SLMGen

SLMGen is a web application that helps users fine-tune small language models (SLMs). Fine-tuning means teaching a pre-existing AI model to perform a specific task better using your own data. SLMGen makes this process faster and easier, aiming to be up to two times quicker and free to use.

Benefits

SLMGen simplifies the process of customizing AI models. Users can upload their data, and the application provides ready-to-use Google Colab notebooks. These notebooks are optimized with tools like Unsloth and LoRA for better performance. The application also scores the quality of your dataset, checks for duplicates, and ensures consistency. It helps you choose the best model for your task by considering factors like task fit, deployment needs, and data characteristics. SLMGen offers insights into model strengths and weaknesses and can even show you potential failure cases before you start training.

Use Cases

This tool is useful for anyone who wants to create specialized AI models without a complex setup. You can use SLMGen to fine-tune models for various applications, such as chatbots, content generation, or data analysis, tailored to your specific needs. It supports a wide range of models like Phi-4, Llama 3.2, and Mistral 7B, and can be used for deployment on different platforms including cloud, servers, desktops, mobile devices, and even web browsers.

Vibes

The application uses a system to match models based on task fit, deployment target, and data traits, aiming for a perfect score. It also provides a real-time simulation of the training process.

Additional Information

SLMGen is built using modern web technologies like Python with FastAPI for the backend and Next.js with React for the frontend. It uses Supabase for authentication. The training process runs on Google Colab, and the application is deployed on Vercel for the frontend and Render for the backend. The project is licensed under the MIT License.