--- title: "stanford_alpaca vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tatsu-lab-stanford-alpaca-vs-tloen-alpaca-lora" tools: ["tatsu-lab-stanford-alpaca", "tloen-alpaca-lora"] --- # stanford_alpaca vs alpaca-lora *GraphCanon updated Aug 3, 2026* ## Verdict Pick stanford_alpaca if resources for fine-tuning an instruction-following LLaMA model by Stanford University; 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. [stanford_alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) reports 30k GitHub stars, 4.0k forks, and 187 open issues, last pushed Jul 17, 2024. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 365 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [stanford_alpaca's repository](https://github.com/tatsu-lab/stanford_alpaca) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Code and documentation to train Stanford's Alpaca models | Instruct-tune LLaMA on consumer hardware | | Stars | 30,244 | 18,912 | | Forks | 3,992 | 2,180 | | Open issues | 187 | 365 | | Language | Python | Jupyter Notebook | | Adopt for | Resources for fine-tuning an instruction-following LLaMA model by Stanford University. | alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. | | Persona | - | developer harness | | Runtime | - | - | | License | Apache-2.0 | 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. | | Categories | 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) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 745d | 734d | | Open issues (now) | 187 | 365 | | Owner type | Organization | User | | Full report | [trust report](/tools/tatsu-lab-stanford-alpaca/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Decision facts: stanford_alpaca - **Adopt for:** Resources for fine-tuning an instruction-following LLaMA model by Stanford University. ## 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 ## Choose when ### Choose stanford_alpaca if… - stanford_alpaca is primarily Python; alpaca-lora is Jupyter Notebook. - Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model. - When you are conducting academic research on language models and need to experiment with an instruction-following model like Alpaca. ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; stanford_alpaca 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, LLM Frameworks. - 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 NOT to use stanford_alpaca - For commercial applications, as the license restricts usage to research purposes only and prohibits use for non-academic projects. - If you need a model that has been fine-tuned specifically for safety and ethical considerations, since the current version of Alpaca is still in development without these specific refinements. ## 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. ## Common questions ### What is the difference between stanford_alpaca and alpaca-lora? stanford_alpaca: Code and documentation to train Stanford's Alpaca models. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose stanford_alpaca over alpaca-lora? Choose stanford_alpaca over alpaca-lora when stanford_alpaca is primarily Python; alpaca-lora is Jupyter Notebook; Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model; When you are conducting academic research on language models and need to experiment with an instruction-following model like Alpaca. ### When should I choose alpaca-lora over stanford_alpaca? Choose alpaca-lora over stanford_alpaca when alpaca-lora is primarily Jupyter Notebook; stanford_alpaca 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, LLM Frameworks; 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 avoid stanford_alpaca? For commercial applications, as the license restricts usage to research purposes only and prohibits use for non-academic projects. If you need a model that has been fine-tuned specifically for safety and ethical considerations, since the current version of Alpaca is still in development without these specific refinements. ### 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. ### Is stanford_alpaca or alpaca-lora more popular on GitHub? stanford_alpaca has more GitHub stars (30,244 vs 18,912). Stars measure visibility, not whether either tool fits your constraints. ### Are stanford_alpaca and alpaca-lora open source? Yes - both are open-source projects on GitHub (stanford_alpaca: Apache-2.0, alpaca-lora: Apache-2.0). ### Where can I find alternatives to stanford_alpaca or alpaca-lora? GraphCanon lists graph-backed alternatives at [stanford_alpaca alternatives](/tools/tatsu-lab-stanford-alpaca/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([stanford_alpaca markdown twin](/tools/tatsu-lab-stanford-alpaca/alternatives.md), [alpaca-lora markdown twin](/tools/tloen-alpaca-lora/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-tloen-alpaca-lora.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, stanford_alpaca or alpaca-lora? stanford_alpaca: Dormant. alpaca-lora: Dormant. 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 alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [stanford_alpaca trust report](/tools/tatsu-lab-stanford-alpaca/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/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/_