Build a Document Summarization App using LLM on CPU: No OpenAI ❌

In this AI Anytime video, the host guides viewers through the process of building a document summarization application using the open-source Laminiflang T5 language model, which has 248 million parameters. The application is developed without reliance on OpenAI, avoiding the need for API keys. The video demonstrates how to utilize the summarization pipeline from the Hugging Face repository to create a Streamlit application. The host also discusses the potential of the Muhammad bin Zayed University of Artificial Intelligence and its contribution to the fine-tuned model. The video concludes with a live demonstration of the application summarizing a PDF document, showcasing the capabilities of the Laminiflang T5 model for text summarization on a local CPU machine.

Takeaways

• 🌟 The video demonstrates building a document summarization application using the open-source language model 'Lamima Flan T5' with 248 million parameters.
• 🚀 The application is developed using Streamlit, a framework for building data applications quickly without the need for web expertise.
• 📚 The model 'Lamima Flan T5' is fine-tuned on 'Flan T5', a language model by Google, and is considered underrated but effective for summarization tasks.
• 🔍 The video covers the process of setting up the summarization pipeline using the Hugging Face library and the model's capabilities for text generation.
• 💻 The tutorial includes instructions for downloading and setting up the model locally to avoid reliance on the internet or external APIs.
• 🔑 The importance of the 'device map' and 'torch dtype' parameters for optimizing model performance on different hardware configurations is highlighted.
• 📁 The script discusses the use of LangChain for document loading and preprocessing, emphasizing its ease of use and comprehensive documentation.
• 🔍 The video provides a step-by-step guide to creating the summarization function, including handling file uploads and integrating the model's output.
• 🖼️ The application features a PDF viewer within the Streamlit app to display the uploaded document alongside the generated summary.
• 🔌 The video mentions future plans to extend the application with FastAPI to create a more robust API and possibly a web application using React.
• 🔍 The presenter also discusses the potential for using vector databases like ChromaDB for text generation tasks, emphasizing their advantages for embedding storage and retrieval.

Q & A

What is the main purpose of the application being built in the video?

The main purpose of the application is to summarize documents using a language model called Lamini Flan T5, which is an open-source model with 248 million parameters.

Why is the Lamini Flan T5 model chosen for this application?

The Lamini Flan T5 model is chosen because it is an open-source model that does not require reliance on OpenAI or any API keys, and it is fine-tuned on Google's Plan T5, which is considered an underrated yet effective language model.

What are some of the other open-source models mentioned in the video?

Other open-source models mentioned include Llama, Alpaca, and Dolly, which have been released by different organizations and research groups.

What is the significance of Mohammed bin Zayed University of Artificial Intelligence (MBZUAI) in the context of this video?

MBZUAI is significant because they have fine-tuned the Lamini Flan T5 model used in the application. The university is also highlighted for its potential and the quality of its AI research and faculty.

How does the video guide the viewer in setting up the document summarization application?

The video guides the viewer through the process of using the Lamini Flan T5 model with a summarization pipeline, setting up the application using Streamlit, and handling file preprocessing with the help of Langchain.

What is Langchain and how is it used in the application?

Langchain is a library used for handling text processing tasks such as splitting text in documents. It is used in the application for file loading and pre-processing before passing the text to the Lamini Flan T5 model for summarization.

What are some of the key features of the Lamini Flan T5 model mentioned in the video?

Key features of the Lamini Flan T5 model include its ability to perform summarization and text generation, and it being fine-tuned on large-scale instruction datasets.

How does the video address the issue of model size and computational resources?

The video acknowledges that the Lamini Flan T5 model is not as large as some other models, which makes it suitable for running on a local CPU machine without requiring extensive computational resources.

What is the role of the Streamlit framework in building the application?

Streamlit is a web framework used in the video to quickly create the data science application for document summarization. It simplifies the process by not requiring web technologies expertise.

What are some potential next steps or extensions to the application discussed in the video?

The video suggests creating an API using FastAPI and possibly a web application with React for a more polished user interface. Additionally, the text generation pipeline of Lamini could be explored for other applications.

