Visual training configuration
no YAML editing
// official site: github.com ↗
LLM FINE-TUNING · PRO TIER
LLaMA-Factorypro
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.
// What it is
A closer look.
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.
// Use cases
What it's for.
Concrete scenarios where teams pick LLaMA-Factory over the SaaS alternative.
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
Built for these teams.
If your team profile matches one of these, LLaMA-Factory is a strong fit out of the box.
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
// Differentiators
Why teams pick LLaMA-Factory.
When evaluating self-hosted options for this category, here are the dimensions on which LLaMA-Factory consistently lands above the 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
Connects to.
The stack you'll plug LLaMA-Factory into — services, protocols, and adjacent apps in the BluixApps catalog.
◇
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
// Adoption & deployment
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
What we ship
- Cloned
hiyouga/LLaMA-Factoryrepo - pytorch CUDA 12.4 devel base + git pre-installed
- Pip install with
[torch,metrics]extras + gradio llamafactory-cli webuilauncher (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
// Tips & operations
Run it properly.
Operational guidance from running this in production — what to lock down, what surprises people.
// PERFORMANCE
VRAM
// SECURITY
Training stages
// OPERATIONS
Dataset preview
built-in viewer for sharegpt / alpaca / openai format
// RELIABILITY
Multi-GPU
enable in advanced tab; DeepSpeed ZeRO 2 or 3
// DEPLOYMENT
Export
merged model OR LoRA adapter
// SCALING
vs Axolotl
LLaMA-Factory = visual UI; Axolotl = config-driven YAML
// MAINTENANCE
vs Unsloth
LLaMA-Factory = UI; Unsloth = code library (faster but no UI)
32768
// min ram (MB)
100
// min disk (GB)
7881
// access port
http
// protocol
pro
// bluixapps tier
// Alternatives in LLM fine-tuning
Compare with
Project resources
Official sitegithub.com ↗