20+ LLM Project Ideas For Data Wizards To Try in 2024

All the data science folks out there- join us as we explore exciting LLM project ideas that every aspiring data scientist must add to their resume in 2024!

LLM Project to Build and Fine Tune a Large Language Model

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Building Large Language Model projects isn't just about creating cool and fascinating applications—it's about expanding your horizons and skills as a data scientist. These projects allow you to tap into the magical abilities of LLMs, transforming raw data into valuable insights and revolutionizing how you interact with language. Before jumping on to the LLM project ideas, you must remember that all data science and ML professionals must try to gain some hands-on experience they can add to their resume. You can check out several platforms offering data science and ML project solutions, like GitHub and ProjectPro, to prepare a list of LLM project topics you can try. Working on these industry-level LLM project ideas will give you a taste of the real-world business scenarios and challenges you may encounter in your data science journey.

Now, are you ready to explore the top LLM project ideas? Let’s get started!

20+ Unique LLM Project Ideas For Practice in 2023

This LLM projects tutorial will list over 20 interesting LLM projects for data science and machine learning professionals to try their hands on in 2023.

LLM Projects With Source Code

Let us explore a few interesting LLM projects from GitHub and their source code that will help you practice these large language model project ideas confidently and excel in AI/ML-

1. Email Generator

Tired of staring at a blank screen, struggling to come up with interesting email content? Fear not! With LLMs, you can build an email generator that takes a few prompts and magically generates engaging and personalized emails. By training an LLM on a vast corpus of emails, you can leverage its language generation capabilities to save time and streamline your communication.

Tip: Consider using the GPT-3 model by OpenAI or other similar LLMs like GPT-2 or T5. You can use natural language processing libraries like spaCy or NLTK to preprocess and generate email drafts. Check out this Email Generator using GPT-3 to understand how to build an exciting project using LLM that generates convincing-looking emails and sends them over Gmail.

2. Question-Answering System

Ever wished you had a personal assistant to answer all your questions? With LLMs, you can build your question-answering system! Train an LLM on a vast corpus of knowledge, such as Wikipedia or domain-specific data, and develop a system that can provide accurate and informative answers to user queries.

Tip: Models like BERT, ALBERT, or T5 can be finetuned for question-answering tasks. You can also combine them with techniques like tokenization, attention mechanisms, and retrieval methods to build robust question-answering systems. You can refer to this GitHub repository 'OnPoint', which is an application of the open-source XL-Net model on product user review-based question-answering service.

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3. Text Summarization

Summarizing lengthy documents or articles can be time-consuming. But fear not! LLMs can automate this process for you. Build a text summarization tool that takes in a long piece of text and generates concise summaries that capture the key information. By leveraging LLMs trained on summarization tasks, you can save time and quickly extract the essence of large volumes of text.

Tip: Start by exploring models like BART, T5, or Pointer-Generator Networks for text summarization. You can also use techniques like attention mechanisms, beam search, and abstractive or extractive summarization methods to create accurate and relevant summaries. Check out some interesting text summarization LLM projects on GitHub, such as the ‘News Article Text Summarizer’ that involves extractive and abstractive text summarization of news articles using the T5 (Text-To-Text Transfer Transformer) model and text ranking algorithms.

4. Fake News Detection

In the era of misinformation, addressing fake news is crucial. Develop a fake news detection system using LLMs that can automatically analyze news articles, social media posts, or other textual sources to determine the likelihood of the content being false or misleading. By training an LLM on labeled datasets, you can significantly contribute to the fight against fake news and promote reliable information.

Tip: Use labeled datasets and fine-tune LLMs like BERT, RoBERTa, or XLNet for fake news detection. You can combine them with techniques like feature engineering, model ensembles, or rule-based approaches to build effective fake news detection systems.

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Open-Source LLM Projects You Must Explore

5. Chatbots

Bring your customer service to the next level with LLM-powered chatbots! Develop an interactive chatbot to engage in natural language conversations, assist users with complex tasks, and answer frequently asked questions. You can create chatbots that offer personalized experiences and simulate human-like interactions by training an LLM on conversational data.

