Comment créer des modèles personnalisés avec GPT

Table of Contents

1. Introduction
2. Customizing Large Language Models
3. Understanding Lang Chain
4. Modules in Lang Chain
   1. Models
   2. Prompts
   3. Memory
   4. Indexes
   5. Chains
   6. Agents
5. Training GPT Models Using Custom Data Sets
6. Creating Text Chunks and Overlaps
7. Generating Embeddings
8. Storing and Querying Embeddings
9. Implementing Lang Flow with Low Code/No Code Strategy
10. Building a Profile GPT using Lang Flow

Customizing Large Language Models with Lang Chain

Large language models have become increasingly popular with the release of models like GPT. Many developers are now looking to train their own GPT models using custom datasets. In this tutorial, we will explore the concept of customizing large language models using something called Lang Chain. Lang Chain is a framework that provides different modules to help You build your own custom language models.

Understanding Lang Chain

Lang Chain is a framework that offers various modules to customize and build upon existing large language models. These modules include models, prompts, memory, indexes, chains, and agents. Models refer to the actual large language models like Hugging Face LLM or OpenAI LLM. Prompts allow you to Create reusable templates for querying the model. Memory is used to store conversations and maintain Context. Indexes and chains provide ways to link different language models together. Agents provide access to tools within the Lang Chain framework.

Training GPT Models Using Custom Data Sets

One of the main objectives of this tutorial is to train GPT models using custom data sets. We will explore how to achieve this using Lang Chain. By leveraging the various modules available in Lang Chain, we can train GPT models on our own custom data.

Creating Text Chunks and Overlaps

To train GPT models on custom data, we need to create text chunks and define the overlap between these chunks. By splitting a text document into chunks and setting the overlap, we ensure that no information is lost during the training process.

Generating Embeddings

Embeddings play a crucial role in large language models. They represent words or phrases of a document as vectors in a lower-dimensional vector space. In this tutorial, we will learn how to generate embeddings for our custom data sets. We will also explore the process of creating a database to store these embeddings.

Storing and Querying Embeddings

Once we have generated embeddings for our custom data, the next step is to store and query them. We will use vector stores like Chroma DB to store and retrieve embeddings. By comparing vectors, we can find similarity scores or distances between documents, enabling us to perform semantic search.

Implementing Lang Flow with Low Code/No Code Strategy

For developers who want to create NLP applications without extensive coding knowledge, we will introduce a low code/no code strategy using Lang Flow. Lang Flow is a graphical user interface (GUI) that allows you to implement Lang Chain without manual coding. Instead, you can drag and drop components to create your own chain of actions and build GPT models.

Building a Profile GPT using Lang Flow

In this tutorial, we will demonstrate how to build a Profile GPT using Lang Flow. The Profile GPT can be trained on resumes and used to answer HR-related questions. This application can provide information about employees' Current roles, packages, technologies, and more. By utilizing the Profile GPT, HR departments can streamline their information search process.

By following this tutorial, you will learn how to customize large language models using Lang Chain and train GPT models on your own custom data sets. We will also explore low code/no code strategies to simplify the implementation process. Let's get started!

Article

Customizing Large Language Models with Lang Chain

Large language models have revolutionized the field of natural language processing (NLP). With the release of models like GPT, developers now have the opportunity to train their own models using custom datasets. This tutorial will introduce you to a powerful framework called Lang Chain, which provides various modules for customizing and building upon existing large language models.

Understanding Lang Chain

At its Core, Lang Chain is a framework that offers a set of modules designed to enhance and customize large language models. These modules include models, prompts, memory, indexes, chains, and agents. Models refer to the actual large language models such as Hugging Face LLM or OpenAI LLM, which serve as the backbone of Lang Chain. Prompts allow users to create reusable templates for querying the model, making the querying process more efficient. Memory is a vital component for preserving conversation context over an extended period of time, ensuring a continuous flow of information. Indexes, though not extensively covered in this tutorial, provide a way to organize and retrieve specific elements. Chains, on the other HAND, are used to link multiple language models together, enabling seamless integration into your NLP pipeline. Lastly, the agents in Lang Chain are tools that provide direct access to various functionalities within the framework.

Training GPT Models Using Custom Data Sets

One of the main objectives of this tutorial is to demonstrate how to train GPT models using custom data sets. With Lang Chain, this process becomes straightforward and efficient. By utilizing the modules provided, developers can train GPT models using their own custom data, allowing them to tailor the models to their specific needs.

