LLaMA-Factory Online
No-code LLM Fine-tuning Platform
Source rating 5.0
(79)
579 views
579/mo traffic
0 saves
Unknown
Overview
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.
Access & connections
Where you can use LLaMA-Factory Online.
Platforms
Capabilities
What this tool accepts, produces, and is strongest at.
Highlights
- 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.
FAQ 5
What is 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.
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 LLaMA-Factory Online?
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.
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.
How can I improve the model performance if the fine-tuning results are unsatisfactory?
1. Expand the Dataset: Increase the size of your training dataset. 2. Increase Training Duration: Set a higher number of epochs (e.g., num_train_epochs: 5.0) or increase the total steps (e.g., max_steps: 1000). 3. Adjust Learning Rate: Try increasing the learning rate (e.g., set learning_rate to 2.0e-4). 4. Switch Fine-tuning Methods: Experiment with different fine-tuning types, such as freeze (freezing parameters) or full (full-parameter tuning) via the finetuning_type setting.
How do I resolve "CUDA Out of Memory" (OOM) errors during training?
If you run out of GPU memory, try the following optimization strategies: 1. Reduce Batch Size: Set per_device_train_batch_size to 1. 2. Optimize Operators: Enable memory-efficient kernels by setting enable_liger_kernel to true and use_unsloth_gc to true. 3. Shorten Sequence Length: Reduce the cutoff_len (e.g., set to 512). 4. Use Advanced Parallelism: Enable DeepSpeed ZeRO-3 or FSDP to shard model weights across devices, or enable CPU Offloading. 5. Apply Quantization: Set quantization_bit to 4 to compress model parameters (Available for LoRA fine-tuning only).
How do I fix nonsensical or repetitive model outputs?
The solution depends on when the issue occurs: If occurring BEFORE training: This is usually caused by using a raw Base model (unaligned) or an incorrect prompt template. Please ensure you are using an aligned Instruct/Chat model and the correct template. If occurring AFTER training: Template Consistency: Verify that the template used for inference matches the one used during training. Check for Overfitting: If the model is overfitting, try reducing the training epochs (num_train_epochs) and lowering the learning rate (learning_rate).
Related tools
Recommended
LLaMA-Factory Online