git clone https://github.com/ymcui/Chinese-LLaMA-Alpaca.gitcd Chinese-LLaMA-Alpaca# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Must obtain original LLaMA weights from Meta and merge with LoRA adapters using provided scripts before inference; adds setup friction.
- Quantization and deployment scripts are provided (llama.cpp, transformers integration) but require familiarity with Python/shell and model conversion tooling.
- Instruction-tuned Alpaca models expect specific prompt templates; deviation causes performance degradation. Templates are embedded in inference tools.
- GPU memory requirements vary by model size (7B ~6GB, 13B ~12GB, 33B ~24GB for full precision); quantization reduces footprint significantly.
- Context length support documented as 4K+ in transformers and 8K in llama.cpp, but not benchmarked against baseline; longer sequences may degrade quality.
When to avoid it — and what to weigh
- Need standalone, immediately runnable models — LoRA adapters require merging with original LLaMA weights; cannot be used independently without extra reconstruction steps.
- Production systems requiring commercial LLaMA license clarity — Facebook's original LLaMA weights carry usage restrictions. This project distributes LoRA only, but full merged models inherit base LLaMA licensing constraints.
- Multi-language or English-primary workloads — Models optimized specifically for Chinese; English performance not characterized relative to standard LLaMA or other open models.
- Enterprise support and SLA requirements — Community-driven open-source; no commercial support, SLAs, or security incident response guarantees.
License & commercial use
Apache-2.0 for project code (training, inference, conversion scripts). However, merged models inherit Meta's original LLaMA license terms, which officially prohibit commercial use. LoRA adapters alone are Apache-2.0, but full model weights are constrained by base LLaMA license.
Requires careful review. The distributed LoRA adapters carry Apache-2.0 licensing, but merging them with original LLaMA weights produces an artifact bound by Meta's LLaMA License, which explicitly restricts commercial use. Any production deployment must verify compliance with Meta's terms and obtain explicit permission or use an alternative licensed base model.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Possible |
| Assessment confidence | High |
No explicit security audit information provided. As a community-driven research project, undertake due diligence on model outputs (may reflect training data biases and hallucinations). Quantized models reduce attack surface vs. full-precision, but no formal threat model or adversarial robustness claims are made. Ensure base LLaMA weights are obtained from official sources to avoid tampered artifacts.
Alternatives to consider
Llama-2 (Meta)
Official, openly licensed base model (Llama 2 Community License); community Chinese adaptations exist (e.g., Chinese-LLaMA-Alpaca-2) that may offer better support and clearer commercial terms.
Baichuan, Qwen, or other open Chinese LLMs
Purpose-built Chinese models with permissive licenses (Apache-2.0, MIT); avoid external weight merging and licensing ambiguity; established community and commercial backing.
No setup burden, clear licensing, production SLAs, and validated Chinese support; trade off cost and data privacy for simplicity and reliability.
Build on Chinese-LLaMA-Alpaca with DEV.co software developers
Chinese-LLaMA-Alpaca provides scripts and guidance for quantization and inference, but requires external base model weights and careful license review. Contact Devco to evaluate this for your use case, assess commercial viability, or explore licensed alternatives.
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Chinese-LLaMA-Alpaca FAQ
Can I use this for commercial applications?
Do I need the original LLaMA weights?
What hardware do I need?
How does this compare to the original LLaMA?
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Ready to Deploy Chinese LLMs Locally?
Chinese-LLaMA-Alpaca provides scripts and guidance for quantization and inference, but requires external base model weights and careful license review. Contact Devco to evaluate this for your use case, assess commercial viability, or explore licensed alternatives.