LLaMA-Factory Online

LLaMA-Factory Online Product Information

What is LLaMA-Factory Online?

LLaMA-Factory Online is the official cloud platform for LLM training and fine-tuning, created in collaboration with the popular open-source project LLaMA-Factory. It offers a ready-to-use, low-code, and end-to-end solution for users who want to simplify fine-tuning or have limited engineering capabilities.

How to use LLaMA-Factory Online?

Start fine-tuning in three easy steps: 1. Prepare Data & Models: Easily upload your datasets using SFTP or other supported methods. 2. Configure & Launch: Pick your base model and adjust key settings in our visual interface. Choose Quick Mode for a fast setup or Expert Mode for advanced options. Select a pricing plan that suits your budget and timeline, then launch the task with one click. 3. Monitor & Evaluate: Use built-in tools like LlamaBoard and TensorBoard to track training loss and resource usage in real time. After training, evaluate results with Model Evaluation or test performance immediately using Model Chat.

LLaMA-Factory Online's Core Features

Extensive Model Library (100+ Models): Access over 100 leading open-source models, including LLaMA, Qwen, DeepSeek, GPT-OSS, and others.

Comprehensive Training Methods: Supports the entire training process, from pre-training and SFT (Supervised Fine-Tuning) to reward modeling and alignment methods like PPO, DPO, and KTO.

Flexible Precision & Quantization: Customize resource usage with 16-bit full fine-tuning, freeze-tuning, LoRA, and QLoRA, which supports 2/3/4/5/6/8-bit quantization.

Cutting-edge Optimization Algorithms: Benefit from integrated advanced optimizations such as GaLore, BAdam, LoRA+, PiSSA, DoRA, and rsLoRA.

Robust Experiment Tracking: Monitor training progress in real time with built-in support for LlamaBoard, TensorBoard, WandB, MLflow, and SwanLab.

High-Efficiency Acceleration: Boost performance with FlashAttention-2 and Unsloth acceleration, compatible with both Transformers and vLLM inference engines.

LLaMA-Factory Online's Use Cases

Automated Chinese Essay Grader: Uses Qwen3-vl-30B-A3B-Instruct to provide intelligent scoring and feedback.

Specialized AI Ticketing Assistant: A custom customer service agent built with Qwen2.5-14B-Instruct.

Low-Cost Fine-tuning for Massive MoE Models: Efficiently fine-tunes DeepSeek-V3 using KTransformers optimization.

Medical Imaging Analysis: A healthcare diagnostics application fine-tuned with Qwen3-vl-30B-A3B-Instruct.

Lightweight Smart Home Model: Deploys Qwen3-4B for efficient edge-based home automation control.

Legal AI Agent: A specialized legal assistant developed with LightLLM and LlamaIndex.

FAQ from LLaMA-Factory Online

How can I improve the model performance if the fine-tuning results are unsatisfactory?

1. Expand the Dataset: Use a larger training dataset. 2. Increase Training Duration: Raise the number of epochs (e.g., num_train_epochs: 5.0) or total steps (e.g., max_steps: 1000). 3. Adjust Learning Rate: Try a higher learning rate (e.g., 2.0e-4). 4. Switch Fine-tuning Methods: Test different approaches like freeze (parameter freezing) or full (full-parameter tuning) via the finetuning_type setting.

How do I resolve "CUDA Out of Memory" (OOM) errors during training?

If you encounter GPU memory issues, try these optimizations: 1. Reduce Batch Size: Set per_device_train_batch_size to 1. 2. Optimize Operators: Enable memory-efficient kernels by setting enable_liger_kernel and use_unsloth_gc to true. 3. Shorten Sequence Length: Decrease cutoff_len (e.g., to 512). 4. Use Advanced Parallelism: Enable DeepSpeed ZeRO-3 or FSDP to distribute model weights across devices, or use CPU Offloading. 5. Apply Quantization: Set quantization_bit to 4 to compress parameters (available for LoRA fine-tuning only).

How do I fix nonsensical or repetitive model outputs?

The solution depends on when the problem appears: If it occurs BEFORE training: This is often due to using a raw Base model (unaligned) or an incorrect prompt template. Make sure you use an aligned Instruct/Chat model and the right template. If it occurs AFTER training: Check Template Consistency: Ensure the inference template matches the one used during training. Watch for Overfitting: If the model is overfitting, reduce training epochs (num_train_epochs) and lower the learning rate.

LLaMA-Factory Online Support Email & Customer service contact & Refund contact etc.

Contact LLaMA-Factory Online customer support at: [email protected] . For more ways to reach us, visit the contact us page(mailto:[email protected])

LLaMA-Factory Online Screenshot