--- title: "stanford_alpaca vs Chinese-LLaMA-Alpaca" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tatsu-lab-stanford-alpaca-vs-ymcui-chinese-llama-alpaca" tools: ["tatsu-lab-stanford-alpaca", "ymcui-chinese-llama-alpaca"] --- # stanford_alpaca vs Chinese-LLaMA-Alpaca *GraphCanon updated Sep 20, 2026* ## Verdict Pick stanford_alpaca if stanford Alpaca is a repository for training and sharing an instruction-following LLaMA model, including data for fine-tuning, data generation code, and fine-tuning code; pick Chinese-LLaMA-Alpaca if chinese-LLaMA-Alpaca offers Chinese versions of LLaMA and Alpaca models, with expanded Chinese vocabulary and pre-training on Chinese data, supporting local deployment on CPU/GPU. [stanford_alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) reports 30k GitHub stars, 4.0k forks, and 186 open issues, last pushed Jul 17, 2024. [Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca/wiki) has 19k stars, 1.8k forks, and 5 open issues, last pushed Apr 19, 2026. Figures are from public GitHub metadata via [stanford_alpaca's repository](https://github.com/tatsu-lab/stanford_alpaca) and [Chinese-LLaMA-Alpaca's repository](https://github.com/ymcui/Chinese-LLaMA-Alpaca). | | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | [Chinese-LLaMA-Alpaca](/tools/ymcui-chinese-llama-alpaca.md) | | --- | --- | --- | | Tagline | Code and documentation to train Stanford's Alpaca models, and generate the data | Chinese LLaMA & Alpaca LLMs | | Stars | 30,236 | 18,943 | | Forks | 3,972 | 1,836 | | Open issues | 186 | 5 | | Language | Python | Python | | Adopt for | Stanford Alpaca is a repository for training and sharing an instruction-following LLaMA model, including data for fine-tuning, data generation code, and fine-tuning code. | Chinese-LLaMA-Alpaca offers Chinese versions of LLaMA and Alpaca models, with expanded Chinese vocabulary and pre-training on Chinese data, supporting local deployment on CPU/GPU. | | Persona | - | - | | Runtime | - | - | | License | The repository is licensed under Apache-2.0, but the dataset and model weights are under CC BY NC 4.0, restricting use to non-commercial research purposes. | The tool is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | [Chinese-LLaMA-Alpaca](/tools/ymcui-chinese-llama-alpaca.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 792d | 152d | | Open issues (now) | 186 | 5 | | Stars delta | -8 (30d) | +10 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/tatsu-lab-stanford-alpaca/trust.md) | [trust report](/tools/ymcui-chinese-llama-alpaca/trust.md) | ## Decision facts: stanford_alpaca - **Requirements:** Min 8 GB RAM; The model training process may require significant computational resources, including GPUs. - **Adopt for:** Stanford Alpaca is a repository for training and sharing an instruction-following LLaMA model, including data for fine-tuning, data generation code, and fine-tuning code. - **License detail:** The repository is licensed under Apache-2.0, but the dataset and model weights are under CC BY NC 4.0, restricting use to non-commercial research purposes. ## Decision facts: Chinese-LLaMA-Alpaca - **Pricing:** freemium - The repository provides free access to the models and scripts, but users may incur costs related to hardware and cloud services for deployment. - **Requirements:** Min 8 GB RAM; Requires Python environment.; Supports deployment on local CPU/GPU. - **Adopt for:** Chinese-LLaMA-Alpaca offers Chinese versions of LLaMA and Alpaca models, with expanded Chinese vocabulary and pre-training on Chinese data, supporting local deployment on CPU/GPU. - **License detail:** The tool is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose stanford_alpaca if… - Requirements: Min 8 GB RAM; The model training process may require significant computational resources, including GPUs.. - Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model. - When you need to train an instruction-following model using the LLaMA architecture and have access to non-commercial use cases. ### Choose Chinese-LLaMA-Alpaca if… - Pricing: The repository provides free access to the models and scripts, but users may incur costs related to hardware and cloud services for deployment.. - Requirements: Min 8 GB RAM; Requires Python environment.; Supports deployment on local CPU/GPU.. - Tags unique to Chinese-LLaMA-Alpaca: alpaca, llama, llm, lora. - Also covers Inference & Serving. - When you need Chinese-specific large language models for tasks that require deep