Unsloth

App in the BluixApps catalog

What it is

Unsloth is the fastest LLM fine-tuning library — custom Triton kernels deliver 2× the speed and 50% less VRAM than vanilla HuggingFace + PEFT. Maintained by Unsloth AI (Daniel Han, ex-Microsoft). The library of choice when budget GPU + speed matter.

For solo developers and AI tinkerers fine-tuning on Colab/consumer GPUs, Unsloth is the canonical choice.

What it's for

  • Lightning-fast LoRA training — 2-5× faster than alternatives
  • Low VRAM training — 7B QLoRA on 8 GB VRAM (vs 24 GB elsewhere)
  • Pre-quantized models — load 4-bit base instantly (no quantize-at-load delay)
  • Native multi-GPU — added Q4 2024
  • Broad model support — Llama, Mistral, Qwen, Phi, Gemma all covered
  • TRL integration — SFT, DPO, ORPO via TRL trainers

Who it's for

  • Solo AI developers fine-tuning on consumer GPUs
  • Researchers running fine-tuning experiments on a budget
  • Startups wanting fastest iteration on training experiments
  • Educators running fine-tuning workshops on shared hardware
  • Hosting providers offering low-cost fine-tuning tier

Why teams pick Unsloth over alternatives

  • Apache 2.0 — fully open
  • Fastest — 2-5× speedup vs standard transformers + PEFT
  • Lowest VRAM — 50% less than alternatives
  • Pre-quantized HF models at unsloth/* namespace (instant load)
  • Active development — frequent releases, Triton kernel optimizations
  • Daniel Han backing — known LLM optimization expert
  • Notebook library — Colab-ready examples for common tasks

Integrations

  • HuggingFace Transformers — base
  • PEFT + TRL — LoRA + SFTTrainer
  • Pre-quantized models at unsloth/* HF namespace
  • Pair with: vLLM/TGI to serve fine-tuned (Unsloth → save_pretrained_merged → load with vLLM)
  • DPO/ORPO support via TRL
  • Continued pretraining for domain-adapt

Notable users & community

  • 24k+ GitHub stars
  • Unsloth AI corporate backing
  • Daniel Han (ex-Microsoft) leads development
  • Featured in popular Colab fine-tuning tutorials
  • Active Discord + Reddit presence

Tips & operations

  • VRAM with Unsloth:
    • 7B QLoRA: 8 GB VRAM minimum (!)
    • 13B QLoRA: 12 GB
    • 70B QLoRA: 48 GB (vs 80 GB standard)
  • Pre-quantized models: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit loads in seconds
  • Code pattern: from FastLanguageModel.from_pretrained()get_peft_model() → TRL SFTTrainer
  • Multi-GPU: enable in newer versions via tensor_parallel
  • Save: model.save_pretrained_merged() to export combined weights
  • vs Axolotl: Unsloth = code/library, Axolotl = config-driven. Use Unsloth for speed-critical custom code; Axolotl for reproducible config workflows
  • vs LLaMA-Factory: Unsloth = library; LLaMA-Factory = visual UI on top

What we ship in BluixApps

  • Docker (pytorch base + Unsloth pip-installed at runtime)
  • JupyterLab pre-installed for interactive notebooks
  • Persistent volumes: workspace, datasets, outputs
  • Port 8889 mapped
  • Pre-set HF_TOKEN environment variable for gated models
  • Install report at /root/bluixapps/unsloth.txt
  • Full Python quick-start example (paste into Jupyter)
  • Notebook library URL for premade Colab-ready examples
  • Pairing notes (vLLM/TGI for serving merged model)
  • GPU pre-flight check via bluixapps_ensure_nvidia_runtime
  • Backup hook covers workspace + outputs
Read this app's deep dive on bluix.app ↗

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