LLaMA Factory is one of the most practical open-source tools for LLM fine-tuning, helping developers train and customize models with less engineering overhead. In this guide, FPT AI Factory explains what LLaMA Factory is, how to install it, how LoRA and QLoRA workflows work, and when dedicated GPU infrastructure becomes valuable.
1. What Is LLaMA Factory?
LLaMA Factory is an open-source framework for fine-tuning, evaluating, and exporting large language models (LLMs) and vision-language models (VLMs). It is designed to make model customization more accessible by reducing the amount of custom code required to prepare, train, and deploy fine-tuned models.
Instead of building a fine-tuning pipeline from scratch, developers can use LLaMA Factory to configure training workflows through command-line tools or a built-in web interface. This makes it useful for teams that want to experiment with open-source models, adapt models to domain-specific data, or compare different fine-tuning methods more efficiently. LLaMA Factory is commonly used for:
- Supervised fine-tuning
- LoRA and QLoRA fine-tuning
- Full-parameter fine-tuning
- Preference optimization workflows
- Model evaluation and export
- Multimodal model fine-tuning
The official LLaMA Factory GitHub repository describes it as a unified framework for efficient fine-tuning across 100+ language and vision-language models. It supports model families such as LLaMA, LLaVA, Mistral, Qwen, DeepSeek, Gemma, GLM, and Phi, along with training methods such as pretraining, supervised fine-tuning, reward modeling, PPO, DPO, KTO, and ORPO.
Vision language models
2. Why LLaMA Factory Is Popular
LLaMA Factory has become popular because it combines flexible fine-tuning capabilities with a relatively accessible user experience. For many AI teams, the main challenge is not only training a model, but also managing datasets, choosing fine-tuning methods, configuring parameters, evaluating outputs, and exporting model weights consistently.
2.1. Easy Web Interface
LLaMA Factory provides a web UI that helps users configure training workflows without writing every command manually. This is useful for teams that want a faster way to test model fine-tuning setups before moving into more advanced command-line or production workflows.
Through the web interface, users can typically:
- Select a base model
- Register or upload a dataset
- Choose a training method
- Configure training parameters
- Start training
- Evaluate results
- Export the final model
This makes a LLaMA Factory tutorial easier to follow, especially for developers who are new to LLM fine-tuning.
2.2. Strong Fine-Tuning Support
LLaMA Factory supports a wide range of fine-tuning and alignment methods. This allows teams to choose the right method based on model size, dataset type, GPU capacity, and training objective. Common methods include:
- LoRA: Parameter-efficient fine-tuning for adapting models with lower compute cost
- QLoRA: Quantized LoRA fine-tuning for larger models with lower VRAM requirements
- Full fine-tuning: Updates all model parameters for deeper customization
- DPO: Preference optimization based on chosen and rejected responses
- RLHF / PPO: Human-feedback optimization for alignment workflows
- ORPO / KTO: Alternative preference optimization methods for instruction-following models
LLaMA Factory’s example repository includes workflows for LoRA fine-tuning, QLoRA fine-tuning, full-parameter fine-tuning, LoRA adapter merging, quantization, inference, and OpenAI-style API serving.
2.3. Broad Model Compatibility
Another reason LLaMA Factory is widely used is its broad compatibility with open-source model families. Instead of being tied to one model ecosystem, developers can experiment with multiple base models and compare their performance across different tasks.
This is especially helpful for teams evaluating which model family works best for a specific use case, such as customer support, internal copilots, coding assistants, domain-specific Q&A, or multimodal applications.
2.4. Efficient GPU Training
LLaMA Factory supports several techniques that help improve training efficiency and reduce hardware pressure. This is important because LLM fine-tuning can quickly become expensive when model size, sequence length, dataset size, or experiment volume increases.
Efficiency-related features include:
- Quantization
- Mixed precision training
- LoRA and QLoRA
- Flash Attention support
- DeepSpeed integration
- Multi-GPU training
These features help teams fine-tune models more efficiently, but they still require reliable GPU infrastructure when workloads move beyond small experiments.