Tip: You can build chatbots using LLMs like DialoGPT, GPT-2, or Seq2Seq models. You can employ libraries such as Rasa, ChatterBot, or TensorFlow's Seq2Seq to handle the chatbot's dialogue management and integrate it with APIs or frameworks for deployment. Get inspired by this LLM open-source project, 'ChatLLaMA', an open-source implementation for LLaMA-based ChatGPT runnable in a single GPU with a 15-times faster training process than ChatGPT.

List of LLM Chatbot Project Examples for Practice

Check out the following chatbot LLM-based projects and build your own LLM chatbot leveraging various tools and technologies-

• Build a Conversational Chatbot With Langchain and LLMs

In this LLM project sample, you will learn how to build a conversational chatbot using Langchain, linking users' data to powerful Large Language Models like GPT 3.5 and HuggingFace Instructor X1. This chatbot will let users ask questions about multiple PDF documents and get relevant answers using conversational retrieval methods. To do this, you will leverage the power of AI by building a customizable and user-friendly Streamlit application.

The process starts by extracting text from the PDF files and dividing it into chunks. This step is vital because the embedding models have a token limit. Then, you will transform this text data into digital form, stored as bits and bytes, so the models can understand it. This digital database serves as a knowledge hub for our application, serving as a reference when answering users' queries. Whenever a user asks a question, you will use an embedding model to convert the question into a format the system understands. Then, utilizing the LLMs, you can provide the best possible response by using semantic search to determine which text chunks in the database contain relevant information.

• Build A PDF-Chat App Using LangChain, OpenAI, and Streamlit

This project will teach you how to use a ChromaDB vector store in a Langchain setup to develop a PDF Chat application. The main aim of this app is to let users load a specific PDF file and ask questions about it, with LangChain and OpenAI API working together to find precise answers from the PDF. 

First, you will set up the development environment and then create the user interface using Streamlit, a popular Python library for building interactive web apps. Next, you will integrate LangChain and the OpenAI API to handle PDF processing and generate helpful answers to user questions. You will explore setting up the OpenAI API, processing PDF files, and managing prompts to provide accurate and context-aware responses. This project offers a hands-on opportunity to learn how to build an app fetching relevant information from PDFs using Langchain and OpenAI.

Source: PDF-Chat App Using LangChain, OpenAI, and Streamlit

• Build A Veterinary Chatbot Using Llama 2 For Pet Care

This project is an exciting journey into creating a chatbot tailored for veterinary doctors. Using cutting-edge open-source tools and an LLM, you will explore the world of Generative AI to bring this chatbot to life. First, you will use the Multilingual E5 Large Embeddings Model to create document embeddings, while the FAISS vector store will help manage the data efficiently.

You will leverage Llama 2 by Meta AI, a robust large language model, for the chatbot's intelligence. The backend will run on FastAPI to ensure smooth user-bot communication, while LangChain will handle prompts and chains. To equip the chatbot with knowledge, you will tap into a comprehensive dog pet care encyclopedia as a data source. Additionally, you will explore vet case studies, enriching the chatbot with real-life scenarios for making informed decisions. This project offers hands-on experience in developing a chatbot that assists veterinary doctors with vast pet care knowledge and real-world scenarios.

Source: Veterinary Chatbot Using Llama 2 For Pet Care

LLM Project Ideas In Retail And E-Commerce

Below are a few innovative LLM project topics associated with the retail and e-commerce industry that will help you understand how these businesses benefit from LLMs-

6. E-commerce Product Search Relevance

The product search relevance system ensures that search results are more accurate and relevant by interpreting user intent and product information. This further boosts customer satisfaction and increases conversion rates for e-commerce companies. E-commerce platforms can offer customers a seamless and satisfying search experience by integrating data science with cutting-edge technology in this exciting project.

Tip: You can develop an innovative e-commerce product search relevance system using Databricks' Dolly LLM to improve the accuracy and effectiveness of product search results. You can refer to this Enhancing Product Search blog before you leverage the power of Dolly to understand user search queries and product descriptions, enabling semantic search and relevance ranking. You can use the Wayfair Annotation Dataset (WANDS) that offers descriptive text for 42,000+ products on the Wayfair website and 233K labeled results generated from 480 searches. 

