GPT-LLM-Trainer-Quickly turn an idea into a fully trained model

Introduce GPT-LLM-Trainer, Can quickly turn an idea into a fully trained model

The “gpt-llm-trainer” project on GitHub aims to simplify the process of training high-performing task-specific AI models. It utilizes GPT-4 to generate prompts and responses for a given use-case, automating dataset creation and model fine-tuning. By inputting a task description, users can generate a dataset, fine-tune a LLaMA 2 model, and achieve a ready-to-use AI. The process involves setting the prompt, temperature, and number of examples, then running the notebook cells. Contributions to improve efficiency, diversity, and performance of the model generation pipeline are encouraged. The project is MIT licensed and led by Matt Shumer.

Project Link: https://github.com/mshumer/gpt-llm-trainer

Overview

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. 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.

Features:

• Dataset Generation: Using GPT-4, gpt-llm-trainer will generate a variety of prompts and responses based on the provided use-case.
• System Message Generation: gpt-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.

Setup:

1. Open the notebook in Google Colab or in a local Jupyter notebook.
2. If you’re using Colab, switch to the best GPU available (go to Runtime -> change runtime type).
3. Add your OpenAI API key to the line openai.api_key = “YOUR KEY HERE”.

How to Use:

1. Define your prompt. The prompt is a description of what you want the trained AI to do. The more descriptive and clear you can be, the better. Additionally, set the temperature we will use to generate your dataset (high=creative, low=precise), and the number of examples you want to generate (100 is a good starting point).
2. Run all the cells (stop at Merge the model and store in Google Drive). It’ll take some time (from 10 minutes to a couple of hours, depending on how many examples you generate), but soon, you’ll have your fine-tuned model!
3. After your model is trained, you can use the Run Inference cell to test the model, and the cells below that allow you to save and load the model to and from Google Drive for later use.