LLaMA-Factory Online: No-Code Model Fine-Tuning Platform 135

LLaMA-Factory Online is an online platform officially partnered with the star open-source project LLaMA-Factory, specifically designed to help users train and fine-tune large models. It mainly serves users who want to fine-tune models but have relatively weak programming capabilities, providing cloud services that require no complex operations and cover the entire workflow. Users don't need to worry about underlying resource allocation and configuration—they can simply open a webpage to access high-performance GPU compute with a single click, handling everything from data preparation to building a customized model. LLaMA-Factory Online allows teams to focus on business and technology, boosting development efficiency.

LLaMA-Factory Online Features

• Wide Model Selection: LLaMA-Factory Online offers over 100 mainstream large models to choose from, such as LLaMA, Qwen, DeepSeek, GPT-OSS, and more.

• Comprehensive Training Algorithms: Supports multiple training methods including pre-training, SFT, Reward Modeling, PPO/DPO/KTO, and more.

• Flexible Computation Precision: LLaMA-Factory Online offers 16-bit full-parameter fine-tuning, frozen fine-tuning, LoRA fine-tuning, as well as QLoRA fine-tuning based on 2/3/4/5/6/8-bit.

• Advanced Optimization Techniques: Integrates cutting-edge optimization techniques such as GaLore, Badam, LoRA+, PiSSA, DORA, rsLoRA, and more.

• Convenient Experiment Monitoring: Built-in real-time monitoring tools including LlamaBoard, TensorBoard, Wandb, Mlflow, SwanLab, and more.

• Fast Training and Inference: Uses accelerated operators such as FlashAttention-2 and Unsloth, and supports Transformers and vLLM inference engines.

LLaMA-Factory Online Use Cases

• University Research Users: No need to worry about complex GPU configuration and maintenance, solving the problem of insufficient or poor-performance computing resources at schools, and accelerating research progress.

• Individual Developers / Tech Enthusiasts: Quickly run experiments and validate ideas, lowering the barrier to innovating with large models and reducing compute costs.

• Enterprise Users: No coding or configuration required, significantly lowering the technical barrier to deploying large models and the cost of building a team, with high-performance compute ensuring fine-tuning efficiency and effectiveness.

LLaMA-Factory Online Advantages

• Reliable Official Partnership: Officially partnered with the star open-source project LLaMA-Factory, with a mature technical roadmap and timely updates.

• Top-Tier and Efficient Compute: Built on NVIDIA H-series high-performance GPUs at the foundation, supporting distributed training across multiple machines and multiple cards, greatly shortening training time.

• Full-Chain and Easy to Use: Covers the entire workflow from data upload, preprocessing, fine-tuning, monitoring to evaluation—truly ready to use right out of the box.

• Flexible Adaptation for Various Scenarios: University research users, individual developers, tech enthusiasts, and startup teams can all start customizing large models with low barriers and low cost.

• Simple and Intuitive Operation: LLaMA-Factory Online features a user-friendly web interface, enabling one-click access to cloud GPU resources—users without a technical background can quickly learn the entire fine-tuning workflow.

• Flexible and Cost-Effective Billing: Offers multiple billing modes (Premium Speed, Dynamic Savings, Flexible Economy), allowing them to choose the most economical compute usage method based on task requirements.

How to Use LLaMA-Factory Online

1. Prepare Data and Model: Upload the dataset to be fine-tuned to the platform via SFTP or other methods.

2. Configure and Launch the Task: Enter the model fine-tuning task space, select the base model to fine-tune on the web interface, set key parameters, and choose Quick Fine-Tuning (suitable for beginners) or Expert Fine-Tuning (suitable for deep customization). Based on budget and time requirements, select an appropriate billing mode (Premium Speed, Dynamic Savings, Flexible Economy), then launch with a single click.

3. Monitor and Evaluate: Use tools such as LlamaBoard and TensorBoard to monitor training loss and resource usage in real time. Once training is complete, use the model evaluation feature to check fine-tuning results, and immediately test model performance using the model dialogue feature.