Skip to main content
⌘K
All Categories
All ToolsLLM Inference EnginesLLM ModelsVector DatabasesAgent FrameworksChat InterfacesRAG & Document ProcessingSpeech to TextText to SpeechImage GenerationFine-tuning & TrainingMonitoring & ObservabilityPrivacy & SecurityEmbedding ModelsDeploymentAgent & Workflow AutomationVideo GenerationVision & MultimodalCode AssistantsData Utilities

Axolotl

YAML-driven fine-tuning framework supporting QLoRA, DPO, and ORPO with flash attention for efficient model training.

View on GitHub Official site
Fine Tuning Training Apache 2.0 Medium setup 9,000 stars

Overview

Plain English

YAML-driven fine-tuning framework supporting QLoRA, DPO, and ORPO with flash attention for efficient model training.

Technical

YAML-driven fine-tuning framework supporting QLoRA, DPO, and ORPO with flash attention for efficient model training.

Technical scorecard

License Apache 2.0
Commercial use Yes
OpenAI-compatible API No
REST API No
Fine-tuning support No
Quantization support No
Docker available No
GUI / no-code available No
Telemetry None
Offline after setup Yes

Data & Privacy

Does it send data online?

After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.

Does it store history?

Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.

License checks?

Commercial use is marked as allowed or likely allowed by the listed license.

Telemetry?

None

Last verified: May 14, 2026. Maintainer verification should be treated as directory guidance, not legal advice.

Setup & Installation

Medium

A developer can usually get this running with standard docs.

Prerequisites

Python, Docker, Bare Metal 

# Start with the official project documentation
# https://github.com/axolotl-ai-cloud/axolotl

Hardware Requirements

RAM16 GB minimum / 32 GB recommended
Hardware tagsNVIDIA GPU (CUDA)
Model formatsNot specified
Primary languagePython

Works Well With

DeepSeek V3 (DeepSeek AI) Open-weight Mixture-of-Experts model with MIT license, strong reasoning, and efficient inference for offline use. Ray Serve Scalable model serving built on Ray with dynamic batching, GPU sharing, and multi-model composition for AI pipelines. Llama 3 (Meta) The official Meta Llama 3 GitHub site. Qwen3 (Alibaba) Qwen3 is the large language model series developed by Qwen team, Alibaba Cloud.
Open suggested stack
Share on X Share on LinkedIn