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Comparison

apps vs stanford_alpaca

Verdict

Pick apps when license: apps is MIT, stanford_alpaca is Apache-2.0; pick stanford_alpaca when license: stanford_alpaca is Apache-2.0, apps is MIT.

Markdown twin · apps alternatives · stanford_alpaca alternatives

GraphCanon updated today

apps logo

apps

hendrycks/apps

★ 536pushed Jun 19, 2024
vs
stanford_alpaca logo

stanford_alpaca

tatsu-lab/stanford_alpaca

★ 30kpushed Jul 17, 2024

Trust & integrity

Signalappsstanford_alpaca
Maintenance
Dormant (752d 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)
77 low (77 low)
As of today · osv@v1
46 low (46 low)
As of today · osv@v1
apps trust reportstanford_alpaca trust reportTrust methodology

Tagline

apps
APPS: Automated Programming Progress Standard (NeurIPS 2021)
stanford_alpaca
Code and documentation to train Stanford's Alpaca models, and generate the data.

Stars

apps
536
stanford_alpaca
30k

Forks

apps
70
stanford_alpaca
4.0k

Open issues

apps
4
stanford_alpaca
188

Language

apps
Python
stanford_alpaca
Python

Adopt for

apps
-
stanford_alpaca
-

Persona

apps
-
stanford_alpaca
-

Runtime

apps
-
stanford_alpaca
-

License

apps
MIT
stanford_alpaca
Apache-2.0

Last pushed

apps
Jun 19, 2024
stanford_alpaca
Jul 17, 2024

Categories

apps
Model Training, Vector Databases, Evaluation & Observability
stanford_alpaca
LLM Frameworks, Model Training, Vector Databases

Trust and health

Days since push

apps
752d
stanford_alpaca
724d

Open issues (now)

apps
4
stanford_alpaca
188

Owner type

apps
User
stanford_alpaca
Organization

Security scan

apps
77 low (77 low)
stanford_alpaca
46 low (46 low)

Full report

apps
Trust report
stanford_alpaca
Trust report

Choose apps if…

  • License: apps is MIT, stanford_alpaca is Apache-2.0.
  • Tags unique to apps: program-synthesis, code-generation.
  • Also covers Evaluation & Observability.

When NOT to use apps

  • Last GitHub push was 753 days ago (dormant maintenance, Jun 19, 2024). Validate activity before betting a new project on apps.
  • 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.
  • Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
GitHubDetails

Choose stanford_alpaca if…

  • License: stanford_alpaca is Apache-2.0, apps is MIT.
  • Tags unique to stanford_alpaca: deep-learning, 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.
GitHubDetails

Explore

apps trust report →stanford_alpaca trust report →Model Training category →Vector Databases category →Evaluation & Observability category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: apps 536 · stanford_alpaca 30k (synced Jul 11, 2026).

Common questions

What is the difference between apps and stanford_alpaca?
apps: APPS: Automated Programming Progress Standard (NeurIPS 2021). 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 apps over stanford_alpaca?
Choose apps over stanford_alpaca when License: apps is MIT, stanford_alpaca is Apache-2.0; Tags unique to apps: program-synthesis, code-generation; Also covers Evaluation & Observability.
When should I choose stanford_alpaca over apps?
Choose stanford_alpaca over apps when License: stanford_alpaca is Apache-2.0, apps is MIT; Tags unique to stanford_alpaca: deep-learning, language-model, instruction-following; Also covers LLM Frameworks.
When should I avoid apps?
Last GitHub push was 753 days ago (dormant maintenance, Jun 19, 2024). Validate activity before betting a new project on apps. 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. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
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 apps or stanford_alpaca more popular on GitHub?
stanford_alpaca has more GitHub stars (30,250 vs 536). Stars measure visibility, not whether either tool fits your constraints.
Are apps and stanford_alpaca open source?
Yes - both are open-source projects on GitHub (apps: MIT, stanford_alpaca: Apache-2.0).
Where can I find alternatives to apps or stanford_alpaca?
GraphCanon lists graph-backed alternatives at apps alternatives and stanford_alpaca alternatives (apps 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, apps or stanford_alpaca?
apps: 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 apps and stanford_alpaca?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: apps trust report; stanford_alpaca trust report.