LLaMA-Factory Online

LLaMA-Factory Online: easy fine-tuning without code
1. Introduction: (Out-of-the-box, low-code, full-link functional coverage online large model training and fine-tuning service platform)
LLaMA-Factory Online is an online large model training and fine-tuning service platform built in cooperation with the star open source project LLaMA-Factory. We are committed to providing out-of-the-box, low-code, full-link functional coverage of large model training and fine-tuning services for user groups with fine-tuning needs and basic engineering capabilities.
LLaMA-Factory Online completely reconstructs the traditional fine-tuning process into a visual, online, low-code one-stop cloud-based service. Users don't need to worry about the allocation and configuration of underlying resources, they can simply schedule high-performance and highly flexible GPU computing power with a single click through the friendly web interface, realizing the coverage of the whole chain from data to customized models, allowing the team to focus on the business and technical implementation itself, and dramatically improving the development efficiency.

2. Functional features: deep integration and cutting-edge technologies
a. 100+ models to choose from: covering LLaMA/Qwen/DeepSeek/GPT-OSS and other mainstream models.
b. Complete training algorithms: support for pre-training, SFT, Reward Modeling, PPO/DPO/KTO and other training methods.
c. Operational accuracy flexibility: covering 16bit full parameter trim, freeze trim, LoRA trim and 2/3/4/5/6/8bit based QLoRA trim.
d. Advanced optimization algorithms: integration of GaLore/Badam/LoRA+/PiSSA/DORA/rsLoRA and other cutting-edge optimization techniques, etc.
e. Complete experiment monitoring: built-in real-time monitoring tools such as LlamaBoard/TensorBoard/Wandb/Mlflow/SwanLab.
f. Efficient training inference: adopts FlashAttention-2/Unsloth and other acceleration operators, and supports Transformers/vLLM inference engine.

3. Application Scenarios: Helping AI Innovations in Multiple Fields to Come to Fruition
With its flexibility and power, LLaMA-Factory Online is widely applicable to the following people and scenarios:
a. Higher education research users: eliminating complex GPU configuration and maintenance, overcoming the bottleneck of tight computing resources or insufficient performance on campus, and accelerating scientific research.
b. Individual developers/technology enthusiasts: quickly try and experimentally validate, lowering the threshold of innovation and arithmetic use for large model applications.
c. Enterprise users: zero code, configuration-free, significantly reducing the technical threshold of large model application landing and team formation costs; high-performance computing power to ensure the efficiency and effectiveness of fine-tuning.

4. Product Advantage: Official Endorsement and Ultimate Ease of Use
The core strength of LLaMA-Factory Online is the combination of reliability, ease of use and high performance:
a. Official cooperation, reliable technology: with the star of the open source project LLaMA-Factory official cooperation production, to ensure that the technology line is mature, updated in a timely manner.
b. Top arithmetic, doubling the efficiency: the bottom is equipped with NVIDIA H-series high-performance graphics cards, and supports multi-machine and multi-card distributed training, significantly shortening the training cycle.
c. Full-link support, out-of-the-box: covering the whole process of model fine-tuning training from data upload, preprocessing, fine-tuning, monitoring to evaluation, truly realizing out-of-the-box use.
d. Flexible adaptation, wide range of application scenarios: Whether it is education and research users, individual developers, technology enthusiasts or start-up teams, they can start the practice of large model customization with low threshold and low cost.
e. Low-code visualization, minimal operation: provide a friendly and easy-to-use Web interface, one-click scheduling of cloud GPU resources, even users with no technical background can quickly get started with the whole process of fine-tuning.
f. Flexible billing, cost-effective: provide a variety of billing modes (high-speed enjoyment, dynamic preferential, dynamic super-saving), users can choose the most cost-effective way to use the arithmetic power according to the rhythm of the task.

5. Usage: three quick steps to start the fine-tuning journey
a. Data and model preparation: Upload the dataset to be fine-tuned to the platform via SFTP or other means.
b. Configure and start the task: enter the model fine-tuning task space, in the visualization interface, select the base model you need to fine-tune, set the key parameters, and choose quick fine-tuning (Extreme Getting Started) or expert fine-tuning (Deep Customization). According to the budget and timeliness, choose the appropriate billing mode (Extreme Premium, Dynamic Discount, and Spirit Ultra Save) and start it with one click.
c. Monitoring and Evaluation: Monitor the training loss and resource utilization of the task in real time through built-in tools such as LlamaBoard/TensorBoard. After the training is completed, use the model evaluation function to quantify the fine-tuning effect; use the model dialog function to instantly check the model performance.