Axolotl

App in the BluixApps catalog

What it is

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.

What it's for

  • LoRA / QLoRA fine-tuning — adapter training with low VRAM
  • 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

  • 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

Why teams pick Axolotl over 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

  • 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

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

Tips & operations

  • VRAM budgets:
    • 7B QLoRA: 16 GB
    • 7B LoRA: 24 GB
    • 13B QLoRA: 24 GB
    • 70B QLoRA: 80+ GB (or 2× 80 GB)
  • Dataset format: JSONL with {instruction, input, output} (alpaca) or {conversations: [...]} (sharegpt)
  • Config-driven: start with examples/ configs, modify
  • Multi-GPU: accelerate launch --num-processes N -m axolotl.cli.train config.yml
  • DeepSpeed: enable ZeRO 2 or 3 for 70B+ models
  • Monitoring: WandB integration for loss curves
  • Output: LoRA adapter file + merged weights option

What we ship in BluixApps

  • 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)
Read this app's deep dive on bluix.app ↗

Get this app — pick a BluixApps plan

Same catalog. Scaling tenant isolation, white-label and support tier.

TierTenantsCatalogSupportWhite-labelMonthly
Stacks119 curated stacksStandard$19/moDetailDeploy
Starter10Full catalogStandard+$15–25/mo$49/moDetailDeploy
Pro25Full catalogPriority bugfix+$15–25/mo$149/moDetailDeploy
Growth100Full catalogPriority bugfix+$15–25/mo$349/moDetailDeploy
Scale500Full catalog7-day window+$15–25/mo$799/moDetailDeploy
EnterpriseUnlimitedFull catalogPriority 7-dayBundled$1,499/moDetailDeploy

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