--- title: "SAM-Adapter-PyTorch vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tianrun-chen-sam-adapter-pytorch-vs-tloen-alpaca-lora" tools: ["tianrun-chen-sam-adapter-pytorch", "tloen-alpaca-lora"] --- # SAM-Adapter-PyTorch vs alpaca-lora *GraphCanon updated Aug 24, 2026* ## Verdict Pick SAM-Adapter-PyTorch if sAM-Adapter-PyTorch facilitates downstream task adaptation for SAM through adapters and prompts, specialized in camouflaged object detection with PyTorch; 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. [SAM-Adapter-PyTorch](https://github.com/tianrun-chen/SAM-Adapter-PyTorch) reports 1.6k GitHub stars, 123 forks, and 66 open issues, last pushed May 17, 2026. [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 [SAM-Adapter-PyTorch's repository](https://github.com/tianrun-chen/SAM-Adapter-PyTorch) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [SAM-Adapter-PyTorch](/tools/tianrun-chen-sam-adapter-pytorch.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts | Instruct-tune LLaMA on consumer hardware | | Stars | 1,550 | 18,912 | | Forks | 123 | 2,180 | | Open issues | 66 | 365 | | Language | Python | Jupyter Notebook | | Adopt for | SAM-Adapter-PyTorch facilitates downstream task adaptation for SAM through adapters and prompts, specialized in camouflaged object detection with PyTorch. | 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 | MIT | 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 | Computer Vision, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [SAM-Adapter-PyTorch](/tools/tianrun-chen-sam-adapter-pytorch.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 98d | 734d | | Open issues (now) | 66 | 365 | | Stars delta | +6 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/tianrun-chen-sam-adapter-pytorch/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Decision facts: SAM-Adapter-PyTorch - **Adopt for:** SAM-Adapter-PyTorch facilitates downstream task adaptation for SAM through adapters and prompts, specialized in camouflaged object detection with PyTorch. ## 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 SAM-Adapter-PyTorch if… - SAM-Adapter-PyTorch is primarily Python; alpaca-lora is Jupyter Notebook. - License: SAM-Adapter-PyTorch is MIT, alpaca-lora is Apache-2.0. - Tags unique to SAM-Adapter-PyTorch: 2d-segmentation, adapter, camouflage-images, camouflaged-object-detection. - Also covers Computer Vision. - Need to adapt SAM to specific tasks like detecting camouflaged objects ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; SAM-Adapter-PyTorch is Python. - License: alpaca-lora is Apache-2.0, SAM-Adapter-PyTorch is MIT. - 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 SAM-Adapter-PyTorch - Looking for a toolset that primarily focuses on training from scratch rather than adapting pre-trained models - Interested in frameworks other than PyTorch ## 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 SAM-Adapter-PyTorch and alpaca-lora? SAM-Adapter-PyTorch: Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose SAM-Adapter-PyTorch over alpaca-lora? Choose SAM-Adapter-PyTorch over alpaca-lora when SAM-Adapter-PyTorch is primarily Python; alpaca-lora is Jupyter Notebook; License: SAM-Adapter-PyTorch is MIT, alpaca-lora is Apache-2.0; Tags unique to SAM-Adapter-PyTorch: 2d-segmentation, adapter, camouflage-images, camouflaged-object-detection; Also covers Computer Vision; Need to adapt SAM to specific tasks like detecting camouflaged objects. ### When should I choose alpaca-lora over SAM-Adapter-PyTorch? Choose alpaca-lora over SAM-Adapter-PyTorch when alpaca-lora is primarily Jupyter Notebook; SAM-Adapter-PyTorch is Python; License: alpaca-lora is Apache-2.0, SAM-Adapter-PyTorch is MIT; 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 SAM-Adapter-PyTorch? Looking for a toolset that primarily focuses on training from scratch rather than adapting pre-trained models Interested in frameworks other than PyTorch ### 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 SAM-Adapter-PyTorch or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 1,550). Stars measure visibility, not whether either tool fits your constraints. ### Are SAM-Adapter-PyTorch and alpaca-lora open source? Yes - both are open-source projects on GitHub (SAM-Adapter-PyTorch: MIT, alpaca-lora: Apache-2.0). ### Where can I find alternatives to SAM-Adapter-PyTorch or alpaca-lora? GraphCanon lists graph-backed alternatives at [SAM-Adapter-PyTorch alternatives](/tools/tianrun-chen-sam-adapter-pytorch/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([SAM-Adapter-PyTorch markdown twin](/tools/tianrun-chen-sam-adapter-pytorch/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/tianrun-chen-sam-adapter-pytorch-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, SAM-Adapter-PyTorch or alpaca-lora? SAM-Adapter-PyTorch: Slowing. 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 SAM-Adapter-PyTorch and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [SAM-Adapter-PyTorch trust report](/tools/tianrun-chen-sam-adapter-pytorch/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tianrun-chen-sam-adapter-pytorch`](/api/graphcanon/graph?tool=tianrun-chen-sam-adapter-pytorch) - 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/_