Axolotl

axolotl.ai · Training & Fine tuning

Open source

#7 in Training & Fine tuning

Config driven tool for fine tuning open models across many architectures and training methods.

#fine-tuning#config#multi-gpu#post-training

Axolotl is one of 9 training and fine tuning tools on Axiomi. See all training and fine tuning tools or how we check facts.

// guides

Guides that cover Axolotl

LLM Distillation vs Quantization: Key Differences
LLM distillation vs quantization: distillation trains a smaller student model, quantization stores the same weights in fewer bits. When to use each, or both.
How to Fine Tune an LLM for Code Generation
How to fine tune an LLM for code generation: pick a coder base model, build an instruction or FIM dataset, train a LoRA with TRL or Unsloth, test with pass@k.
LoRA vs QLoRA vs DoRA: Which Fine Tuning Method?
QLoRA vs LoRA explained, plus DoRA and full fine tuning: how each works, real VRAM figures, quality tradeoffs, recommended settings, and when to choose which.
How to Fine Tune an LLM Locally in 2026
Fine tune an LLM locally, step by step: when it pays off, picking a base model, formatting data, Unsloth vs Axolotl vs TRL vs mlx-lm, training and GGUF export.
GRPO vs PPO: How LLM RL Training Differs
GRPO vs PPO explained simply: how each trains LLMs with rewards, why GRPO drops the critic, what DAPO and Dr. GRPO changed, and when to pick GRPO, PPO or DPO.

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