Outlines

00:00

🚀 Introduction to Creating a Streamlit Application for Document Summarization

The video begins with an introduction to a project aimed at creating a Streamlit application for summarizing documents. The application will utilize a language model called LaminFlan T5, which has 248 million parameters. The host highlights the open-source nature of the model, contrasting it with other large language models like Llama and Alpaca. The focus is on using LaminFlan T5, a fine-tuned model by Google's Plan T5, which is considered underrated in the community. The video promises to demonstrate how to leverage this model to build the application without relying on any APIs or keys, and mentions future plans to create an API with FastAPI and possibly a web app using React.

05:01

🔍 Exploring the LaminFlan T5 Model and Setting Up the Project

The host discusses the LaminFlan T5 model in more detail, mentioning its fine-tuning on Plan T5 and the availability of various pipelines, such as summarization and text generation. The video then shifts to practical setup, where the host explains how to download and store the model files locally for privacy and independence from the internet. The host also introduces the LangChain library for text processing and the use of a PDF file for demonstration purposes. The video outlines the necessary dependencies and sets up the environment for building the application, including the installation of relevant Python libraries.

10:02

📚 Discussing the LaminFlan T5 Model's Background and Features

The script delves into the background of the LaminFlan T5 model, highlighting its development by the Muhammad bin Zayed University of Artificial Intelligence in the United Arab Emirates. The model is part of the Lamin series, with the video focusing on the 248 million parameter model. The host expresses admiration for the university's AI research and educational programs, suggesting it as a potential choice for those interested in pursuing a master's degree in AI-related fields. The video also discusses the model's capabilities and the importance of using language models with fewer parameters for easier setup on local machines.

15:04

🛠️ Building the Application: Importing Libraries and Setting Up the Model

The host begins coding the application by importing necessary libraries and setting up the LaminFlan T5 model. The focus is on using the summarization pipeline from LangChain and the T5 tokenizer and model from the Transformers library. The video demonstrates how to load the model and tokenizer with specific parameters, such as device mapping and data types, to ensure the model runs efficiently on a CPU machine. The host also discusses the importance of having the model files locally cached for faster access and offline use.

20:05

📝 Implementing File Preprocessing and Summarization Pipeline

The video continues with the implementation of a file preprocessing function that utilizes LangChain for loading and splitting text from a PDF document. The host details the process of defining a chunk size for text splitting and mentions the possibility of extending the application to handle different file formats. The summarization pipeline is then set up using the LaminFlan T5 model, with parameters for maximum and minimum length of the summary text. The host emphasizes the ease of using LangChain for handling text processing tasks.

25:07

🖥️ Developing the Streamlit Interface for Document Upload and Summarization

The host describes the development of the Streamlit interface, starting with setting up the page layout and configuration for a wide view. The main function is outlined, which includes a title for the application and a file uploader for PDF documents. The video demonstrates how to create a button that, when clicked, will trigger the summarization process. The host also discusses the use of Streamlit columns to organize the UI, showing the uploaded PDF on one side and the summarization result on the other.

30:08

🔧 Debugging and Finalizing the Streamlit Application

The host encounters an error while running the Streamlit application, which is resolved by correcting the import statement for the T5 tokenizer. The video then shows the application running successfully, with the ability to upload a PDF file and click a button to generate a summary. The host explains the process flow, from uploading the file to displaying the PDF and finally showing the summary generated by the LaminFlan T5 model. The video emphasizes the open-source nature of the project and the potential for further extension of the application.

35:10

📈 Demonstrating the Summarization Process and Discussing Model Capabilities

The host demonstrates the summarization process by uploading a PDF file and clicking the summarize button. The video shows the application processing the file and generating a summary, which is displayed on the UI. The host discusses the capabilities of the LaminFlan T5 model, noting its ability to perform abstractive summarization and paraphrasing. The video also touches on the potential for crashes when processing large files on a CPU machine and suggests that the code will be available on GitHub for further exploration and extension.

40:11

🌟 Wrapping Up and Future Plans for the Project

The host concludes the video by summarizing the work done, which includes building a Streamlit application that can summarize documents using the LaminFlan T5 model. The host expresses enthusiasm for the capabilities of the model and the application, and invites viewers to extend the project further. The video also mentions future plans to create an API and a web application for a more polished user experience. The host encourages viewers to like, subscribe, and share the video, and looks forward to the next project.

