Unsloth
Mlops
Open-source library for fast, memory-efficient LLM fine-tuning
llm-fine-tuningopen-sourcegpu-optimization
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Axolotl
Open-source framework that makes LLM fine-tuning reproducible from a single YAML config
Free plan Claimed Self-hosted
About Axolotl
Axolotl is a free, open-source LLM fine-tuning framework that makes training reproducible via a single YAML config, supporting LoRA, QLoRA, full fine-tuning, and distributed multi-GPU runs.
Axolotl is one of the most popular open-source frameworks for fine-tuning large language models. It wraps the underlying stack, including Transformers, PEFT, DeepSpeed and FSDP, and flash attention, behind a single declarative YAML configuration, so experiments become reproducible and easy to share. This design lowers the barrier to serious fine-tuning while still exposing advanced techniques like LoRA, QLoRA, full fine-tuning, and distributed multi-GPU training. The project is actively maintained: through 2026 it has added expert parallelism for distributed mixture-of-experts training, context parallelism, low-bit training paths, and support for a steady stream of new model architectures. Because it is MIT/Apache licensed, teams can inspect, fork, modify, and self-host with no vendor lock-in, running on their own GPUs or on GPU clouds. Axolotl is the default choice for many practitioners doing production fine-tuning runs on open-weight models.
TL;DR
Axolotl is a free, open-source LLM fine-tuning framework that makes reproducible training simple via YAML, supporting LoRA, QLoRA, full fine-tuning, and distributed multi-GPU runs.
Company overview
Axolotl is a community-driven open-source project (axolotl-ai-cloud) that has become a default framework for fine-tuning open-weight LLMs. It emphasizes reproducibility, breadth of technique support, and freedom from vendor lock-in.
Development is highly active, with frequent releases adding new model architectures and advanced training methods, and an associated cloud effort exists around the core open-source project.
Product features
Axolotl wraps Transformers, PEFT, DeepSpeed, and FSDP behind a single YAML config, supporting LoRA, QLoRA, and full fine-tuning. In 2026 it added expert and context parallelism and low-bit training paths.
Because it is MIT/Apache licensed and self-hostable, teams can run it on any GPU infrastructure, inspect and modify the code, and share exact configs for reproducible experiments.
Target market
ML engineers, researchers, and AI teams that fine-tune open-weight models and need a flexible, reproducible, self-hostable training framework.
Buyer personas
End users
ML engineers and researchers fine-tuning models.
Buyers
Teams standardizing on an open training framework.
Key influencers
Open-source ML practitioners and model authors.
Ideal customer profile
Technical teams doing serious fine-tuning of open-weight LLMs on their own compute.
Funding & performance
Community-driven open-source project with an associated cloud effort; funding details should be verified with the maintainers.
Pros & cons
Pros
- Free and open source under MIT/Apache
- Single YAML config makes runs reproducible
- Supports LoRA, QLoRA, and full fine-tuning
- Multi-GPU training with FSDP and DeepSpeed
- Very active development and new model support
- No vendor lock-in and fully self-hostable
Cons
- Requires ML and infrastructure expertise
- No managed UI or hosted service in the core project
- You supply and pay for your own GPUs
- Debugging distributed runs can be complex
- Not aimed at non-technical users
Key features
Self-hosted
Multi-language
Integrations
Hugging Face, DeepSpeed, FSDP, PyTorch, RunPod
Input types
text
Output types
text
Best For
fine-tuning open-weight LLMs, reproducible training configs, multi-GPU training
Compare key features
View all alternatives →Feature
Axolotl
Unsloth
Predibase
Pricing
Free
Freemium
Paid
Free plan
Yes
Yes
No
Free trial
No
No
Yes
API
No
Yes
Yes
Self-hosted
Yes
Yes
Yes
Team support
No
No
Yes
Frequently asked questions
Is Axolotl free?+
Yes. Axolotl is fully open source under MIT/Apache licensing; the only cost is the GPU compute you run it on.
What fine-tuning methods does Axolotl support?+
It supports LoRA, QLoRA, full-parameter fine-tuning, and modern techniques, configured through a single YAML file.
Can Axolotl do multi-GPU training?+
Yes. Axolotl integrates FSDP and DeepSpeed to parallelize training across multiple GPUs for faster, larger runs.
Which models does it support?+
Axolotl supports a wide and growing range of open-weight architectures, with new model support added frequently throughout 2026.
Do I need to write code to use Axolotl?+
Most configuration happens in YAML, but you still need ML knowledge and command-line comfort to prepare data and launch training.
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