-->

⏱️ Estimated Reading Time: 8 minutes

TL;DR LLaMA Factory is an open-source framework that enables fine-tuning of over 100 cutting-edge large language and multimodal models including Llama 3, Qwen 3, DeepSeek, and Mistral with a single line of code. It integrates the latest techniques such as LoRA/QLoRA, FSDP, Flash‑Attention 2, vLLM, and PPO/DPO/KTO/ORPO, while providing both CLI and Gradio-based Web UI to significantly bridge the gap between research and production environments.

What is LLaMA Factory?

LLaMA Factory is a PyTorch-based open-source LLM fine-tuning framework that offers the following characteristics:

  • 100+ Model Support — Llama 3/4, Qwen 3, DeepSeek, Yi, Gemma 3, Mixtral‑MoE, LLaVA‑NeXT, and more
  • Diverse Training Approaches — Full/Freeze/LoRA/QLoRA, (multimodal) SFT, Reward Model, PPO, DPO, KTO, ORPO, SimPO, etc.
  • Efficiency Optimization — FlashAttention‑2, Unsloth, GaLore, BAdam, APOLLO, DoRA, LongLoRA, Mixture‑of‑Depths, PiSSA
  • User-Friendly Interface — Single command llamafactory-cli train … or Gradio-based LLaMA Board GUI
  • Deployment-Ready — OpenAI‑style REST API (/v1/chat/completions), vLLM·SGLang backends, Docker & K8s examples

Already utilized in Amazon SageMaker HyperPod, NVIDIA RTX AI Toolkit, Aliyun PAI‑DSW, and other platforms.

Why Should You Use LLaMA Factory?

Bridging the Research-Production Gap

The framework handles the entire pipeline from data preprocessing to training, evaluation, inference, and deployment within a single framework, significantly reducing MLOps complexity.

Flexible Resource Utilization

Through combinations of LoRA·QLoRA·FSDP, even 70B models can be trained on 2×24GB GPUs. It also supports Ascend NPU·AMD ROCm environments.

Day-N Support for Latest Algorithms

Major models and algorithms are integrated within D+1 days of paper publication, such as Llama 4, GLM‑4, Muon, and GaLore.

Key Features at a Glance

  • CLI: Provides complete workflow through four commands: train/chat/export/api
  • Web UI (LLaMA Board): Visualizes everything from dataset upload to hyperparameter settings and real-time logs
  • Docker Stack: Provides Compose files for CUDA·NPU·ROCm environments → identical environment anywhere, local or cloud
  • Large Dataset Bundle: Pre-defined 80+ public SFT/RLHF/multimodal datasets
  • Experiment Tracker: TensorBoard, W&B, SwanLab, MLflow integration

Quick Model Support Snapshot

The framework supports an extensive range of models across different families:

Meta: Llama 2·3·4 (7B → 402B parameters) Alibaba: Qwen 1‑3 / Qwen 2‑VL (0.5B → 235B parameters) Mistral: Mistral / Mixtral‑MoE (7B → 8×22B parameters) Google: Gemma 3 / PaliGemma 2 (1B → 28B parameters) OpenGVLab: InternVL 3‑8B (8B parameters)

Additional support includes DeepSeek, Yi, Phi 4, MiniCPM‑V, and many others. The complete list and template mappings can be found in the constants.py file.

3-Minute Quick Start

The framework provides an incredibly simple getting-started experience:

# 1) LoRA‑SFT example (Llama 3‑8B‑Instruct)
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml

# 2) Interactive inference
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml

# 3) LoRA adapter merging & checkpoint export
llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml

Gradio GUI Execution

llamafactory-cli webui  # Access localhost:7860

Docker Compose (CUDA)

cd docker/docker-cuda && docker compose up -d

Practical Implementation Tips

The framework offers several optimization strategies for enhanced performance:

FlashAttention‑2: Setting flash_attn: fa2 provides over 30% speed improvement on A100 GPUs.

QLoRA: Combining 4‑bit quantization with LoRA enables fine-tuning 8B models with just 6GB vRAM.

NEFTune: Using neftune_noise_alpha: 5 for regularization improves convergence speed.

vLLM Serving: Setting infer_backend: vllm increases average TPS by 2.7×.

Advanced Training Methodologies

Supervised Fine-Tuning (SFT)

The framework excels at adapting pre-trained models to specific tasks and domains through supervised fine-tuning. This process involves training models on carefully curated instruction-response pairs to improve their ability to follow human instructions and generate appropriate responses in specific contexts.

Reinforcement Learning from Human Feedback (RLHF)

LLaMA Factory supports comprehensive RLHF pipelines including reward model training and policy optimization through various algorithms. The framework implements multiple preference optimization techniques such as PPO (Proximal Policy Optimization), DPO (Direct Preference Optimization), KTO (Kahneman-Tversky Optimization), and ORPO (Odds Ratio Preference Optimization), enabling fine-grained control over model behavior alignment with human preferences.

Parameter-Efficient Fine-Tuning

The framework provides extensive support for parameter-efficient methods that dramatically reduce computational requirements while maintaining performance. These techniques include Low-Rank Adaptation (LoRA) and its variants, which decompose weight updates into low-rank matrices, significantly reducing the number of trainable parameters while preserving model capabilities.

Multimodal Training Capabilities

Beyond text-only models, LLaMA Factory supports training of vision-language models that can process and understand both textual and visual inputs. This capability enables development of sophisticated AI systems that can perform tasks requiring understanding of images, documents, charts, and other visual content alongside natural language processing.

Production Deployment Strategies

API Server Integration

The framework provides seamless integration with production environments through OpenAI-compatible API endpoints. This standardized interface ensures easy integration with existing applications and services that already use OpenAI’s API format, reducing migration complexity and enabling rapid deployment of custom fine-tuned models.

Scalable Inference Solutions

LLaMA Factory integrates with high-performance inference engines including vLLM and SGLang, which provide optimized serving capabilities for production workloads. These engines implement advanced techniques such as continuous batching, memory-efficient attention mechanisms, and dynamic request scheduling to maximize throughput and minimize latency.

Container Orchestration

The framework includes comprehensive Docker and Kubernetes deployment configurations, enabling seamless scaling across different infrastructure environments. These containerized deployments ensure consistent behavior across development, staging, and production environments while providing the flexibility to scale resources based on demand.

Quality Assurance and Monitoring

Evaluation Framework Integration

LLaMA Factory incorporates robust evaluation capabilities that enable systematic assessment of model performance across various metrics and benchmarks. This includes support for both automatic metrics and human evaluation protocols, ensuring comprehensive quality assessment throughout the development lifecycle.

Experiment Tracking and Reproducibility

The framework provides extensive logging and experiment tracking capabilities through integration with popular MLOps platforms. This enables systematic comparison of different training configurations, hyperparameter settings, and model variants, facilitating data-driven decision making in model development processes.

Community and Ecosystem

Open Source Collaboration

LLaMA Factory benefits from active community contributions and maintains compatibility with the broader ecosystem of machine learning tools and frameworks. The project welcomes contributions from researchers and practitioners, fostering innovation and rapid adoption of new techniques and methodologies.

Educational Resources

The framework provides comprehensive documentation, tutorials, and example configurations that enable both beginners and experts to effectively utilize its capabilities. These resources cover everything from basic fine-tuning workflows to advanced optimization techniques and production deployment strategies.

Framework and model licenses must comply with Apache‑2.0 and individual model-specific licenses. Please refer to the repository LICENSE file and model cards for detailed information.

참고