7. Sentiment Analysis

Understanding customer sentiment is crucial for businesses. With LLMs, you can develop a sentiment analysis tool that automatically analyzes text data, such as customer reviews or social media posts, and classifies them into positive, negative, or neutral sentiments. Training an LLM on labeled sentiment datasets can offer valuable insights to businesses, enabling them to enhance customer satisfaction.

Tip: You can fine-tune models like BERT, RoBERTa, or DistilBERT for sentiment analysis using libraries like Hugging Face's Transformers. You must combine the power of LLMs with pre-processing techniques and machine-learning algorithms to build accurate sentiment classifiers. Work on this Sentiment Analysis with Deep Learning using pre-trained BERT transformer large-scale language learnings and smile annotations dataset using PyTorch Framework.

8. Product Recommendation System

LLMs help in product recommendation use cases by analyzing vast amounts of textual data, including user reviews, product descriptions, and browsing behavior. They can understand user preferences, identify patterns, and make accurate predictions, leading to personalized and relevant recommendations that optimize the user experience and drive higher engagement and sales. So, you get super personalized recommendations that improve your shopping experience, and companies sell more stuff! A win-win situation for all!

Tip: To build a product recommendation system, consider using popular large language models like GPT-3, BERT, or RoBERTa. You can use Python, TensorFlow, or PyTorch for model training. Preprocess and vectorize data, use collaborative filtering, and combine user behavior with language model outputs for personalized recommendations. You must evaluate the system with metrics like RMSE or AUC and continuously collect user feedback to improve and iterate the recommendations.

LLM Project Ideas In Finance

Below are a few innovative LLM project topics associated with the finance industry that will help you understand how financial organizations benefit from LLMs-

9. Stock Market Trend Prediction

This LLM project in Finance is ideal for all the Wall Street Wizards and Market Magicians! Ever wondered what it would be like to predict stock market trends like a pro? Well, it's time for you to dive into the thrilling world of stock market prediction with LLMs and put your data science skills to the test.

Tip: You can develop a stock market trend prediction system using the BloombergGPT LLM to analyze financial news, company reports, and social media data. The BloombergGPT can quickly implement sentiment analysis and topic modeling techniques to identify critical stock price factors. You can use Python, NLP libraries (NLTK, spaCy), and machine learning algorithms for predictive modeling. To build this project, you can leverage financial news articles from popular and reliable sources like Bloomberg, CNBC, and Wall Street Journal.

10. Credit Card Fraud Detection

Ever wished you could protect credit card users from sneaky cyber criminals? Get ready to join the frontlines of defense against credit card fraud with powerful LLMs that can sniff out fraudulent transactions like a pro! In this thrilling LLM project in Finance, you will leverage the magical abilities of LLMs to analyze transaction data and user behavior to stay one step ahead of those crafty fraudsters.

Tip: You can build a fraud detection system for credit card transactions using the BERT LLM to analyze transaction data and user behavior. You must implement anomaly detection algorithms and sequence modeling techniques to identify fraudulent patterns. For model development, you can use Python, NLP libraries, and machine learning frameworks (TensorFlow or PyTorch).

11. Credit Risk Assessment

Credit Risk Assessment is crucial in the financial industry, as it enables lenders to evaluate the creditworthiness of borrowers and assess the probability of default. Accurate credit risk assessment is crucial for making informed lending decisions, optimizing loan portfolios, and minimizing potential losses. By leveraging Large Language Models (LLMs) like BERT or GPT-3, you can build a Credit Risk Assessment project that dives into customer data, credit histories, and financial records to predict credit risks accurately.

Tip: In this exciting project, you will delve into the realm of credit risk assessment using the power of LLMs like BERT or GPT-3. These LLMs have shown exceptional capabilities in understanding textual data, making them ideal for analyzing creditworthiness. You will use these linguistic wizards to process vast customer data, financial records, and credit histories. By integrating the insights from LLMs with traditional credit risk models  (e.g., logistic regression, decision trees), you can enhance the accuracy and efficiency of credit risk assessment, enabling financial institutions to make informed lending decisions.

