Axolotl

Open-source LLM fine-tuning library — single YAML config drives full / LoRA / QLoRA / DPO training across most popular base models.

Active · Verified 2026-10-27 · fresh as of 2026-10-27 · 20 cited facts, 1 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type: YAML-driven LLM fine-tuning repo fix
Tier: T2
Section: Training infrastructure
Created: 2023-04 repo fix
Latest release: v0.21.1 (2026-10-27) registry fix
License: Apache-2.0 repo fix
GitHub: axolotl-ai-cloud/axolotl — ~12.6k stars, last commit 2026-10 repo fix
Pricing: searched not found fix
Funding: Axolotl AI raised pre-seed / undisclosed; primarily OSS-led by Wing Lian; Apache-2.0. repo fix
Freshness: 2026-10-27 fix
Layer: model-infra repo fix
Licensing: open-source repo fix
Deployment locus: local repo fix
Platform vs product: platform repo fix
Developer activity: OpenRank 7.27 (12-mo avg 13.78) · 39 participants — 2026-09 third-party fix
Maintainer health: 1 issues in 90 days · 13 releases in 12 months (latest 2026-10-27) · bus factor 1 — as of 2026-10-31 repo

Optimised for: searched not found

Anti-fit: searched not found

searched not found

searched not found

Popular fine-tuning trainer for Llama/Mistral/Mixtral/Qwen; supports QLoRA, full FT, DPO.

API surface: CLI (axolotl train/fetch/export), YAML config, Docker images
Backend storage: searched not found
Deployment: Self-hosted only — library/CLI with Docker images; runs on third-party GPU clouds
Embedding model: not applicable — not a memory product
Multi-tenancy: searched not found
MCP: searched not found
A2A: searched not found
OpenTelemetry: searched not found

Other training infrastructure in the catalog, ranked by inbound references.

MLflow T1
Databricks-incubated open platform for ML lifecycle — tracking, projects, models, model registry. Standard ML-ops baseline. MLflow 3.0 (2024) added LLM-eval features.
AWS SageMaker T1
AWS's managed ML platform — Studio, Training, HyperPod (large-scale clusters), Endpoints. Industry-standard for enterprise AWS-hosted training.
Weights & Biases T1
Experiment-tracking and ML-ops platform (metrics, artifacts, sweeps, Weave tracing/evals). Acquired by CoreWeave (completed 2025-05-05, ~$1.7B); now part of CoreWeave Forge.
Comet ML T2
ML experiment tracking + LLM evaluation platform (Opik for LLM eval). Comet competes head-to-head with Weights & Biases.
Azure Machine Learning T1
Microsoft Azure's managed ML platform — pipelines, AutoML, Designer, prompt flow (LLM). Heavy integration with Azure OpenAI and Azure AI Studio.
LoRA T3
Microsoft Research's Low-Rank Adapters method — trains rank-r matrices added to attention weights; 10000x parameter reduction. Foundation of all adapter-style fine-tuning.

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2026-10-27
- Latest release first recorded as “v0.21.1 (2026-10-27)” source
- Deployment first recorded as “Self-hosted only — library/CLI with Docker images; runs on third-party GPU clouds” source
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