--- title: "Awesome-AutoDL vs stanford_alpaca" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-tatsu-lab-stanford-alpaca" tools: ["d-x-y-awesome-autodl", "tatsu-lab-stanford-alpaca"] --- # Awesome-AutoDL vs stanford_alpaca *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-AutoDL when license: Awesome-AutoDL is MIT, stanford_alpaca is Apache-2.0; pick stanford_alpaca when license: stanford_alpaca is Apache-2.0, Awesome-AutoDL is MIT. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) reports 2.3k GitHub stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. [stanford_alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) has 30k stars, 4.0k forks, and 188 open issues, last pushed Jul 17, 2024. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [stanford_alpaca's repository](https://github.com/tatsu-lab/stanford_alpaca). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | | --- | --- | --- | | Tagline | Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis) | Code and documentation to train Stanford's Alpaca models, and generate the data. | | Stars | 2,339 | 30,250 | | Forks | 319 | 3,985 | | Open issues | 2 | 188 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Model Training, Speech & Audio, Vector Databases | LLM Frameworks, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | | --- | --- | --- | | Days since push | 1384d | 724d | | Open issues (now) | 2 | 188 | | Owner type | User | Organization | | Security scan | No lockfile | 46 low (46 low) | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/tatsu-lab-stanford-alpaca/trust.md) | ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, stanford_alpaca is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, automl, awesome, hyper-parameter-optimization. - Also covers Speech & Audio. ### Choose stanford_alpaca if… - License: stanford_alpaca is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to stanford_alpaca: instruction-following, language-model. - Also covers LLM Frameworks. ## When NOT to use Awesome-AutoDL - Last GitHub push was 1385 days ago (dormant maintenance, Sep 26, 2022). Validate activity before betting a new project on Awesome-AutoDL. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - 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 stanford_alpaca - Last GitHub push was 725 days ago (dormant maintenance, Jul 17, 2024). Validate activity before betting a new project on stanford_alpaca. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between Awesome-AutoDL and stanford_alpaca? Awesome-AutoDL: Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis). stanford_alpaca: Code and documentation to train Stanford's Alpaca models, and generate the data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over stanford_alpaca? Choose Awesome-AutoDL over stanford_alpaca when License: Awesome-AutoDL is MIT, stanford_alpaca is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, automl, awesome, hyper-parameter-optimization; Also covers Speech & Audio. ### When should I choose stanford_alpaca over Awesome-AutoDL? Choose stanford_alpaca over Awesome-AutoDL when License: stanford_alpaca is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to stanford_alpaca: instruction-following, language-model; Also covers LLM Frameworks. ### When should I avoid Awesome-AutoDL? Last GitHub push was 1385 days ago (dormant maintenance, Sep 26, 2022). Validate activity before betting a new project on Awesome-AutoDL. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. 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 stanford_alpaca? Last GitHub push was 725 days ago (dormant maintenance, Jul 17, 2024). Validate activity before betting a new project on stanford_alpaca. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is Awesome-AutoDL or stanford_alpaca more popular on GitHub? stanford_alpaca has more GitHub stars (30,250 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and stanford_alpaca open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, stanford_alpaca: Apache-2.0). ### Where can I find alternatives to Awesome-AutoDL or stanford_alpaca? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [stanford_alpaca alternatives](/tools/tatsu-lab-stanford-alpaca/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [stanford_alpaca markdown twin](/tools/tatsu-lab-stanford-alpaca/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/d-x-y-awesome-autodl-vs-tatsu-lab-stanford-alpaca.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or stanford_alpaca? Awesome-AutoDL: Dormant. stanford_alpaca: 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-AutoDL and stanford_alpaca? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [stanford_alpaca trust report](/tools/tatsu-lab-stanford-alpaca/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=d-x-y-awesome-autodl`](/api/graphcanon/graph?tool=d-x-y-awesome-autodl) - 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/_