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LLaMA Factory

What it is

LLaMA Factory is a unified, efficient fine-tuning framework that supports over 100 Large Language Models (LLMs). It provides a comprehensive suite of tools, including a web-based UI (LLaMA Board), a command-line interface, and an API, to streamline the entire fine-tuning pipeline from data preparation to model evaluation and deployment.

What problem it solves

Fine-tuning different LLM architectures often requires custom code and deep expertise in various libraries (e.g., PEFT, DeepSpeed, TRT-LLM). LLaMA Factory simplifies this by: - Standardizing the Workflow: Providing a single entry point for fine-tuning diverse models like Llama, Mistral, Qwen, and Baichuan. - Reducing Technical Barrier: Offering a "no-code" web UI for users who prefer graphical interfaces over CLI. - Integrating Best Practices: Built-in support for advanced techniques like GaLore, BAdam, DoRA, and Mixture-of-Experts (MoE) tuning.

Where it fits in the stack

LLaMA Factory sits in the Frameworks/Fine-tuning layer. It is an orchestration framework that coordinates lower-level libraries (PyTorch, Transformers) to perform complex training tasks.

Typical use cases

  • Multi-Model Experimentation: Quickly comparing fine-tuning results across different model families.
  • RLHF & DPO Training: Implementing Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO) workflows.
  • Automated Dataset Conversion: Using its built-in scripts to convert raw data into the required ShareGPT or Alpaca formats.
  • Rapid Prototyping: Using LLaMA Board to tune hyperparameters visually before scaling to CLI-based production runs.

Strengths

  • Massive Model Support: Covers almost all popular open-source LLMs.
  • Web UI (LLaMA Board): Exceptional ease of use for beginners and rapid experimentation.
  • Efficiency: Supports 4-bit/8-bit QLoRA and various memory-saving optimizers (Unsloth, GaLore).
  • Extensible: Easily integrated into larger pipelines via its Python API or CLI.

Limitations

  • Complexity Overhead: For extremely simple one-off tunes, the framework might feel more "heavyweight" than a direct Unsloth script.
  • Dependency Management: Requires a specific environment setup to ensure compatibility between CUDA, PyTorch, and the framework's own requirements.
  • UI Constraints: While powerful, the Web UI might not expose every granular hyperparameter available in the underlying CLI/YAML config.

When to use it

  • When you need to fine-tune a model that isn't yet supported by more specialized tools.
  • When you want to use DPO, PPO, or ORPO without writing custom training loops.
  • When you want a graphical interface to monitor training metrics and chat with the tuned model immediately.

When not to use it

  • If you are doing extremely low-level kernel development.
  • If you only ever tune one specific architecture (e.g., Llama 3) and prefer the absolute maximum speed of Unsloth.
  • If your environment is extremely resource-constrained and you cannot afford the overhead of the framework's management layers.

Getting started

Installation

git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[metrics,bitsandbytes,qwen]"

Launching LLaMA Board (Web UI)

llamafactory-cli webui

Hello-world Fine-tuning (CLI)

Create a train.yaml config: 

model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
stage: sft
do_train: true
dataset: identity,alpaca_gpt4_en
template: llama3
finetuning_type: lora
lora_target: all
output_dir: llama3_lora_sft
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
logging_steps: 10
save_steps: 100
plot_loss: true
overwrite_output_dir: true
Then run: 
llamafactory-cli train train.yaml

  • Fine-tuning Open Models — Standard patterns for model adaptation.
  • Unsloth — Specialized backend for maximum fine-tuning speed.
  • axolotl — Configuration-driven alternative for advanced users.
  • distilabel — For generating the synthetic datasets used in LLaMA Factory.
  • vLLM — For serving models fine-tuned with LLaMA Factory.
  • Qwen — Frequently tuned model family within this framework.
  • Weights & Biases — Integrated for experiment tracking.

Sources / references

Contribution Metadata

  • Last reviewed: 2026-05-18
  • Confidence: high