Axolotl

• Open-source framework for fine-tuning large language models and multimodal AI models.
• Supports LoRA, QLoRA, full fine-tuning, RLHF, GRPO, and reinforcement learning workflows.
• Compatible with popular model families including Llama, Mistral, Gemma, Qwen, Phi, GPT-OSS, and more.
• Configuration-driven training through simple YAML-based configuration files.
• Integrated with the Hugging Face ecosystem for models, datasets, and tokenizers.
• Supports distributed training across multiple GPUs and nodes.
• Leverages modern acceleration technologies such as Flash Attention, Triton, DeepSpeed, FSDP, and xFormers.
• Supports quantization-aware training and memory-efficient fine-tuning techniques.
• Enables custom dataset preparation, preprocessing, and conversational training formats.
• Supports enterprise AI, domain adaptation, instruction tuning, and custom copilot development.
• Suitable for AI assistants, chatbots, coding assistants, agentic AI, and specialized LLM solutions.
• Can be deployed on cloud infrastructure, virtual machines, Kubernetes clusters, and dedicated GPU servers.

Axolotl Usage:

$ sudo su

$ cd /opt

# Activate Axolotl Environment

$ cd ~/axolotl-018

$ source .venv/bin/activate

# Verify Installation

$ python -c "import axolotl; print('Axolotl OK')

$ python -c "import axolotl; print(axolotl.__version__)"

# Display Installed Package Information

$ python -m pip show axolotl

# Verify PyTorch Installation

$ python -c "import torch; print(torch.__version__)"

# Display Axolotl Help

$ axolotl --help

Disclaimer:

Axolotl is an open-source software project developed and maintained by the Axolotl community and its contributors. Axolotl and related trademarks belong to their respective owners. This software is not affiliated with or endorsed by any third-party model provider, cloud provider, operating system vendor, AI service provider, or technology platform referenced for compatibility, deployment, or integration purposes. Users are responsible for ensuring compliance with applicable licenses, model usage terms, security policies, and regulatory requirements when deploying or fine-tuning AI models.