LLaMA-Factory Online

LLaMA-Factory Online is the official cloud-based LLM training and fine-tuning platform, developed in partnership with the renowned open-source project, LLaMA-Factory.

We are dedicated to providing an out-of-the-box, low-code, and end-to-end solution for users seeking to streamline the fine-tuning process or those with limited engineering resources.

How to Use

Start Fine-tuning in 3 Simple Steps：

1. Prepare Data & Models Upload your datasets to the platform seamlessly via SFTP or other supported methods.

2. Configure & Launch Select your base model and configure key parameters in our visual interface. Choose Quick Mode for a fast start or Expert Mode for deep customization. Select the pricing plan that fits your budget and timeline, then launch the task with a single click.

3. Monitor & Evaluate Track training loss and resource usage in real-time with built-in tools like LlamaBoard and TensorBoard. Once finished, quantify the results using Model Evaluation, or verify performance instantly via the Model Chat feature.

LLaMA-Factory Online's

Key Features

• Extensive Model Library (100+ Models) Choose from over 100 mainstream open-source models, including LLaMA, Qwen, DeepSeek, GPT-OSS, and many more.
• Comprehensive Training Methods Supports the full training lifecycle: Pre-training, SFT (Supervised Fine-Tuning), Reward Modeling, and Alignment techniques like PPO, DPO, and KTO.
• Flexible Precision & Quantization Tailor your resource usage with 16-bit Full Fine-tuning, Freeze-tuning, LoRA, and QLoRA (supporting 2/3/4/5/6/8-bit quantization).
• Cutting-edge Optimization Algorithms Stay ahead with integrated state-of-the-art optimization techniques, including GaLore, BAdam, LoRA+, PiSSA, DoRA, and rsLoRA.
• Robust Experiment Tracking Monitor training in real-time with built-in support for LlamaBoard, TensorBoard, WandB, MLflow, and SwanLab.
• High-Efficiency Acceleration Maximize speed with FlashAttention-2 and Unsloth acceleration operators, supporting both Transformers and vLLM inference engines.

Use Cases

• Automated Chinese Essay Grader Built with Qwen3-vl-30B-A3B-Instruct for intelligent scoring and feedback.
• Low-Cost Fine-tuning for Massive MoE Models Efficiently fine-tuning DeepSeek-V3 using KTransformers optimization.
• Medical Imaging Analysis A practical fine-tuning case for healthcare diagnostics using Qwen3-vl-30B-A3B-Instruct.
• Lightweight Smart Home Model Deployed on Qwen3-4B for efficient, edge-based home automation control.
• Legal AI Agent A specialized legal assistant constructed with LightLLM and LlamaIndex.