--- title: "ml-surveys vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-tloen-alpaca-lora" tools: ["eugeneyan-ml-surveys", "tloen-alpaca-lora"] --- # ml-surveys vs alpaca-lora *GraphCanon updated Jul 12, 2026* ## Verdict Pick ml-surveys when license: ml-surveys is MIT, alpaca-lora is Apache-2.0; pick alpaca-lora when license: alpaca-lora is Apache-2.0, ml-surveys is MIT. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 291 forks, and 2 open issues, last pushed Mar 17, 2023. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 366 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc. | Instruct-tune LLaMA on consumer hardware | | Stars | 2,900 | 18,913 | | Forks | 291 | 2,185 | | Open issues | 2 | 366 | | Language | - | Jupyter Notebook | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Vector Databases, Computer Vision | Model Training, Inference & Serving, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 1212d | 712d | | Open issues (now) | 2 | 366 | | Security scan | No lockfile | 1 critical, 5 high, 12 medium, 28 low (1 critical, 5 high, 12 medium, 28 low) | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Choose when ### Choose ml-surveys if… - License: ml-surveys is MIT, alpaca-lora is Apache-2.0. - Tags unique to ml-surveys: reinforcement-learning, embeddings, deep-learning, nlp. - Also covers Vector Databases. ### Choose alpaca-lora if… - License: alpaca-lora is Apache-2.0, ml-surveys is MIT. - Tags unique to alpaca-lora: jupyter notebook. - Also covers Model Training, Inference & Serving. - alpaca-lora ships Docker support for self-hosted deployment. ## When NOT to use ml-surveys - Last GitHub push was 1213 days ago (dormant maintenance, Mar 17, 2023). Validate activity before betting a new project on ml-surveys. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use alpaca-lora - Last GitHub push was 712 days ago (dormant maintenance, Jul 29, 2024). Validate activity before betting a new project on alpaca-lora. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between ml-surveys and alpaca-lora? ml-surveys: 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose ml-surveys over alpaca-lora? Choose ml-surveys over alpaca-lora when License: ml-surveys is MIT, alpaca-lora is Apache-2.0; Tags unique to ml-surveys: reinforcement-learning, embeddings, deep-learning, nlp; Also covers Vector Databases. ### When should I choose alpaca-lora over ml-surveys? Choose alpaca-lora over ml-surveys when License: alpaca-lora is Apache-2.0, ml-surveys is MIT; Tags unique to alpaca-lora: jupyter notebook; Also covers Model Training, Inference & Serving; alpaca-lora ships Docker support for self-hosted deployment. ### When should I avoid ml-surveys? Last GitHub push was 1213 days ago (dormant maintenance, Mar 17, 2023). Validate activity before betting a new project on ml-surveys. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid alpaca-lora? Last GitHub push was 712 days ago (dormant maintenance, Jul 29, 2024). Validate activity before betting a new project on alpaca-lora. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is ml-surveys or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,913 vs 2,900). Stars measure visibility, not whether either tool fits your constraints. ### Are ml-surveys and alpaca-lora open source? Yes - both are open-source projects on GitHub (ml-surveys: MIT, alpaca-lora: Apache-2.0). ### Where can I find alternatives to ml-surveys or alpaca-lora? GraphCanon lists graph-backed alternatives at [ml-surveys alternatives](/tools/eugeneyan-ml-surveys/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/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/eugeneyan-ml-surveys-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, ml-surveys or alpaca-lora? ml-surveys: 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 ml-surveys and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ml-surveys trust report](/tools/eugeneyan-ml-surveys/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - 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/_