understanding of the Chinese language. ## When NOT to use stanford_alpaca - If your project requires commercial use, as the dataset and model weights are licensed under CC BY NC 4.0, allowing only non-commercial use. - When you need a fully safety-tested model, as Alpaca has not been fine-tuned to be safe and harmless. - If you require immediate access to model weights, as they are not currently available and depend on permission from the creators of LLaMA. ## When NOT to use Chinese-LLaMA-Alpaca - If your project requires models that are not specific to the Chinese language or do not benefit from Chinese-specific pre-training. - When you need models that are not available in the sizes provided by Chinese-LLaMA-Alpaca, such as models smaller than 7B or larger than 33B. - If you prefer models that are not based on LLaMA or Alpaca architectures. ## Common questions ### What is the difference between stanford_alpaca and Chinese-LLaMA-Alpaca? stanford_alpaca: Code and documentation to train Stanford's Alpaca models, and generate the data. Chinese-LLaMA-Alpaca: Chinese LLaMA & Alpaca LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose stanford_alpaca over Chinese-LLaMA-Alpaca? Choose stanford_alpaca over Chinese-LLaMA-Alpaca when Requirements: Min 8 GB RAM; The model training process may require significant computational resources, including GPUs.; Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model; When you need to train an instruction-following model using the LLaMA architecture and have access to non-commercial use cases. ### When should I choose Chinese-LLaMA-Alpaca over stanford_alpaca? Choose Chinese-LLaMA-Alpaca over stanford_alpaca when Pricing: The repository provides free access to the models and scripts, but users may incur costs related to hardware and cloud services for deployment.; Requirements: Min 8 GB RAM; Requires Python environment.; Supports deployment on local CPU/GPU.; Tags unique to Chinese-LLaMA-Alpaca: alpaca, llama, llm, lora; Also covers Inference & Serving; When you need Chinese-specific large language models for tasks that require deep understanding of the Chinese language. ### When should I avoid stanford_alpaca? If your project requires commercial use, as the dataset and model weights are licensed under CC BY NC 4.0, allowing only non-commercial use. When you need a fully safety-tested model, as Alpaca has not been fine-tuned to be safe and harmless. If you require immediate access to model weights, as they are not currently available and depend on permission from the creators of LLaMA. ### When should I avoid Chinese-LLaMA-Alpaca? If your project requires models that are not specific to the Chinese language or do not benefit from Chinese-specific pre-training. When you need models that are not available in the sizes provided by Chinese-LLaMA-Alpaca, such as models smaller than 7B or larger than 33B. If you prefer models that are not based on LLaMA or Alpaca architectures. ### Is stanford_alpaca or Chinese-LLaMA-Alpaca more popular on GitHub? stanford_alpaca has more GitHub stars (30,236 vs 18,943). Stars measure visibility, not whether either tool fits your constraints. ### Are stanford_alpaca and Chinese-LLaMA-Alpaca open source? Yes - both are open-source projects on GitHub (stanford_alpaca: Apache-2.0, Chinese-LLaMA-Alpaca: Apache-2.0). ### Where can I find alternatives to stanford_alpaca or Chinese-LLaMA-Alpaca? GraphCanon lists graph-backed alternatives at [stanford_alpaca alternatives](/tools/tatsu-lab-stanford-alpaca/alternatives) and [Chinese-LLaMA-Alpaca alternatives](/tools/ymcui-chinese-llama-alpaca/alternatives) ([stanford_alpaca markdown twin](/tools/tatsu-lab-stanford-alpaca/alternatives.md), [Chinese-LLaMA-Alpaca markdown twin](/tools/ymcui-chinese-llama-alpaca/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/tatsu-lab-stanford-alpaca-vs-ymcui-chinese-llama-alpaca.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, stanford_alpaca or Chinese-LLaMA-Alpaca? stanford_alpaca: Dormant. Chinese-LLaMA-Alpaca: 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 stanford_alpaca and Chinese-LLaMA-Alpaca? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [stanford_alpaca trust report](/tools/tatsu-lab-stanford-alpaca/trust); [Chinese-LLaMA-Alpaca trust report](/tools/ymcui-chinese-llama-alpaca/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tatsu-lab-stanford-alpaca`](/api/graphcanon/graph?tool=tatsu-lab-stanford-alpaca) - 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/_