Comparison
LLM-Adapters vs litgpt
Verdict
Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; pick litgpt if litgpt offers a suite of over 20 high-performance large language models, with tools for pretraining, finetuning, and deployment at scale, all under the Apache-2.0 license.
Markdown twin · LLM-Adapters alternatives · litgpt alternatives
GraphCanon updated Sep 20, 2026
14views this month
Trust & integrity
| Signal | LLM-Adapters | litgpt |
|---|---|---|
| Maintenance | Dormant (923d since push) As of Sep 19, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- LLM-Adapters
- Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs
- litgpt
- High-performance LLMs for pretraining, finetuning, and deployment
Stars
- LLM-Adapters
- 1.2k
- litgpt
- 14k
Forks
- LLM-Adapters
- 116
- litgpt
- 1.5k
Open issues
- LLM-Adapters
- 55
- litgpt
- 290
Language
- LLM-Adapters
- Python
- litgpt
- Python
Adopt for
- LLM-Adapters
- LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing.
- litgpt
- litgpt offers a suite of over 20 high-performance large language models, with tools for pretraining, finetuning, and deployment at scale, all under the Apache-2.0 license.
Persona
- LLM-Adapters
- -
- litgpt
- -
Runtime
- LLM-Adapters
- -
- litgpt
- -
License
- LLM-Adapters
- Apache-2.0
- litgpt
- Apache-2.0
Last pushed
- LLM-Adapters
- Mar 10, 2024
- litgpt
- Sep 14, 2026
Categories
- LLM-Adapters
- LLM Frameworks, Model Training
- litgpt
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- LLM-Adapters
- Dormant (18%)
- litgpt
- Very active (96%)
Days since push
- LLM-Adapters
- 923d
- litgpt
- 3d
Open issues (now)
- LLM-Adapters
- 55
- litgpt
- 290
Stars delta
- LLM-Adapters
- +1 (30d)
- litgpt
- +62 (30d)
Open issues delta
- LLM-Adapters
- 0 (30d)
- litgpt
- +18 (30d)
Full report
- LLM-Adapters
- Trust report
- litgpt
- Trust report
Shared compatibility
- Python · LLM-Adapters: Python runtime · litgpt: Python runtime
Choose LLM-Adapters if…
- Tags unique to LLM-Adapters: adapters, fine-tuning, parameter-efficient.
- Optimizing resource usage when you need to fine-tune large language models without altering their core parameters
- Leaner open-issue backlog (55).
When NOT to use LLM-Adapters
- You require a full retraining approach that modifies all model weights, not just adapters
- Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023
Choose litgpt if…
- Tags unique to litgpt: ai, artificial-intelligence, deep-learning, llm.
- Also covers Inference & Serving.
- When you need a comprehensive set of over 20 high-performance LLMs for pretraining, finetuning, and deployment.
When NOT to use litgpt
- If you are looking for a tool that supports languages other than Python, as litgpt is exclusively Python-based.
- When you need a tool that offers fewer model options, as litgpt provides over 20 models which might be overwhelming for specific use cases.
- If you require proprietary licensing, as litgpt is open-source under the Apache-2.0 license.
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 Sep 20, 2026
- GitHub forks (AGI-Edgerunners/LLM-Adapters) · observed Sep 20, 2026
- Last push (AGI-Edgerunners/LLM-Adapters) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Lightning-AI/litgpt) · observed Sep 20, 2026
- GitHub forks (Lightning-AI/litgpt) · observed Sep 20, 2026
- Last push (Lightning-AI/litgpt) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: LLM-Adapters 1.2k · litgpt 14k (synced Sep 20, 2026).
Common questions
- What is the difference between LLM-Adapters and litgpt?
- LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. litgpt: High-performance LLMs for pretraining, finetuning, and deployment. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLM-Adapters over litgpt?
- Choose LLM-Adapters over litgpt when Tags unique to LLM-Adapters: adapters, fine-tuning, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters; Leaner open-issue backlog (55).
- When should I choose litgpt over LLM-Adapters?
- Choose litgpt over LLM-Adapters when Tags unique to litgpt: ai, artificial-intelligence, deep-learning, llm; Also covers Inference & Serving; When you need a comprehensive set of over 20 high-performance LLMs for pretraining, finetuning, and deployment.
- When should I avoid LLM-Adapters?
- You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023
- When should I avoid litgpt?
- If you are looking for a tool that supports languages other than Python, as litgpt is exclusively Python-based. When you need a tool that offers fewer model options, as litgpt provides over 20 models which might be overwhelming for specific use cases. If you require proprietary licensing, as litgpt is open-source under the Apache-2.0 license.
- Is LLM-Adapters or litgpt more popular on GitHub?
- litgpt has more GitHub stars (13,667 vs 1,235). Stars measure visibility, not whether either tool fits your constraints.
- Are LLM-Adapters and litgpt open source?
- Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, litgpt: Apache-2.0).
- Where can I find alternatives to LLM-Adapters or litgpt?
- GraphCanon lists graph-backed alternatives at LLM-Adapters alternatives and litgpt alternatives (LLM-Adapters markdown twin, litgpt 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 litgpt?
- LLM-Adapters: Dormant. litgpt: Very 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 litgpt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLM-Adapters trust report; litgpt trust report.