--- title: "alpaca-lora vs Chinese-LLaMA-Alpaca-2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tloen-alpaca-lora-vs-ymcui-chinese-llama-alpaca-2" tools: ["tloen-alpaca-lora", "ymcui-chinese-llama-alpaca-2"] --- # alpaca-lora vs Chinese-LLaMA-Alpaca-2 *GraphCanon updated Aug 17, 2026* ## Verdict Pick alpaca-lora if alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration; pick Chinese-LLaMA-Alpaca-2 if chinese-LLaMA-Alpaca-2 provides Chinese-specific versions of LLaMA-2 and Alpaca-2 with extended context lengths up to 64K tokens. [alpaca-lora](https://github.com/tloen/alpaca-lora) reports 19k GitHub stars, 2.2k forks, and 365 open issues, last pushed Jul 29, 2024. [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) has 7.1k stars, 562 forks, and 6 open issues, last pushed Apr 19, 2026. Figures are from public GitHub metadata via [alpaca-lora's repository](https://github.com/tloen/alpaca-lora) and [Chinese-LLaMA-Alpaca-2's repository](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2). | | [alpaca-lora](/tools/tloen-alpaca-lora.md) | [Chinese-LLaMA-Alpaca-2](/tools/ymcui-chinese-llama-alpaca-2.md) | | --- | --- | --- | | Tagline | Instruct-tune LLaMA on consumer hardware | Chinese LLaMA-2 & Alpaca-2 models with extended context lengths | | Stars | 18,912 | 7,124 | | Forks | 2,180 | 562 | | Open issues | 365 | 6 | | Language | Jupyter Notebook | Python | | Adopt for | alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. | Chinese-LLaMA-Alpaca-2 provides Chinese-specific versions of LLaMA-2 and Alpaca-2 with extended context lengths up to 64K tokens. | | Persona | developer harness | - | | Runtime | - | - | | License | The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [alpaca-lora](/tools/tloen-alpaca-lora.md) | [Chinese-LLaMA-Alpaca-2](/tools/ymcui-chinese-llama-alpaca-2.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 734d | 120d | | Open issues (now) | 365 | 6 | | Stars delta | Unknown | -8 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/tloen-alpaca-lora/trust.md) | [trust report](/tools/ymcui-chinese-llama-alpaca-2/trust.md) | ## Decision facts: alpaca-lora - **Pricing:** freemium - The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply. - **Adopt for:** alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. - **License detail:** The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. - **Persona:** developer harness ## Decision facts: Chinese-LLaMA-Alpaca-2 - **Adopt for:** Chinese-LLaMA-Alpaca-2 provides Chinese-specific versions of LLaMA-2 and Alpaca-2 with extended context lengths up to 64K tokens. ## Choose when ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca-2 is Python. - Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.. - Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, llama. - Also covers Inference & Serving, Model Training. - alpaca-lora ships Docker support for self-hosted deployment. - When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ### Choose Chinese-LLaMA-Alpaca-2 if… - Chinese-LLaMA-Alpaca-2 is primarily Python; alpaca-lora is Jupyter Notebook. - Tags unique to Chinese-LLaMA-Alpaca-2: chinese, flash-attention, large language model (llm), long context models. - When you need advanced models specifically tailored for the Chinese language, offering better performance compared to generic models ## When NOT to use alpaca-lora - When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. - For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ## When NOT to use Chinese-LLaMA-Alpaca-2 - If your project requires support for languages other than Chinese, as the focus is on refining Chinese text understanding and generation - In scenarios where shorter context lengths (less than 16K tokens) are sufficient, as the extended models may be overkill in terms of resource usage ## Common questions ### What is the difference between alpaca-lora and Chinese-LLaMA-Alpaca-2? alpaca-lora: Instruct-tune LLaMA on consumer hardware. Chinese-LLaMA-Alpaca-2: Chinese LLaMA-2 & Alpaca-2 models with extended context lengths. See the comparison table for live GitHub stats and shared categories. ### When should I choose alpaca-lora over Chinese-LLaMA-Alpaca-2? Choose alpaca-lora over Chinese-LLaMA-Alpaca-2 when alpaca-lora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca-2 is Python; Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.; Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, llama; Also covers Inference & Serving, Model Training; alpaca-lora ships Docker support for self-hosted deployment; When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ### When should I choose Chinese-LLaMA-Alpaca-2 over alpaca-lora? Choose Chinese-LLaMA-Alpaca-2 over alpaca-lora when Chinese-LLaMA-Alpaca-2 is primarily Python; alpaca-lora is Jupyter Notebook; Tags unique to Chinese-LLaMA-Alpaca-2: chinese, flash-attention, large language model (llm), long context models; When you need advanced models specifically tailored for the Chinese language, offering better performance compared to generic models. ### When should I avoid alpaca-lora? When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ### When should I avoid Chinese-LLaMA-Alpaca-2? If your project requires support for languages other than Chinese, as the focus is on refining Chinese text understanding and generation In scenarios where shorter context lengths (less than 16K tokens) are sufficient, as the extended models may be overkill in terms of resource usage ### Is alpaca-lora or Chinese-LLaMA-Alpaca-2 more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 7,124). Stars measure visibility, not whether either tool fits your constraints. ### Are alpaca-lora and Chinese-LLaMA-Alpaca-2 open source? Yes - both are open-source projects on GitHub (alpaca-lora: Apache-2.0, Chinese-LLaMA-Alpaca-2: Apache-2.0). ### Where can I find alternatives to alpaca-lora or Chinese-LLaMA-Alpaca-2? GraphCanon lists graph-backed alternatives at [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) and [Chinese-LLaMA-Alpaca-2 alternatives](/tools/ymcui-chinese-llama-alpaca-2/alternatives) ([alpaca-lora markdown twin](/tools/tloen-alpaca-lora/alternatives.md), [Chinese-LLaMA-Alpaca-2 markdown twin](/tools/ymcui-chinese-llama-alpaca-2/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/tloen-alpaca-lora-vs-ymcui-chinese-llama-alpaca-2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, alpaca-lora or Chinese-LLaMA-Alpaca-2? alpaca-lora: Dormant. Chinese-LLaMA-Alpaca-2: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for alpaca-lora and Chinese-LLaMA-Alpaca-2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust); [Chinese-LLaMA-Alpaca-2 trust report](/tools/ymcui-chinese-llama-alpaca-2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tloen-alpaca-lora`](/api/graphcanon/graph?tool=tloen-alpaca-lora) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_