Axolotl
Free open-source framework for fine-tuning LLMs, driven by config files and supporting many new models and methods.
How the 7.0 was reached
- Quality of output7/10
12,512 stars, 1,449 forks, Apache-2.0, pushed recently, and frequent support for new models and training methods. github.com
- Ease of use5/10
Config-file driven and uv-first; no separate docs found on pages checked, so setup needs technical effort. github.com
- Pricing value9/10
Free Apache-2.0 code; user supplies their own GPU compute. github.com
- API and integration quality7/10
Integrates with Hugging Face libraries, PEFT, DeepSpeed and DDP; no separate reference docs were checked. github.com
- Solves the problem it claims to solve7/10
Active, widely starred open-source fine-tuning framework matching its claim of being free and open source. github.com
7.0/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
- Apache-2.0 licence
- You supply your own GPU compute
Key Features
- Config-file driven training setup
- LoRA training for MoE models with NVFP4 (4-bit)
- Expert Parallelism for distributed MoE training
- Context Parallelism for hybrid SSM models
- Integrates with Hugging Face, PEFT, DeepSpeed and DDP
Pros & Cons
Pros
- Apache-2.0 licence with 12,512 stars and 1,449 forks
- Frequent support for new models and training methods
- Free code; costs are your own compute
- Works with Hugging Face, PEFT and DeepSpeed
Cons
- Setup needs technical effort
- No separate docs found on pages checked
- No reference docs checked for integrations
- You must bring and pay for GPUs
Axolotl suits ML engineers who want a configurable, actively updated fine-tuning stack and own their compute. Beginners or teams wanting a hosted, guided service should look elsewhere.
Try Axolotl →Added to scored.tools on
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