axolotl vs peft
LLM post-training frameworks compared
axolotl and peft are Python projects that help adapt pretrained models with parameter-efficient methods such as LoRA. axolotl provides configurable workflows spanning training and related tasks, while peft focuses on adding and managing trainable parameters within other libraries' workflows.
axolotl: Fine-Tune Large Language Models
Axolotl is a Python framework for fine-tuning and post-training language models through configurable workflows. It supports methods from LoRA and QLoRA to preference tuning and reinforcement learning, with options for multimodal and distributed training.
peft: Fine-Tune Models with Fewer Trainable Parameters
Hugging Face PEFT adapts pretrained models by training a small set of additional parameters instead of updating the full model. It is for practitioners who want to reduce fine-tuning compute and storage costs across supported model workflows.
| axolotl | peft | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 12.5k | 21.8k |
| Forks | 1.5k | 2.5k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- axolotl covers supervised and preference tuning, reinforcement learning, evaluation, quantization, and inference; peft focuses on parameter-efficient adaptation rather than a complete training pipeline.
- axolotl offers YAML-configured workflows and integrations for distributed training; peft supplies adapter methods and integrates with libraries such as Transformers, Diffusers, Accelerate, and TRL.
- axolotl supports multimodal training for supported vision, audio, and video models; peft supports LLMs, diffusion models, and other compatible architectures.
- peft saves adapters separately from base models and supports switching between adapters in compatible Transformers workflows; axolotl describes a broader workflow for preparing, training, evaluating, and using models.
- Both projects use Python and Apache-2.0 licensing; peft lists 21.8k stars and 2.5k forks, while axolotl lists 12.5k stars and 1.5k forks.
Choose axolotl if you…
- need configurable workflows for supervised fine-tuning, preference tuning, or reinforcement learning.
- want one framework covering training alongside evaluation, quantization, and inference.
- need multimodal or distributed training options for supported models.
Choose peft if you…
- want parameter-efficient adaptation methods and separately saved adapters.
- need to integrate adaptation into workflows built around Hugging Face libraries.
- want to compare task-specific adaptations without saving a full model copy for each task.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.