Comparison
peft vs Liger-Kernel
Verdict
Pick peft when license: peft is Apache-2.0, Liger-Kernel is BSD-2-Clause; pick Liger-Kernel when license: Liger-Kernel is BSD-2-Clause, peft is Apache-2.0.
Markdown twin · peft alternatives · Liger-Kernel alternatives
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Trust & integrity
| Signal | peft | Liger-Kernel |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Very active (4d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of 1d · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of 1d · none |
Tagline
- peft
- State-of-the-art Parameter-Efficient Fine-Tuning
- Liger-Kernel
- Efficient Triton Kernels for LLM Training
Stars
- peft
- 21k
- Liger-Kernel
- 6.5k
Forks
- peft
- 2.4k
- Liger-Kernel
- 554
Open issues
- peft
- 62
- Liger-Kernel
- 161
Language
- peft
- Python
- Liger-Kernel
- Python
Adopt for
- peft
- PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.
- Liger-Kernel
- -
Persona
- peft
- -
- Liger-Kernel
- -
Runtime
- peft
- -
- Liger-Kernel
- -
License
- peft
- Apache-2.0
- Liger-Kernel
- BSD-2-Clause
Last pushed
- peft
- Jul 10, 2026
- Liger-Kernel
- Jul 6, 2026
Categories
- peft
- LLM Frameworks, Model Training
- Liger-Kernel
- LLM Frameworks, Model Training
Trust and health
Days since push
- peft
- 0d
- Liger-Kernel
- 4d
Open issues (now)
- peft
- 62
- Liger-Kernel
- 161
Full report
- peft
- Trust report
- Liger-Kernel
- Trust report
Choose peft if…
- License: peft is Apache-2.0, Liger-Kernel is BSD-2-Clause.
- Tags unique to peft: adapter, diffusion, fine-tuning, llm.
- When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
When NOT to use peft
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only.
- When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
Choose Liger-Kernel if…
- License: Liger-Kernel is BSD-2-Clause, peft is Apache-2.0.
- Tags unique to Liger-Kernel: finetuning, gemma2, hacktoberfest, llama.
When NOT to use Liger-Kernel
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huggingface/peft) · observed Jul 11, 2026
- GitHub forks (huggingface/peft) · observed Jul 11, 2026
- Last push (huggingface/peft) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (linkedin/Liger-Kernel) · observed Jul 11, 2026
- GitHub forks (linkedin/Liger-Kernel) · observed Jul 11, 2026
- Last push (linkedin/Liger-Kernel) · observed Jul 6, 2026
- License file (BSD-2-Clause) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: peft 21k · Liger-Kernel 6.5k (synced Jul 11, 2026).
Common questions
- What is the difference between peft and Liger-Kernel?
- peft: State-of-the-art Parameter-Efficient Fine-Tuning. Liger-Kernel: Efficient Triton Kernels for LLM Training. See the comparison table for live GitHub stats and shared categories.
- When should I choose peft over Liger-Kernel?
- Choose peft over Liger-Kernel when License: peft is Apache-2.0, Liger-Kernel is BSD-2-Clause; Tags unique to peft: adapter, diffusion, fine-tuning, llm; When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
- When should I choose Liger-Kernel over peft?
- Choose Liger-Kernel over peft when License: Liger-Kernel is BSD-2-Clause, peft is Apache-2.0; Tags unique to Liger-Kernel: finetuning, gemma2, hacktoberfest, llama.
- When should I avoid peft?
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
- When should I avoid Liger-Kernel?
- 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.
- Is peft or Liger-Kernel more popular on GitHub?
- peft has more GitHub stars (21,385 vs 6,494). Stars measure visibility, not whether either tool fits your constraints.
- Are peft and Liger-Kernel open source?
- Yes - both are open-source projects on GitHub (peft: Apache-2.0, Liger-Kernel: BSD-2-Clause).
- Where can I find alternatives to peft or Liger-Kernel?
- GraphCanon lists graph-backed alternatives at peft alternatives and Liger-Kernel alternatives (peft markdown twin, Liger-Kernel 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, peft or Liger-Kernel?
- peft: Very active. Liger-Kernel: 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 peft and Liger-Kernel?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: peft trust report; Liger-Kernel trust report.