--- title: "LLM-Adapters vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-tloen-alpaca-lora" tools: ["agi-edgerunners-llm-adapters", "tloen-alpaca-lora"] --- # LLM-Adapters vs alpaca-lora *GraphCanon updated Aug 24, 2026* ## Verdict Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; 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. [LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 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 [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | Instruct-tune LLaMA on consumer hardware | | Stars | 1,233 | 18,912 | | Forks | 115 | 2,180 | | Open issues | 55 | 365 | | Language | Python | Jupyter Notebook | | Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | 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 | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 896d | 734d | | Open issues (now) | 55 | 365 | | Stars delta | -1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Decision facts: LLM-Adapters - **Adopt for:** LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. ## 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 LLM-Adapters if… - LLM-Adapters is primarily Python; alpaca-lora is Jupyter Notebook. - Tags unique to LLM-Adapters: adapters, fine-tuning, large language models, parameter-efficient. - Optimizing resource usage when you need to fine-tune large language models without altering their core parameters ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; LLM-Adapters 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 LLM-Adapters - You require a full retraining approach that modifies all model weights, not just adapters - Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ## 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 LLM-Adapters and alpaca-lora? LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM-Adapters over alpaca-lora? Choose LLM-Adapters over alpaca-lora when LLM-Adapters is primarily Python; alpaca-lora is Jupyter Notebook; Tags unique to LLM-Adapters: adapters, fine-tuning, large language models, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters. ### When should I choose alpaca-lora over LLM-Adapters? Choose alpaca-lora over LLM-Adapters when alpaca-lora is primarily Jupyter Notebook; LLM-Adapters 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 LLM-Adapters? You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ### 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 LLM-Adapters or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 1,233). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM-Adapters and alpaca-lora open source? Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, alpaca-lora: Apache-2.0). ### Where can I find alternatives to LLM-Adapters or alpaca-lora? GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/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/agi-edgerunners-llm-adapters-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, LLM-Adapters or alpaca-lora? LLM-Adapters: 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 LLM-Adapters and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters) - 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/_