--- title: "WizardLM vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nlpxucan-wizardlm-vs-tloen-alpaca-lora" tools: ["nlpxucan-wizardlm", "tloen-alpaca-lora"] --- # WizardLM vs alpaca-lora *GraphCanon updated Aug 5, 2026* ## Verdict Pick WizardLM if wizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling; 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. [WizardLM](https://github.com/nlpxucan/WizardLM) reports 9.5k GitHub stars, 749 forks, and 169 open issues, last pushed Jun 7, 2025. [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 [WizardLM's repository](https://github.com/nlpxucan/WizardLM) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [WizardLM](/tools/nlpxucan-wizardlm.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Empowering Large Pre-Trained Language Models to Follow Complex Instructions | Instruct-tune LLaMA on consumer hardware | | Stars | 9,484 | 18,912 | | Forks | 749 | 2,180 | | Open issues | 169 | 365 | | Language | Python | Jupyter Notebook | | Adopt for | WizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling. | 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 | (unknown) | 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 | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [WizardLM](/tools/nlpxucan-wizardlm.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 424d | 734d | | Open issues (now) | 169 | 365 | | Full report | [trust report](/tools/nlpxucan-wizardlm/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Decision facts: WizardLM - **Adopt for:** WizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling. - **License detail:** (unknown) ## 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 WizardLM if… - WizardLM is primarily Python; alpaca-lora is Jupyter Notebook. - Tags unique to WizardLM: instruction-following, large language models, wizardcoder, wizardmath. - When advanced coding tasks need precise solutions, surpassing GPT-3.5-Turbo and Gemini Pro ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; WizardLM 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. - 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 WizardLM - If real-time updates are needed beyond Nov 2023, as performance is based on past benchmarks - When looking for broad language capabilities of GPT-4, which outperformance in some benchmarks ## 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 WizardLM and alpaca-lora? WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose WizardLM over alpaca-lora? Choose WizardLM over alpaca-lora when WizardLM is primarily Python; alpaca-lora is Jupyter Notebook; Tags unique to WizardLM: instruction-following, large language models, wizardcoder, wizardmath; When advanced coding tasks need precise solutions, surpassing GPT-3.5-Turbo and Gemini Pro. ### When should I choose alpaca-lora over WizardLM? Choose alpaca-lora over WizardLM when alpaca-lora is primarily Jupyter Notebook; WizardLM 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; 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 WizardLM? If real-time updates are needed beyond Nov 2023, as performance is based on past benchmarks When looking for broad language capabilities of GPT-4, which outperformance in some benchmarks ### 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 WizardLM or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 9,484). Stars measure visibility, not whether either tool fits your constraints. ### Are WizardLM and alpaca-lora open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to WizardLM or alpaca-lora? GraphCanon lists graph-backed alternatives at [WizardLM alternatives](/tools/nlpxucan-wizardlm/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([WizardLM markdown twin](/tools/nlpxucan-wizardlm/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/nlpxucan-wizardlm-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, WizardLM or alpaca-lora? WizardLM: 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 WizardLM and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [WizardLM trust report](/tools/nlpxucan-wizardlm/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nlpxucan-wizardlm`](/api/graphcanon/graph?tool=nlpxucan-wizardlm) - 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/_