--- title: "align-anything vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pku-alignment-align-anything-vs-tloen-alpaca-lora" tools: ["pku-alignment-align-anything", "tloen-alpaca-lora"] --- # align-anything vs alpaca-lora *GraphCanon updated Aug 17, 2026* ## Verdict Pick align-anything if align Anything focuses on training large models with multiple forms of feedback across various data modalities, leveraging RLHF and DPO; 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. [align-anything](https://github.com/PKU-Alignment/align-anything) reports 4.7k GitHub stars, 505 forks, and 32 open issues, last pushed Nov 27, 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 [align-anything's repository](https://github.com/PKU-Alignment/align-anything) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [align-anything](/tools/pku-alignment-align-anything.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Training All-modality Model with Feedback | Instruct-tune LLaMA on consumer hardware | | Stars | 4,666 | 18,912 | | Forks | 505 | 2,180 | | Open issues | 32 | 365 | | Language | Python | Jupyter Notebook | | Adopt for | Align Anything focuses on training large models with multiple forms of feedback across various data modalities, leveraging RLHF and DPO. | 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 | This tool operates under Apache License 2.0, allowing free use, modification, and distribution provided copyright notices are preserved. | 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._ | | [align-anything](/tools/pku-alignment-align-anything.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 263d | 734d | | Open issues (now) | 32 | 365 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/pku-alignment-align-anything/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Decision facts: align-anything - **Requirements:** Python execution environment - **Adopt for:** Align Anything focuses on training large models with multiple forms of feedback across various data modalities, leveraging RLHF and DPO. - **License detail:** This tool operates under Apache License 2.0, allowing free use, modification, and distribution provided copyright notices are preserved. ## 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 align-anything if… - align-anything is primarily Python; alpaca-lora is Jupyter Notebook. - Requirements: Python execution environment. - Tags unique to align-anything: chameleon, dpo, large language models, multimodal. - - When you are developing a model that requires feedback from human evaluators and needs to handle different types of data (multimodal). ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; align-anything 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. - 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 align-anything - - When the model training does not benefit from advanced feedback mechanisms like RLHF or DPO. - - For projects that do not require support for multiple data modalities. ## 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 align-anything and alpaca-lora? align-anything: Training All-modality Model with Feedback. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose align-anything over alpaca-lora? Choose align-anything over alpaca-lora when align-anything is primarily Python; alpaca-lora is Jupyter Notebook; Requirements: Python execution environment; Tags unique to align-anything: chameleon, dpo, large language models, multimodal; - When you are developing a model that requires feedback from human evaluators and needs to handle different types of data (multimodal). ### When should I choose alpaca-lora over align-anything? Choose alpaca-lora over align-anything when alpaca-lora is primarily Jupyter Notebook; align-anything 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; 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 align-anything? - When the model training does not benefit from advanced feedback mechanisms like RLHF or DPO. - For projects that do not require support for multiple data modalities. ### 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 align-anything or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 4,666). Stars measure visibility, not whether either tool fits your constraints. ### Are align-anything and alpaca-lora open source? Yes - both are open-source projects on GitHub (align-anything: Apache-2.0, alpaca-lora: Apache-2.0). ### Where can I find alternatives to align-anything or alpaca-lora? GraphCanon lists graph-backed alternatives at [align-anything alternatives](/tools/pku-alignment-align-anything/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([align-anything markdown twin](/tools/pku-alignment-align-anything/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/pku-alignment-align-anything-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, align-anything or alpaca-lora? align-anything: 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 align-anything and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [align-anything trust report](/tools/pku-alignment-align-anything/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pku-alignment-align-anything`](/api/graphcanon/graph?tool=pku-alignment-align-anything) - 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/_