LoRA / QLoRA fine-tuning
adapter training with low VRAM
// official site: github.com ↗
LLM FINE-TUNING · PRO TIER
Axolotlpro
Axolotl is a config-driven LLM fine-tuning toolkit by OpenAccess AI Collective — supports LoRA/QLoRA/full-parameter training, multi-GPU via DeepSpeed/FSDP, and broad model coverage (Llama, Mistral, Qwen, Gemma, ChatGLM, Phi, etc.). The industry standard for production LLM fine-tuning.
// What it is
A closer look.
Axolotl is a config-driven LLM fine-tuning toolkit by OpenAccess AI Collective — supports LoRA/QLoRA/full-parameter training, multi-GPU via DeepSpeed/FSDP, and broad model coverage (Llama, Mistral, Qwen, Gemma, ChatGLM, Phi, etc.). The industry standard for production LLM fine-tuning.
When AI startups fine-tune their own LLMs, Axolotl is the most common choice.
// Use cases
What it's for.
Concrete scenarios where teams pick Axolotl over the SaaS alternative.
Full-parameter SFT
supervised fine-tuning end-to-end
DPO / ORPO / KTO
preference alignment / RLHF alternatives
Continued pretraining
extend base model on new domain
Multi-GPU training
DeepSpeed ZeRO 1/2/3, FSDP
Dataset prep
alpaca, sharegpt, jsonl formats supported
// Who it's for
Built for these teams.
If your team profile matches one of these, Axolotl is a strong fit out of the box.
AI startups
fine-tuning custom LLMs for their domain
Research teams
publishing fine-tuned model variants
Enterprises
training internal-data-aware models
Compliance-conscious teams
keeping training data on-prem
Hosting providers
offering managed fine-tuning to clients
// Differentiators
Why teams pick Axolotl.
When evaluating self-hosted options for this category, here are the dimensions on which Axolotl consistently lands above the alternatives.
- ✓Apache 2.0 — fully open
- ✓Config-driven — YAML files define entire training run
- ✓Broad model support — every major HF base model works
- ✓DeepSpeed integration — multi-GPU production-grade
- ✓Active maintenance — OpenAccess collective + contributors
- ✓Community recipes — example configs for popular use cases
- ✓Used by major fine-tunes — Hermes, OpenHermes, etc. published with Axolotl
// Integrations
Connects to.
The stack you'll plug Axolotl into — services, protocols, and adjacent apps in the BluixApps catalog.
◇
HuggingFace Transformers
base library
◈
PEFT
LoRA / QLoRA / IA3 / Prefix tuning
◆
TRL
SFTTrainer, DPOTrainer, ORPOTrainer
▣
bitsandbytes
4/8-bit quantization
▦
DeepSpeed
ZeRO optimization for multi-GPU
▩
WandB / TensorBoard
training monitoring
▼
Pair with
vLLM/TGI to serve fine-tuned model post-training
// Adoption & deployment
Notable users & community
- 9k+ GitHub stars
- OpenAccess AI Collective backing
- Used to train NousResearch Hermes, Teknium models, OpenHermes series
- Many published HF fine-tunes credit Axolotl in their model cards
- Active Discord with researchers + practitioners
What we ship
- Docker (axolotlai/axolotl:main-latest)
- JupyterLab pre-installed for interactive training
- Persistent volumes: workspace, datasets, outputs
- Port 8888 mapped (Jupyter lab interface)
- Pre-set HF_TOKEN environment variable for gated models
- Install report at
/root/bluixapps/axolotl.txt - Quick-start commands for LoRA training
- Multi-GPU launch example
- Pairing notes (vLLM/TGI for serving fine-tuned)
- GPU pre-flight check via
bluixapps_ensure_nvidia_runtime - Backup hook covers workspace + outputs (datasets opt-in)
// Tips & operations
Run it properly.
Operational guidance from running this in production — what to lock down, what surprises people.
// PERFORMANCE
VRAM budgets
// SECURITY
Dataset format
JSONL with {instruction, input, output} (alpaca) or {conversations: [...]} (sharegpt)
// OPERATIONS
Config-driven
start with examples/ configs, modify
// RELIABILITY
Multi-GPU
accelerate launch --num-processes N -m axolotl.cli.train config.yml
// DEPLOYMENT
DeepSpeed
enable ZeRO 2 or 3 for 70B+ models
// SCALING
Monitoring
WandB integration for loss curves
// MAINTENANCE
Output
LoRA adapter file + merged weights option
32768
// min ram (MB)
100
// min disk (GB)
8888
// access port
http
// protocol
pro
// bluixapps tier
// Alternatives in LLM fine-tuning
Compare with
Project resources
Official sitegithub.com ↗