Users can launch training jobs directly from a browser
3. Which Models and Training Methods Does LLaMA Factory Support?
LLaMA Factory supports many open-source LLMs and VLMs, making it useful for both text-only and multimodal fine-tuning workflows. Before choosing a model or training method, teams should consider the target use case, dataset size, expected output quality, available VRAM, and deployment requirements.
| Method | Best use case | Infrastructure consideration |
| LoRA | Low-cost adapter tuning for chatbots, copilots, or domain adaptation | Suitable when teams need efficient training without updating all model weights |
| QLoRA | Fine-tuning larger models with limited GPU memory | Useful when VRAM is constrained but model size is relatively large |
| Full fine-tuning | Deep model customization where all parameters are updated | Requires stronger GPU resources and more careful training control |
| DPO | Preference alignment using selected and rejected responses | Useful for improving response quality and alignment |
| RLHF / PPO | Human-feedback optimization for advanced alignment workflows | More complex and resource-intensive than standard supervised fine-tuning |
LLaMA Factory is especially known for LLaMA Factory QLoRA and LLaMA Factory LoRA training workflows because they reduce memory requirements while still enabling effective model adaptation. This makes the framework practical for teams that want to fine-tune open-source LLMs without building a full custom training stack.
4. How to Install LLaMA Factory
Users searching for LLaMA Factory install typically want a fast setup path for local testing or development. The common installation workflow starts from the official GitHub repository.
| git clone https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory pip install -e . |
After installation, users can launch the web UI with:
| llamafactory-cli webui |
Before installing LLaMA Factory, teams should check the technical environment carefully. LLM fine-tuning depends heavily on the compatibility between Python, CUDA, GPU drivers, dependencies, and available GPU memory.
Recommended checks include:
- Python version compatibility
- CUDA and GPU driver readiness
- Available VRAM for the selected model
- Storage for datasets, checkpoints, and exported weights
- Training method requirements, especially for LoRA or QLoRA
- Whether the setup is intended for local testing or scalable training
For the most accurate setup commands, users should always refer to the official LLaMA Factory GitHub repository, because installation steps and supported dependencies may change over time.
Python version compatibility
5.How to Use LLaMA Factory for LoRA and QLoRA Training
A typical LLaMA Factory tutorial for LoRA or QLoRA training follows a structured workflow: choose a base model, prepare a dataset, select a tuning method, configure training parameters, run training, evaluate outputs, and export the final model.
Basic workflow:
- Choose a base model
- Upload or register a dataset
- Select LoRA or QLoRA
- Configure learning rate, epochs, batch size, and sequence length
- Start the training job
- Evaluate model outputs
- Export model weights or adapters
LoRA is often suitable when teams need efficient model adaptation without updating all parameters. It is commonly used for domain adaptation, internal copilots, chatbots, and task-specific response improvement.
QLoRA is useful when teams want to fine-tune larger models with lower GPU memory usage. By combining quantization with LoRA-style adaptation, QLoRA can make large-model fine-tuning more accessible when VRAM is limited.
For small experiments, teams may run LLaMA Factory in a local or single-GPU environment. However, as model size, dataset volume, and experiment frequency increase, dedicated GPU infrastructure becomes more important.
FPT AI Factory can support this stage with GPU-based infrastructure options for AI teams running fine-tuning workloads. GPU Virtual Machine is suitable for teams that need flexible GPU compute for model training and experimentation, while GPU Container can support containerized training environments with more consistent setup and portability. For teams moving from experiments to production workflows, this infrastructure can help reduce setup complexity and improve training scalability.
In short, LLaMA Factory is one of the most accessible ways to fine-tune open-source LLMs using LoRA, QLoRA, and modern training workflows. For teams moving beyond experiments into scalable AI operations, FPT AI Factory can provide GPU infrastructure suited for enterprise model training and deployment.
Contact information
- Hotline: 1900 638 399
- Email: support@fptcloud.com
