Comparison
Awesome-AutoDL vs stanford_alpaca
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.
Markdown twin · Awesome-AutoDL alternatives · stanford_alpaca alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Awesome-AutoDL | stanford_alpaca |
|---|---|---|
| Maintenance | Dormant (1384d since push) As of today · github_public_v1 | Dormant (724d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | 46 low (46 low) As of today · osv@v1 |
Tagline
- 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.
Stars
- Awesome-AutoDL
- 2.3k
- stanford_alpaca
- 30k
Forks
- Awesome-AutoDL
- 319
- stanford_alpaca
- 4.0k
Open issues
- Awesome-AutoDL
- 2
- stanford_alpaca
- 188
Language
- Awesome-AutoDL
- Python
- stanford_alpaca
- Python
Adopt for
- Awesome-AutoDL
- -
- stanford_alpaca
- -
Persona
- Awesome-AutoDL
- -
- stanford_alpaca
- -
Runtime
- Awesome-AutoDL
- -
- stanford_alpaca
- -
License
- Awesome-AutoDL
- MIT
- stanford_alpaca
- Apache-2.0
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- stanford_alpaca
- Jul 17, 2024
Categories
- Awesome-AutoDL
- Model Training, Vector Databases, Speech & Audio
- stanford_alpaca
- LLM Frameworks, Model Training, Vector Databases
Trust and health
Days since push
- Awesome-AutoDL
- 1384d
- stanford_alpaca
- 724d
Open issues (now)
- Awesome-AutoDL
- 2
- stanford_alpaca
- 188
Owner type
- Awesome-AutoDL
- User
- stanford_alpaca
- Organization
Security scan
- Awesome-AutoDL
- No lockfile
- stanford_alpaca
- 46 low (46 low)
Full report
- Awesome-AutoDL
- Trust report
- stanford_alpaca
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, stanford_alpaca is Apache-2.0.
- Tags unique to Awesome-AutoDL: automl, hyper-parameter-optimization, neural-architecture-search, awesome.
- Also covers Speech & Audio.
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.
Choose stanford_alpaca if…
- License: stanford_alpaca is Apache-2.0, Awesome-AutoDL is MIT.
- Tags unique to stanford_alpaca: language-model, instruction-following.
- Also covers LLM Frameworks.
When NOT to use stanford_alpaca
- Last GitHub push was 724 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (D-X-Y/Awesome-AutoDL) · observed Jul 11, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Jul 11, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tatsu-lab/stanford_alpaca) · observed Jul 11, 2026
- GitHub forks (tatsu-lab/stanford_alpaca) · observed Jul 11, 2026
- Last push (tatsu-lab/stanford_alpaca) · observed Jul 17, 2024
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · stanford_alpaca 30k (synced Jul 11, 2026).
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: automl, hyper-parameter-optimization, neural-architecture-search, awesome; 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: language-model, instruction-following; 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 724 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 and stanford_alpaca alternatives (Awesome-AutoDL markdown twin, stanford_alpaca markdown twin), 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 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; stanford_alpaca trust report.