axolotl
SkillAI & modelsStreamlined fine-tuning framework for LLMs. Supports full fine-tune, LoRA, QLoRA, FSDP, DeepSpeed, and multi-GPU. YAML config driven. Works with Llama, Mistral, Qwen, DeepSeek, and hundreds of HF models.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the axolotl skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/axolotl/SKILL.md and read by ahel’s review.
Overview
Axolotl is a fine-tuning framework supporting SFT, QLoRA, LoRA, full fine-tuning, DPO, and multimodal tuning for 100+ models (Llama, Mistral, Qwen, Gemma, DeepSeek). YAML-driven config avoids boilerplate. Supports multi-GPU, FSDP, DeepSpeed, and flash attention.
Installation
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl
uv pip install -e .
Basic Config
# config.yml
base_model: Qwen/Qwen2.5-1.5B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
output_dir: ./output
# LoRA
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
# Training
sequence_len: 2048
micro_batch_size: 2
gradient_accumulation_steps: 4
num_epochs: 3
learning_rate: 2e-5
optimizer: adamw_bnb_8bit
Run
accelerate launch -m axolotl.cli.train config.yml
Inference
python -m axolotl.cli.inference --lora_model_dir ./output --base_model Qwen/Qwen2.5-1.5B-Instruct
References
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
axolotl-mkurman- Source
- github.com/mkurman/zorai