How to train and fine tune your LLM 10 mins

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-trainer will generate a variety of prompts and responses based on the provided use-case.
  • System Message Generationgpt-llm-trainer will 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.

Details here.

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