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).

LLaMA-Factory Online: No-code LLM Fine-tuning Platform

Name: LLaMA-Factory Online
Rating: 5 (79 reviews)
Author: toolify

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No-code LLM Fine-tuning Platform

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LLaMA-Factory Online Product Information

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.

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.

LLaMA-Factory Online's Core Features

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.

LLaMA-Factory Online's Use Cases

Automated Chinese Essay Grader Built with Qwen3-vl-30B-A3B-Instruct for intelligent scoring and feedback.

Specialized AI Ticketing Assistant A customized customer service agent powered by Qwen2.5-14B-Instruct.

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

Medical Imaging Analysis A practical fine-tuning case for healthcare diagnostics using Qwen3-vl-30B-A3B-Instruct.

Lightweight Smart Home Model Deployed on Qwen3-4B for efficient, edge-based home automation control.

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

FAQ from LLaMA-Factory Online

How can I improve the model performance if the fine-tuning results are unsatisfactory?
How do I resolve "CUDA Out of Memory" (OOM) errors during training?
How do I fix nonsensical or repetitive model outputs?

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

Here is the LLaMA-Factory Online support email for customer service: [email protected] . More Contact, visit the contact us page(mailto:[email protected])
LLaMA-Factory Online Company

LLaMA-Factory Online Company name: DataCanvas .

LLaMA-Factory Online Company address: .

More about LLaMA-Factory Online, Please visit the about us page().
LLaMA-Factory Online Login

LLaMA-Factory Online Login Link: https://www.llamafactory.com.cn/login
LLaMA-Factory Online Sign up

LLaMA-Factory Online Sign up Link: https://www.llamafactory.com.cn/login