Customize Your Own Large Model - Is It Necessary to Manually Write Code? This Is the Correct Way to Fine-tune!

Over the past two or three years, large models have evolved from "novelties" into part of many people's work and daily lives. From ChatGPT, LLaMA to Qwen, DeepSeek, more and more general-purpose models continue to be updated and improved, with increasingly powerful capabilities.

However, in real business scenarios, many teams and developers face this dilemma: general models can "say a few words about anything," but often "answer off-topic." To make models truly understand industry knowledge and align with business logic, fine-tuning has become almost an inevitable path.

The problem is - traditional fine-tuning methods are still very high in entry barriers:

• Complex environment: configuring dependencies can take days;

• High costs: high-performance GPU computing resources are scarce and expensive, and one fine-tuning experiment means tens of thousands of yuan in GPU expenses;

• Parameter tuning is complicated: newcomers unfamiliar with training parameters often get stuck during configuration.

These issues have been raised hundreds of times in our technical communities. Until LLaMA-Factory Online appeared — the fine-tuning barrier has been reduced to a new low, even beginners can get started, customizing a dedicated model as simple as opening a browser.

LLaMA-Factory Online: A One-stop Fine-tuning Platform for Everyone

LLaMA-Factory Online is an online large model training and fine-tuning service platform co-developed with the star open-source project LLaMA-Factory officially, providing high-performance and highly elastic GPU computing resources at the core. It offers ready-to-use, low-code, full-chain functional large model training and fine-tuning services for users with basic coding and engineering abilities.

It restructures the previously costly and technically demanding fine-tuning process into a visualized, online, and low-code one-stop cloud service, allowing teams to focus on business or technical implementation without worrying about resource and configuration issues.

Why choose LLaMA-Factory Online?

l Official cooperation, reliable endorsement: produced by the official collaboration with the star open-source project LLaMA-Factory, with mature technical routes and timely updates.

l Low-code visualization, simple operation: provides a friendly and easy-to-use Web interface, one-click scheduling of cloud GPU resources, making it easy for even those without technical backgrounds to quickly get started with fine-tuning.

l Full-chain support, ready to use: covers the entire model fine-tuning and training process, from data upload, preprocessing, fine-tuning, monitoring to evaluation, all done seamlessly.

l Flexible adaptation, wide application scenarios: whether you are an education and research user, individual developer, tech enthusiast, or startup team, you can start large model customization at a low cost and low threshold.

Outstanding Features, Everything You Need

In addition to a convenient one-stop fine-tuning experience, LLaMA-Factory Online also deeply empowers you in terms of technical depth and training support, helping you efficiently customize high-quality AI models.

1. Support for over 100 models, covering all business scenarios

LLaMA-Factory Online supports mainstream large models such as LLaMA, DeepSeek, Mistral, Qwen, Gemma, GPT-OSS, ChatGLM, Phi, meeting diverse needs from research experiments to enterprise applications.

The platform also pre-installs rich mainstream open-source datasets and supports uploading private models and data, balancing generality and personalization while ensuring data security and usage flexibility.

2. Flexible fine-tuning methods, suitable for various needs

The platform offers lightweight and fast fine-tuning solutions like LoRA and QLoRA, suitable for low-cost experiments.

It also supports expert-level deep optimization paths such as incremental pre-training and full-parameter fine-tuning, meeting high-precision requirements.

3. Efficient and low-cost, intelligent GPU resource scheduling

LLaMA-Factory Online relies on high-performance NVIDIA H-series GPUs, significantly increasing training speed and shortening the training cycle, helping to quickly achieve results.

Compute power is billed by usage, and there are zero costs when not in use. In task mode, it supports three modes: [Ultra Fast Priority / Dynamic Discount / Flexible Cost Saving], offering flexible and controllable costs with elastic billing.

4. Visual Monitoring, Full Control Over the Training Process

Through API integration with SwanLab, track training progress and key metrics in real-time. Additionally, it includes tools such as LlamaBoard, TensorBoard, Wandb, and MLflow, supporting comparison and tracking, facilitating timely adjustments and optimizations.

Tip: The LLaMA-Factory Online exclusive user group regularly shares fine-tuning techniques and best practices, helping you avoid pitfalls in fine-tuning～

10 Hours to Fine-tune a Production-Level Smart Home Dedicated Large Model, This Is the True One-stop Experience

In a smart home project, a team completed the fine-tuning of a production-level smart home interaction model from scratch in just 10 hours using LLaMA-Factory Online, shortening the development cycle by 67% while achieving a performance improvement of more than 50% in the model.

The team built a complete workflow from data engineering to model production based on the LLaMA-Factory framework, targeting smart home control tasks such as device switching, parameter adjustment, condition triggering, chain operations, and scenario modes, and solved the performance bottleneck of lightweight models in vertical scenarios.

Prerequisites

Register and log in with a mobile phone number to LLaMA-Factory Online, no longer requiring local deployment of frameworks, environment configuration, and verification.

Data Set Preparation

Recommended public dataset: Smart Home Command Dataset (https://huggingface.co/datasets/youkwan/Smart-Home-Control-Zh), containing 10k+ smart home command samples, compatible with Alpaca format.

On the LLaMA-Factory Online platform instance mode, select a CPU instance and process the data through Jupyter, including: deduplication, format repair, length filtering, and quality validation.

Model Fine-tuning

To adapt to edge devices, the initial selection included LLaMA-2-7B-Chat, Mistral-0.6B-Instruct, and Qwen3-4B-Instruct as base models. Then, they were screened based on three dimensions: "pre-training task match degree + inference efficiency + fine-tuning cost," ultimately choosing Qwen3-4B-Instruct.

The LLaMA-Factory Online platform already preinstalls mainstream open-source large models and datasets, which can be directly selected during fine-tuning. Additionally, common key parameters can be configured through a visual interface, saving time spent writing code and allowing development teams to focus on parameter selection and optimization.

In this case, key parameters include lora_rank, lora_alpha, lora_dropout, num_train_epochs, per_device_train_batch_size, etc. Specific parameter configurations and key monitoring indicators of the training process can be viewed in Document Center - Best Practices, and exchanged with users and contributors in the community.

Example of Fine-tuning Effect

Comparison of core functions before and after fine-tuning:

Conditional Judgment Function: From "Failure" to "Stability"

Chain Operation Function: From "Low Throughput Rate" to "Almost Perfect"

In this case, thanks to the full-chain functionality of the LLaMA-Factory Online platform, the team saved time on installing frameworks, downloading models, preparing hardware resources, and environment verification, allowing them to focus on the key aspects of model fine-tuning and effect evaluation, enabling them to solve the challenge of insufficient performance of lightweight models in smart home interaction scenarios within a very short time.

Conclusion

If you're looking for a zero-barrier, low-cost and high-efficiency large model fine-tuning tool, why not try it now: https://www.llamafactory.online/, which not only lowers the barrier to AI innovation, but also brings large models into education, research, finance, e-commerce, customer service and other industries, shining brightly in their respective fields!