Automatiza creación de conjuntos de datos en Llama-2 con GPT-4

Table of Contents

1. Introduction
2. Background
3. GPT Llm Trainer: A Brief Overview
4. Setting Up the Project
5. Creating a Data Set
   • Generating Examples
   • Customizing the Prompt and System Message
   • Adjusting Parameters
6. Training the Model
   • Defining Hyperparameters
   • Using the Custom Llama27b Chat Model
   • Saving the Trained Model
7. Testing and Inference
8. Alternative Approaches
   • Auto Train Advanced Package from Hugging Face
9. Conclusion

GPT Llm Trainer: Automating Data Creation and Model Training

In this article, we will explore the GPT Llm Trainer, a tool that simplifies the process of training and fine-tuning large language models. Training language models is notoriously complex and involves various steps such as data collection, data cleaning, formatting, model selection, and code writing. The GPT Llm Trainer aims to streamline this process by providing a pipeline for training high-performing task-specific models.

1. Introduction

Training language models is a challenging task that requires expertise in data science and machine learning. It involves collecting Relevant data, cleaning and formatting it appropriately, selecting an appropriate model architecture, writing code to train the model, and finally conducting the training process. These steps can be time-consuming and require significant computational resources.

2. Background

The GPT Llm Trainer project was developed to address these challenges and simplify the process of training language models. It utilizes the power of the GPT-3.5 Turbo and GPT-4 APIs from OpenAI to automate the data creation and model training process. With this tool, users only need to provide a description of their task, and the GPT Llm Trainer will generate a data set, format it correctly, and fine-tune a Llama2 model for the specific task.

3. GPT Llm Trainer: A Brief Overview

The GPT Llm Trainer is a Google Colab notebook that provides a streamlined pipeline for training task-specific models. It relies on the GPT-3.5 Turbo and GPT-4 APIs from OpenAI to generate data sets and perform fine-tuning on the Llama2 model. The notebook is designed to be easy to use, even for users with limited coding experience. It requires access to the GPU, such as a V100 or A100 GPU, for optimal performance.

4. Setting Up the Project

To begin using the GPT Llm Trainer, You need to set up the project in a Google Colab notebook. The notebook contains all the necessary code and instructions for automating the data creation and model training process. You will also need an OpenAI API Key to access the GPT-3.5 Turbo and GPT-4 APIs. Detailed setup instructions are provided in the notebook.

5. Creating a Data Set

The first step in using the GPT Llm Trainer is creating a data set. The notebook provides a convenient function to generate examples for the data set. You can customize the prompt and system message to make it specific to your task. It is important to provide clear and descriptive instructions to ensure the generation of a high-quality data set.

5.1 Generating Examples

The GPT Llm Trainer utilizes the GPT-3.5 Turbo and GPT-4 APIs to generate examples for the data set. By providing a single prompt, the system automatically generates relevant examples in the form of prompt-response pairs. However, it is important to note that the number of examples generated may not match the requested number due to limitations in querying the OpenAI API.

5.2 Customizing the Prompt and System Message

To fine-tune the Llama2 model effectively, it is recommended to provide a specific and descriptive prompt. The prompt should clearly indicate the desired task or functionality that the AI should learn. Additionally, a system message can be added to provide additional Context for the task. The notebook provides options to customize both the prompt and the system message.

5.3 Adjusting Parameters

The GPT Llm Trainer offers two parameters that can be adjusted according to your requirements. The first parameter is the temperature, which controls the creativity of the generated responses. Higher values result in more diverse but potentially less coherent outputs, while lower values produce more focused outputs. The Second parameter is the number of examples to generate. However, due to limitations in querying the OpenAI API, the actual number of generated examples may not match the requested number.

6. Training the Model

Once the data set is created, the GPT Llm Trainer facilitates the fine-tuning of the Llama2 model. The notebook provides a straightforward process to define the hyperparameters for training and select the appropriate Llama27b chat model. The trained model can be saved for future use.

6.1 Defining Hyperparameters

Before training the model, it is necessary to define the hyperparameters. These include the model name, data set name, and the desired name for the new trained model. The GPT Llm Trainer provides default values for these parameters, but they can be modified to suit your specific needs.

6.2 Using the Custom Llama27b Chat Model

The GPT Llm Trainer uses a custom Llama27b chat model for fine-tuning. The notebook makes use of a default prompt template provided by the model's Creators. This ensures that the data set is formatted correctly for the chat version of the Llama2 model. If you are fine-tuning a different version of the Llama2 model, you can define your own prompt template.

6.3 Saving the Trained Model

After the model is trained, it can be saved to your Google Drive for later use. The notebook provides a code segment to save the trained model's tokens and corresponding configurations to a custom path. This allows you to load the stored model from your Google Drive and perform inference on new examples.

7. Testing and Inference

Once the model is trained and saved, it can be tested on new Prompts or used for inference on real-world data. The notebook provides examples of how to perform inference using the trained model. By providing a prompt, the trained model can generate responses related to the specified task.

8. Alternative Approaches

While the GPT Llm Trainer offers a convenient pipeline for data creation and model training, there are alternative approaches available. One such approach is the Auto Train Advanced package from Hugging Face. This package allows users to train powerful language models with a single line of code. However, it requires a powerful GPU and may not have the same level of automation as the GPT Llm Trainer.

9. Conclusion

In conclusion, the GPT Llm Trainer is a valuable tool for automating the data creation and model training process. By leveraging the power of the GPT-3.5 Turbo and GPT-4 APIs, users can generate high-quality data sets and fine-tune task-specific models. While some limitations and challenges may be encountered during the process, the GPT Llm Trainer provides a user-friendly solution that simplifies the training of language models. With its straightforward setup and easy-to-use interface, it is accessible to both beginners and experienced practitioners. Consider using the GPT Llm Trainer for your next language model training project.

Highlights

• The GPT Llm Trainer automates the data creation and model training process for language models.
• It leverages the GPT-3.5 Turbo and GPT-4 APIs from OpenAI for generating data sets and fine-tuning models.
• The GPT Llm Trainer provides a user-friendly Google Colab notebook that simplifies the training process.
• Users can customize prompts and system messages to fine-tune task-specific models.
• The trained models can be saved for future use and utilized for testing and inference on new examples.
• An alternative approach is the Auto Train Advanced package from Hugging Face, which offers a powerful yet manual training process.

FAQ

Q: What is the GPT Llm Trainer? A: The GPT Llm Trainer is a tool that automates the data creation and model training process for language models. It utilizes the GPT-3.5 Turbo and GPT-4 APIs from OpenAI to simplify the training pipeline.

Q: How does the GPT Llm Trainer generate data sets? A: The GPT Llm Trainer generates data sets by utilizing the GPT-3.5 Turbo and GPT-4 APIs. Users provide a single prompt, and the system generates relevant examples in the form of prompt-response pairs.

Q: Can I customize the prompts and system messages used by the GPT Llm Trainer? A: Yes, users can customize the prompts and system messages to fine-tune the task-specific models. Clear and descriptive instructions should be provided to ensure the generation of high-quality data sets.

Q: Can I use the GPT Llm Trainer with other models? A: The GPT Llm Trainer is primarily designed for the Llama2 chat model. However, users can modify the prompt template and use other base versions of the Llama2 model if desired.

Q: Are there alternative approaches to the GPT Llm Trainer? A: Yes, an alternative approach is the Auto Train Advanced package from Hugging Face. It allows users to train powerful language models with minimal code. However, it requires a powerful GPU and may not offer the same level of automation as the GPT Llm Trainer.