🔑 Advantages of Using Vector Databases for Text Generation and Future Content

In the final paragraph, the host discusses the advantages of using vector databases for text generation tasks, such as default similarity search and faster retrieval of embeddings. The host suggests that vector databases like ChromaDB, Faiss, and Elasticsearch are beneficial for creating conversational AI and text generation applications. The video ends with a reminder that the host focuses on creating project-based videos powered by recent language models and technologies, and invites viewers to engage with the content by liking, subscribing, and sharing.

Keywords

Document Summarization

Document summarization refers to the process of condensing a large document into a shorter version while retaining the key points and essence of the original text. In the context of the video, this is the main functionality of the application being developed, which uses a language model to generate summaries of uploaded documents, thereby providing a quick and efficient way to grasp the main ideas without reading the entire content.

Streamlit

Streamlit is an open-source library used to create custom web apps for machine learning and data science. In the video, the author utilizes Streamlit to build the user interface for the document summarization application, allowing users to upload documents and receive summaries through a web-based platform.

Lamini Plan T5

Lamini Plan T5 is a language model mentioned in the video that has been fine-tuned on the Flan T5 model, which is an underrated language model by Google. The model has 248 million parameters and is used in the video to perform the summarization task. It is an open-source alternative to relying on commercial APIs like OpenAI, and it is capable of understanding and generating human-like text.

Language Model

A language model is a type of artificial intelligence model that is trained on a large corpus of text and can generate human-like language. In the video, the Lamini Plan T5 language model is employed to understand and summarize documents. It is a key component of the application, demonstrating the capability of such models to process and generate text based on provided input.

Open Source

Open source refers to software whose source code is available to the public, allowing anyone to view, use, modify, and distribute the software freely. The video emphasizes the use of open-source models like Lamini Plan T5, which means that developers can build applications without relying on proprietary APIs or paying for usage, promoting accessibility and community contribution.

API Keys

API keys are unique identifiers used to authenticate requests to an API (Application Programming Interface). In the context of the video, the author mentions not needing API keys to build the application, as they are using open-source models and not relying on services like OpenAI, which typically require API keys for access.

Hugging Face

Hugging Face is a company that provides a platform for developers to share and collaborate on state-of-the-art natural language processing models. In the video, the author refers to Hugging Face as the platform where the Lamini Plan T5 model is hosted, and it is used to access the model for the summarization application.

Fine-tuning

Fine-tuning is a technique in machine learning where a pre-trained model is further trained on a specific task to improve its performance on that task. The Lamini Plan T5 model mentioned in the video is an example of a fine-tuned model, which has been trained on the Flan T5 base model and then further trained on a specific dataset to perform summarization.

Pipelines

In the context of machine learning and natural language processing, pipelines refer to a sequence of processing steps that data goes through. The video mentions the use of pipelines for summarization and text generation, which are part of the Lamini Plan T5 model's capabilities. These pipelines allow for the structured processing of text data to produce summaries or generate new text.

Fast API

Fast API is a modern, fast web framework for building APIs with Python. Although not directly used in the current application shown in the video, the author mentions plans to use Fast API to create an API for the document summarization application in a future video. Fast API is known for its performance and ease of use, making it a suitable choice for building APIs.

Highlights

Introduction of a streamlit application to summarize documents using the Lamini Plan T5 language model with 248 million parameters.

The application is completely open source and does not rely on OpenAI or API keys.

Lamini Plan T5 is an underrated language model fine-tuned on Google's Plan T5.

The video will demonstrate leveraging the summarization pipeline directly from Hugging Face.

Mention of the Mohammed bin Zayed University of Artificial Intelligence as a leading institution in AI research and education.

The university's development of the fine-tuned Lamini model used in the application.

The model contains 2.58 samples for instruction fine-tuning.

The application will be built using Streamlit, focusing on Lamini Plan T5 for summarization.

Explanation of how to load the model and tokenizer locally for privacy and independence from the internet.

Use of LangChain for text splitting and document loading, with a preference for its excellent documentation.

The process of creating a Streamlit application with a user-friendly interface for document summarization.

Demonstration of uploading a PDF file and using the summarization function to generate a summary.

The ability of the Lamini Plan T5 model to perform abstractive summarization and paraphrasing.

Potential for extending the application to include text generation pipelines and other file formats.

Future plans to create an API and web application using FastAPI for a more polished user experience.

Emphasis on the importance of using vector databases like ChromaDB for text generation and embeddings.

Conclusion and invitation for collaboration on extending the application and creating more project-based videos.