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LLaMA-Factory
Zero-code fine-tuning platform for diverse large language models
LLaMA Factory lets you fine-tune over 100 LLMs via a CLI or web UI, supporting LoRA, QLoRA, advanced optimizers, multimodal data, and OpenAI-style API deployment without writing code.
Overview
Overview
LLaMA Factory is a zero‑code platform that enables developers, researchers, and ML engineers to fine‑tune more than a hundred large language models—including LLaMA, Mistral, Qwen, Gemma, and multimodal variants—through a simple CLI or a web‑based GUI. The system bundles a wide range of training approaches such as LoRA, QLoRA (2‑8‑bit), full‑parameter tuning, and advanced optimizers like GaLore, OFT, and DoRA, allowing users to experiment with supervised, reward‑modeling, and PPO/DPO pipelines without writing custom scripts.
Deployment & Integration
Trained models can be exported to an OpenAI‑compatible endpoint powered by vLLM or SGLang, or served locally via Gradio. The framework supports Docker builds, cloud‑GPU deals (e.g., Alaya NeW), and integrates with experiment trackers such as TensorBoard, Wandb, MLflow, and SwanLab. Built‑in tricks like FlashAttention‑2 and Liger Kernel further accelerate training, while the extensive logging and monitoring tools keep the workflow transparent from data preparation to inference.
Highlights
Supports 100+ models including LLaMA, Mistral, Qwen, Gemma, and multimodal variants
Zero-code fine-tuning via CLI and web UI with LoRA, QLoRA, and 2‑8‑bit quantization
Built-in advanced optimizers and algorithms such as GaLore, OFT, and DoRA
Ready-to-deploy OpenAI-compatible API using vLLM or SGLang
Pros
- Broad model and method coverage reduces tool switching
- No-code interface accelerates prototyping for non-engineers
- Quantization options enable training on limited GPU memory
- Integrated monitoring (TensorBoard, Wandb, SwanLab) simplifies experiment tracking
Considerations
- Feature-rich UI may have a learning curve for beginners
- Rapid model updates can outpace documentation
- Heavy reliance on GPU resources for large models
- Advanced algorithms may require expert tuning to benefit
Managed products teams compare with
When teams consider LLaMA-Factory, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- ML engineers needing quick fine-tuning of many LLMs
- Researchers prototyping multimodal tasks without writing training scripts
- Teams deploying custom models behind an OpenAI-style endpoint
- Organizations leveraging cloud GPU deals (e.g., Alaya NeW) for cost-effective scaling
Not ideal when
- Environments lacking GPU acceleration or sufficient VRAM
- Users requiring strict reproducibility with minimal external dependencies
- Projects that need highly customized training loops beyond provided methods
- Teams preferring a fully managed SaaS solution over self-hosted deployment
How teams use it
Domain-specific chatbot for mental health support
Fine-tuned a LLaMA-3 model on curated counseling data, deployed via OpenAI-compatible API, delivering empathetic responses in production.
Visual document extraction for banking
Trained Qwen2-VL on scanned forms, enabling image understanding and data extraction through a Gradio UI.
Reinforcement learning for code generation
Applied PPO and DPO on CodeLlama using the built-in RL pipeline, improving generation quality measured by automated tests.
Low-memory fine-tuning on consumer GPUs
Utilized 4-bit QLoRA with LoRA+ to fine-tune a 7B model on a single RTX 3060, achieving comparable performance to full-precision training.
Tech snapshot
Python100%
Dockerfile1%
Makefile1%
Frequently asked questions
Do I need programming experience to use LLaMA Factory?
No, the platform provides a zero-code CLI and a web UI that handle data preparation, training, and deployment without writing Python scripts.
Which hardware is required for fine-tuning large models?
GPU memory is the main constraint; quantization (2‑8-bit) and LoRA allow training 7‑30B models on 16‑32 GB GPUs, while larger models need multi-GPU or cloud resources.
How can I monitor training progress?
LLaMA Factory integrates with TensorBoard, Wandb, MLflow, and SwanLab, and also offers the LlamaBoard dashboard for real-time metrics.
Can I serve the fine-tuned model as an API?
Yes, the tool can launch an OpenAI-style endpoint using vLLM or SGLang workers, and also provides a Gradio UI for quick testing.
Is there support for multimodal data?
The framework includes supervised fine-tuning for image, video, and audio inputs, with models like LLaVA, Qwen2-VL, and InternVL3.
Project at a glance
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LicenseApache-2.0
Repo age2 years old
Last commityesterday
Primary languagePython
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