LLaMA-Factory Online

Overview

LLaMA-Factory Online is an online platform for large-model training and fine-tuning scenarios, built in official cooperation with the open-source project LLaMA-Factory. Through a low-code, visual Web interface, it integrates data upload, parameter configuration, model fine-tuning, training monitoring, and performance evaluation into the same platform, lowering the barrier to using large-model customization.

The platform supports 100+ mainstream open-source models, covering series such as LLaMA, Qwen, DeepSeek, GLM, GPT-OSS, making it suitable for individual developers, startup teams, and university research users to quickly carry out model adaptation and experimentation. For users who do not want to build training environments themselves, configure GPUs, or maintain complex dependencies, this kind of one-stop solution is easier to get started with.

Key Features

• Supports multiple types of mainstream open-source models

  • Provides 100+ optional models, making it convenient to choose a suitable base model according to the task type.

• Covers multiple training methods

  • Supports pre-training, SFT, Reward Modeling, as well as training approaches such as PPO, DPO, and KTO.

• Multiple fine-tuning and precision modes

  • Supports 16bit full-parameter fine-tuning, freeze fine-tuning, LoRA fine-tuning, as well as QLoRA fine-tuning at 2/3/4/5/6/8bit.

• Integrates multiple optimization technologies

  • Including GaLore, BAdam, LoRA+, PiSSA, DORA, rsLoRA, etc., used to improve training efficiency or results.

• Visual monitoring of the training process

  • Built-in LlamaBoard, and supports tools such as TensorBoard, Wandb, Mlflow, and SwanLab, making it convenient to track training status and resource usage.

• High-performance computing power and acceleration capabilities

  • Provides high-performance GPU resources, supports distributed training, and combines acceleration solutions such as FlashAttention-2 and Unsloth to improve training efficiency.

• Inference and evaluation support

  • Compatible with Transformers and vLLM inference engines, and model evaluation and dialogue testing can be carried out after training is completed.

• Low-code operating experience

  • Configure tasks and schedule GPUs through a visual interface, without needing complex local environment deployment.

Product Pricing

According to official information, LLaMA-Factory Online provides a flexible billing model, including different types of computing power and task plans, suitable for needs that balance timeliness, budget, and training scale.
Currently, public information does not show a unified fixed price. Specific costs usually vary depending on the selected GPU resources, training duration, task mode, and promotional offers. It is recommended to refer to the actual page after registering on the official website.

Frequently Asked Questions

Who is it suitable for?

• Individual developers who want to quickly validate large-model fine-tuning results
• Startups or small teams hoping to build AI capabilities at low cost
• University researchers and students who lack a local GPU environment

Is it necessary to write a lot of code?

Not necessarily. The platform focuses on low-code and visual operations, and many training workflows can be completed directly in the Web interface. However, for data preparation, parameter understanding, and experiment design, it is still recommended to have basic knowledge of model training.

What fine-tuning tasks can it do?

From publicly available information, the platform can be used for tasks such as supervised fine-tuning, reward modeling, and preference optimization. It also supports different precision and parameter-efficient fine-tuning schemes, making it suitable for scenarios such as question answering, dialogue, and industry knowledge adaptation.