Creating Text Chunks and Overlaps

To train GPT models effectively, it is crucial to break down large text documents into smaller, manageable chunks. Lang Chain offers a text splitter module that enables users to split the text into chunks of their desired size. Additionally, the framework allows overlap between these chunks, ensuring that no crucial information is lost. Balancing the chunk size and overlap is essential to maintain context and optimize training results.

Generating Embeddings

Embeddings play a fundamental role in large language models, and Lang Chain provides a straightforward way to generate them. Embeddings are lower-dimensional representations of words or phrases in a text document. By converting text into vectors, Lang Chain enables efficient storage and comparison of data. These embeddings serve as the foundation for semantic search, allowing users to find documents or pieces of information related to their queries.

Storing and Querying Embeddings

Storing and querying embeddings is a critical step in utilizing large language models effectively. Lang Chain provides integration with vector stores like Chroma DB, allowing users to store and retrieve embeddings seamlessly. By comparing vectors using techniques like Cosine similarity, Lang Chain enables users to find Relevant documents or answers to their queries. This process is known as semantic search, which enhances the accuracy and efficiency of information retrieval.

Implementing Lang Flow with Low Code/No Code Strategy

Building on large language models may seem challenging to those with limited coding experience. However, Lang Chain offers a solution in the form of Lang Flow, a graphical user interface (GUI) that enables users to implement Lang Chain without extensive coding. With Lang Flow, users can visually design their models by dragging and dropping components, creating a chain of actions to build their own customized GPT models. This low code/no code strategy empowers developers to leverage the power of large language models without the need for advanced coding skills.

Building a Profile GPT using Lang Flow

To illustrate the capabilities of Lang Chain and Lang Flow, let's create a Profile GPT. This application will be trained on resumes and will allow HR departments to extract relevant information from employee profiles efficiently. HR personnel can ask questions about an employee's current role, salary Package, technology stack, and more. By training the GPT model on resumes, HR departments can streamline their information retrieval process, saving time and resources.

With Lang Chain and Lang Flow, the process of customizing large language models becomes more accessible and efficient. By following this tutorial, you will be equipped with the knowledge and tools needed to train GPT models on your own custom data sets. Additionally, the low code/no code strategy offered by Lang Flow opens up possibilities for developers with limited coding experience. Start exploring the world of customized language models today with Lang Chain and Lang Flow!

Highlights

• Customizing large language models with Lang Chain
• Understanding the modules in Lang Chain: models, prompts, memory, indexes, chains, and agents
• Training GPT models using custom data sets
• Creating text chunks and overlaps for effective training
• Generating embeddings and storing them in a database
• Querying embeddings for semantic search using vector stores like Chroma DB
• Implementing Lang Flow with a low code/no code strategy
• Building a Profile GPT using Lang Flow for HR-related tasks
• Simplifying the customization process with Lang Chain and Lang Flow
• Enhancing language models without extensive coding knowledge

FAQ

Q: What is Lang Chain? A: Lang Chain is a framework that provides modules for customizing and building upon existing large language models like GPT. It offers functionalities such as prompts, memory, indexes, chains, and agents.

Q: Can I train GPT models using my own data sets? A: Yes, with Lang Chain, you can train GPT models using your own custom data sets. The framework provides modules and tools to streamline the training process.

Q: How do I create text chunks and overlaps for training? A: Lang Chain offers a text splitter module that allows you to break down large text documents into smaller chunks. You can specify the size of the chunks and the overlap between them.

Q: What are embeddings? A: Embeddings are lower-dimensional representations of words or phrases in a text document. They enable efficient storage and comparison of data, forming the basis for semantic search.

Q: Can I store and query embeddings using Lang Chain? A: Yes, Lang Chain integrates with vector stores like Chroma DB to store and retrieve embeddings. You can compare embeddings to find relevant documents or answers to queries.

Q: What is Lang Flow? A: Lang Flow is a graphical user interface (GUI) provided by Lang Chain. It allows users to implement Lang Chain without extensive coding, enabling them to visually design their own customized language models.

Q: How can I build a Profile GPT using Lang Flow? A: By training the GPT model on resumes, you can create a Profile GPT using Lang Flow. This application can answer HR-related questions about employee profiles, streamlining the information retrieval process for HR departments.