Llamafactory

App in the BluixApps catalog

What it is

LLaMA-Factory is a visual web UI for LLM training — config-builder for SFT, DPO, ORPO, PPO, GRPO, KTO across 100+ models (Llama, Mistral, Qwen, ChatGLM, Phi, Gemma, etc.). No-code fine-tuning made accessible, with integrated dataset conversion, training monitoring, and export tools.

The easiest entry to LLM fine-tuning — Axolotl's UI counterpart.

What it's for

  • Visual training configuration — no YAML editing
  • Multi-stage training — SFT, DPO, ORPO, PPO, GRPO, KTO
  • Dataset conversion — built-in format adapters
  • Training monitoring — loss curves + eval metrics live
  • Model export — merged weights or adapter files
  • 100+ models supported — broadest coverage in OSS training space

Who it's for

  • Non-engineers learning LLM fine-tuning
  • AI agency teams offering managed fine-tuning to clients
  • Educators teaching LLM training fundamentals
  • Researchers needing rapid iteration
  • Hosting providers offering visual fine-tuning tier

Why teams pick LLaMA-Factory over alternatives

  • Apache 2.0 — fully open
  • Easiest UX for LLM training in OSS space
  • Broadest model coverage — 100+ models supported out-of-box
  • All major training stages — SFT + alignment (DPO/ORPO) + RL (PPO/GRPO)
  • Live monitoring — loss curves, eval scores
  • Visual everything — model selection, dataset preview, hyperparameter tuning, export
  • Active community — 30k+ GitHub stars

Integrations

  • HuggingFace Transformers + Datasets + PEFT + TRL underneath
  • DeepSpeed for multi-GPU
  • bitsandbytes for quantization
  • WandB / TensorBoard for monitoring
  • Pair with: vLLM/TGI to serve fine-tuned model

Notable users & community

  • 38k+ GitHub stars (one of most popular LLM training tools)
  • hiyouga + extensive contributor base
  • Used widely in academic + commercial fine-tuning
  • Multiple tutorials + courses
  • Active Discord + GitHub community

Tips & operations

  • VRAM:
    • 7B QLoRA: 16 GB
    • 7B LoRA: 24 GB
    • 13B QLoRA: 24 GB
    • 70B QLoRA: 80+ GB
  • Training stages:
    • SFT (Supervised Fine-Tuning) — most common
    • DPO / ORPO / SimPO / CPO — preference alignment
    • PPO / GRPO — reinforcement learning (RLHF / R1-style)
    • KTO — Kahneman-Tversky Optimization
    • Pre-training — continued pretraining on new domain
  • Dataset preview: built-in viewer for sharegpt / alpaca / openai format
  • Multi-GPU: enable in advanced tab; DeepSpeed ZeRO 2 or 3
  • Export: merged model OR LoRA adapter
  • vs Axolotl: LLaMA-Factory = visual UI; Axolotl = config-driven YAML
  • vs Unsloth: LLaMA-Factory = UI; Unsloth = code library (faster but no UI)

What we ship in BluixApps

  • Cloned hiyouga/LLaMA-Factory repo
  • pytorch CUDA 12.4 devel base + git pre-installed
  • Pip install with [torch,metrics] extras + gradio
  • llamafactory-cli webui launcher (Gradio server on port 7860)
  • Persistent volumes: repo, data (training datasets), saves (output), cache (HF)
  • Port 7881 mapped
  • HF_TOKEN environment variable for gated models
  • Install report at /root/bluixapps/llamafactory.txt
  • Training stage guidance
  • Quick-start workflow documentation
  • LLaMA-Factory vs Axolotl vs Unsloth comparison
  • Pairing notes (vLLM/TGI for serving)
  • GPU pre-flight check via bluixapps_ensure_nvidia_runtime
  • Backup hook covers data + saves
Read this app's deep dive on bluix.app ↗

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

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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
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