--- title: "awesome-automl-papers vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-tloen-alpaca-lora" tools: ["hibayesian-awesome-automl-papers", "tloen-alpaca-lora"] --- # awesome-automl-papers vs alpaca-lora *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-automl-papers when tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization; pick alpaca-lora when tags unique to alpaca-lora: jupyter notebook. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 680 forks, and 2 open issues, last pushed Jun 11, 2024. [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 [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers, articles, tutorials, slides and projects | Instruct-tune LLaMA on consumer hardware | | Stars | 4,152 | 18,913 | | Forks | 680 | 2,185 | | Open issues | 2 | 366 | | Language | - | Jupyter Notebook | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | 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._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 760d | 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/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Choose when ### Choose awesome-automl-papers if… - Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization. - Also covers Vector Databases. - Leaner open-issue backlog (2). ### Choose alpaca-lora if… - 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 awesome-automl-papers - Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers. - 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 awesome-automl-papers and alpaca-lora? awesome-automl-papers: A curated list of automated machine learning papers, articles, tutorials, slides and projects. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-automl-papers over alpaca-lora? Choose awesome-automl-papers over alpaca-lora when Tags unique to awesome-automl-papers: automl, neural-architecture-search, automated-feature-engineering, hyperparameter-optimization; Also covers Vector Databases; Leaner open-issue backlog (2). ### When should I choose alpaca-lora over awesome-automl-papers? Choose alpaca-lora over awesome-automl-papers when 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 awesome-automl-papers? Last GitHub push was 760 days ago (dormant maintenance, Jun 11, 2024). Validate activity before betting a new project on awesome-automl-papers. 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 awesome-automl-papers or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,913 vs 4,152). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and alpaca-lora open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, alpaca-lora: Apache-2.0). ### Where can I find alternatives to awesome-automl-papers or alpaca-lora? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/hibayesian-awesome-automl-papers-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, awesome-automl-papers or alpaca-lora? awesome-automl-papers: 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 awesome-automl-papers and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - 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/_