Comparison
LLM-Adapters vs FineTuningLLMs
Verdict
Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
Markdown twin · LLM-Adapters alternatives · FineTuningLLMs alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | LLM-Adapters | FineTuningLLMs |
|---|---|---|
| Maintenance | Dormant (896d since push) As of today · github_public_v1 | Slowing (176d 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 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- FineTuningLLMs
- Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'
Stars
- LLM-Adapters
- 1.2k
- FineTuningLLMs
- 855
Forks
- LLM-Adapters
- 115
- FineTuningLLMs
- 116
Open issues
- LLM-Adapters
- 55
- FineTuningLLMs
- 4
Language
- LLM-Adapters
- Python
- FineTuningLLMs
- Jupyter Notebook
Adopt for
- LLM-Adapters
- LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing.
- FineTuningLLMs
- FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
Persona
- LLM-Adapters
- -
- FineTuningLLMs
- -
Runtime
- LLM-Adapters
- -
- FineTuningLLMs
- -
License
- LLM-Adapters
- Apache-2.0
- FineTuningLLMs
- MIT
Last pushed
- LLM-Adapters
- Mar 10, 2024
- FineTuningLLMs
- Feb 28, 2026
Categories
- LLM-Adapters
- LLM Frameworks, Model Training
- FineTuningLLMs
- LLM Frameworks, Model Training
Trust and health
Maintenance
- LLM-Adapters
- Dormant (18%)
- FineTuningLLMs
- Slowing (36%)
Days since push
- LLM-Adapters
- 896d
- FineTuningLLMs
- 176d
Open issues (now)
- LLM-Adapters
- 55
- FineTuningLLMs
- 4
Stars delta
- LLM-Adapters
- -1 (30d)
- FineTuningLLMs
- +4 (30d)
Owner type
- LLM-Adapters
- Organization
- FineTuningLLMs
- User
Full report
- LLM-Adapters
- Trust report
- FineTuningLLMs
- Trust report
Shared compatibility
- ChatGPT · LLM-Adapters: Works with ChatGPT · FineTuningLLMs: Works with ChatGPT
Choose LLM-Adapters if…
- LLM-Adapters is primarily Python; FineTuningLLMs is Jupyter Notebook.
- License: LLM-Adapters is Apache-2.0, FineTuningLLMs is MIT.
- Tags unique to LLM-Adapters: adapters, parameter-efficient.
- Optimizing resource usage when you need to fine-tune large language models without altering their core parameters
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 FineTuningLLMs if…
- FineTuningLLMs is primarily Jupyter Notebook; LLM-Adapters is Python.
- License: FineTuningLLMs is MIT, LLM-Adapters is Apache-2.0.
- Tags unique to FineTuningLLMs: bitsandbytes, finetuning, hugging-face, llamacpp.
- You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem
When NOT to use FineTuningLLMs
- Not interested in PyTorch; prefer TensorFlow or another framework
- Seek theoretical background over practical applications
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 Aug 24, 2026
- GitHub forks (AGI-Edgerunners/LLM-Adapters) · observed Aug 24, 2026
- Last push (AGI-Edgerunners/LLM-Adapters) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (dvgodoy/FineTuningLLMs) · observed Aug 24, 2026
- GitHub forks (dvgodoy/FineTuningLLMs) · observed Aug 24, 2026
- Last push (dvgodoy/FineTuningLLMs) · observed Feb 28, 2026
- License file (MIT) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLM-Adapters 1.2k · FineTuningLLMs 855 (synced Aug 24, 2026).
Common questions
- What is the difference between LLM-Adapters and FineTuningLLMs?
- LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLM-Adapters over FineTuningLLMs?
- Choose LLM-Adapters over FineTuningLLMs when LLM-Adapters is primarily Python; FineTuningLLMs is Jupyter Notebook; License: LLM-Adapters is Apache-2.0, FineTuningLLMs is MIT; Tags unique to LLM-Adapters: adapters, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters.
- When should I choose FineTuningLLMs over LLM-Adapters?
- Choose FineTuningLLMs over LLM-Adapters when FineTuningLLMs is primarily Jupyter Notebook; LLM-Adapters is Python; License: FineTuningLLMs is MIT, LLM-Adapters is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, finetuning, hugging-face, llamacpp; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem.
- 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 FineTuningLLMs?
- Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications
- Is LLM-Adapters or FineTuningLLMs more popular on GitHub?
- LLM-Adapters has more GitHub stars (1,233 vs 855). Stars measure visibility, not whether either tool fits your constraints.
- Are LLM-Adapters and FineTuningLLMs open source?
- Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, FineTuningLLMs: MIT).
- Where can I find alternatives to LLM-Adapters or FineTuningLLMs?
- GraphCanon lists graph-backed alternatives at LLM-Adapters alternatives and FineTuningLLMs alternatives (LLM-Adapters markdown twin, FineTuningLLMs 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 FineTuningLLMs?
- LLM-Adapters: Dormant. FineTuningLLMs: Slowing. 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 FineTuningLLMs?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLM-Adapters trust report; FineTuningLLMs trust report.