--- title: "peft vs Liger-Kernel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-peft-vs-linkedin-liger-kernel" tools: ["huggingface-peft", "linkedin-liger-kernel"] --- # peft vs Liger-Kernel *GraphCanon updated Jul 12, 2026* ## 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. [peft](https://huggingface.co/docs/peft) reports 21k GitHub stars, 2.4k forks, and 62 open issues, last pushed Jul 10, 2026. [Liger-Kernel](https://linkedin.github.io/Liger-Kernel/) has 6.5k stars, 554 forks, and 161 open issues, last pushed Jul 6, 2026. Figures are from public GitHub metadata via [peft's repository](https://github.com/huggingface/peft) and [Liger-Kernel's repository](https://github.com/linkedin/Liger-Kernel). | | [peft](/tools/huggingface-peft.md) | [Liger-Kernel](/tools/linkedin-liger-kernel.md) | | --- | --- | --- | | Tagline | State-of-the-art Parameter-Efficient Fine-Tuning | Efficient Triton Kernels for LLM Training | | Stars | 21,385 | 6,494 | | Forks | 2,385 | 554 | | Open issues | 62 | 161 | | Language | Python | Python | | Adopt for | PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-2-Clause | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [peft](/tools/huggingface-peft.md) | [Liger-Kernel](/tools/linkedin-liger-kernel.md) | | --- | --- | --- | | Days since push | 0d | 4d | | Open issues (now) | 62 | 161 | | Full report | [trust report](/tools/huggingface-peft/trust.md) | [trust report](/tools/linkedin-liger-kernel/trust.md) | ## Decision facts: peft - **Adopt for:** PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/huggingface-peft/alternatives) and [Liger-Kernel alternatives](/tools/linkedin-liger-kernel/alternatives) ([peft markdown twin](/tools/huggingface-peft/alternatives.md), [Liger-Kernel markdown twin](/tools/linkedin-liger-kernel/alternatives.md)), 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](/compare/huggingface-peft-vs-linkedin-liger-kernel.md) 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](/tools/huggingface-peft/trust); [Liger-Kernel trust report](/tools/linkedin-liger-kernel/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-peft`](/api/graphcanon/graph?tool=huggingface-peft) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_