Training models is hard. You have to collect a dataset, clean it, get it in the right format, select a model, write the training code and train it. And that’s the best-case scenario.
Meet project GPT LLM trainer.
The goal of this project is to explore an experimental new pipeline to train a high-performing task-specific model. We try to abstract away all the complexity, so it’s as easy as possible to go from idea -> performant fully-trained model.
Simply input a description of your task, and the system will generate a dataset from scratch, parse it into the right format, and fine-tune a LLaMA 2 model for you.
- Dataset Generation: Using GPT-4,
gpt-llm-trainerwill generate a variety of prompts and responses based on the provided use-case.
- System Message Generation:
gpt-llm-trainerwill generate an effective system prompt for your model.
- Fine-Tuning: After your dataset has been generated, the system will automatically split it into training and validation sets, fine-tune a model for you, and get it ready for inference.