Comparison
LLM-Adapters vs awesome
Verdict
Pick LLM-Adapters when license: LLM-Adapters is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, LLM-Adapters is Apache-2.0.
Markdown twin · LLM-Adapters alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | LLM-Adapters | awesome |
|---|---|---|
| Maintenance | Dormant (853d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- LLM-Adapters
- Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning using Adapters
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- LLM-Adapters
- 1.2k
- awesome
- 484k
Forks
- LLM-Adapters
- 119
- awesome
- 36k
Open issues
- LLM-Adapters
- 55
- awesome
- 92
Language
- LLM-Adapters
- Python
- awesome
- -
Adopt for
- LLM-Adapters
- -
- awesome
- -
Persona
- LLM-Adapters
- -
- awesome
- -
Runtime
- LLM-Adapters
- -
- awesome
- -
License
- LLM-Adapters
- Apache-2.0
- awesome
- CC0-1.0
Last pushed
- LLM-Adapters
- Mar 10, 2024
- awesome
- Jun 30, 2026
Categories
- LLM-Adapters
- LLM Frameworks, Model Training
- awesome
- LLM Frameworks
Trust and health
Maintenance
- LLM-Adapters
- Dormant (18%)
- awesome
- Active (82%)
Days since push
- LLM-Adapters
- 853d
- awesome
- 11d
Open issues (now)
- LLM-Adapters
- 55
- awesome
- 92
Owner type
- LLM-Adapters
- Organization
- awesome
- User
Full report
- LLM-Adapters
- Trust report
- awesome
- Trust report
Choose LLM-Adapters if…
- License: LLM-Adapters is Apache-2.0, awesome is CC0-1.0.
- Tags unique to LLM-Adapters: fine-tuning, adapters, large-language-models, parameter-efficient.
- Also covers Model Training.
When NOT to use LLM-Adapters
- Last GitHub push was 853 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on LLM-Adapters.
- 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.
Choose awesome if…
- License: awesome is CC0-1.0, LLM-Adapters is Apache-2.0.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 1.2k) - visibility, not fit.
When NOT to use awesome
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AGI-Edgerunners/LLM-Adapters) · observed Jul 11, 2026
- GitHub forks (AGI-Edgerunners/LLM-Adapters) · observed Jul 11, 2026
- Last push (AGI-Edgerunners/LLM-Adapters) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLM-Adapters 1.2k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between LLM-Adapters and awesome?
- LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning using Adapters. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLM-Adapters over awesome?
- Choose LLM-Adapters over awesome when License: LLM-Adapters is Apache-2.0, awesome is CC0-1.0; Tags unique to LLM-Adapters: fine-tuning, adapters, large-language-models, parameter-efficient; Also covers Model Training.
- When should I choose awesome over LLM-Adapters?
- Choose awesome over LLM-Adapters when License: awesome is CC0-1.0, LLM-Adapters is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 1.2k) - visibility, not fit.
- When should I avoid LLM-Adapters?
- Last GitHub push was 853 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on LLM-Adapters. 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.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is LLM-Adapters or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 1,233). Stars measure visibility, not whether either tool fits your constraints.
- Are LLM-Adapters and awesome open source?
- Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, awesome: CC0-1.0).
- Where can I find alternatives to LLM-Adapters or awesome?
- GraphCanon lists graph-backed alternatives at LLM-Adapters alternatives and awesome alternatives (LLM-Adapters markdown twin, awesome 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, LLM-Adapters or awesome?
- LLM-Adapters: Dormant. awesome: Active. 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 LLM-Adapters and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLM-Adapters trust report; awesome trust report.