Best LLM Projects For Beginners

Here are some simple LLM projects for beginners who are willing to gain hands-on experience with LLMs-

12. Supercharging Smaller Open-source LLM Using ChatGPT Data

This project showcases an innovative way to enhance the LLama-2 model's performance, allowing it to understand better and generate insights about products by leveraging specific datasets and efficient fine-tuning techniques. This fascinating LLM project idea aims to enhance the performance of the LLama-2 language model by fine-tuning it using a Databricks notebook, making it more efficient and versatile. The method involves tweaking and optimizing the base model's capabilities for a specific job using a dataset generated by ChatGPT- InstaCart's top 500 products list.

For the fine-tuning task, you must prompt the model to provide three advantages and disadvantages for several products. You will use the 'peft' library, which streamlines the fine-tuning process for LLMs. Once the model is trained and fine-tuned, it will be used for inferencing, comparing the results obtained before and after the LLama-2 model's fine-tuning.

Source- youtu.be/paGr-t1wSOQ and youtu.be/lo11Iczb0Vc

13. Finetuning Base LLMs Using Custom Dataset

This unique LLM project allows you to understand the Falcon model’s flexibility and potential for real-world applications, such as e-commerce. This unique project aims to explore the Falcon LLM, showcasing its exceptional features and innovative techniques like LORA (Low-Rank Adapters) and multi-query attention. We will start by exploring the extensive RefinedWeb data Falcon is trained on, examining its scope and extent, contributing to its exceptional performance.

Furthermore, we will use the Instacart E-commerce Dataset, demonstrating how Falcon can be finetuned using this real-world data. By leveraging a freely available single-node GPU on Colab, we will learn how Falcon can be customized for specific datasets, particularly in e-commerce and other fields.

Source- ​​youtu.be/CxqZ5j3xlt0 and youtu.be/8cc4bJtycOA

14. Multimodal Content Generator with OpenAI

Multimodal content generation apps can seamlessly handle different data inputs, such as images, audio, and text, to produce creative and informative content. Such apps can find applications in various domains, including content creation, multimedia production, education, and entertainment.

Tip: You can develop this innovative multimodal content generation app by integrating OpenAI's language models, such as GPT-3 or later versions. You can use Python for backend development and data preprocessing and libraries like TensorFlow or PyTorch for handling image and audio data. Additionally, datasets like ImageNet, ESC-50, and text sources can help you train and test the app's functionalities across different modalities.

15. Movie Recommendation System

Let us build an intelligent movie buddy that suggests awesome movies based on what you love! Using a Large Language Model (LLM), our system will understand your movie preferences and recommend new ones you will enjoy. Imagine having a movie expert friend always ready with the perfect film suggestion for you!

Tip: You will use Python to create the system and libraries like TensorFlow or scikit-learn to make it smart. You will further dive into Natural Language Processing tools like NLTK or spaCy to understand movie data better. Use datasets like MovieLens, IMDb, and Netflix Prize for your movie info, packed with details about movies, ratings, genres, and more.

16. Resume Evaluator

This LLM project sample aims to help you create the perfect resume that stands out to potential employers. Using an LLM model, your tool becomes a personal resume advisor, giving suggestions to enhance your resume and boost the chances of landing your dream AI job! It's like having a resume expert guiding you to create a winning career pitch!

Tip: You will use Python and libraries like TensorFlow or scikit-learn to build the evaluation system. Leveraging Natural Language Processing tools such as NLTK or spaCy will enhance your understanding of resume content and structure. You will use public datasets containing various resume samples with details about skills, experiences, and qualifications. These datasets will be the foundation for training evaluators to offer meaningful feedback and suggestions for improving resumes, helping you stand out to potential employers.

17. YouTube Script Writing Assistant Using LangChain And Streamlit

This LLM project sample will guide you through building a tool to generate YouTube scripts using LangChain and Streamlit, easing the process for YouTube creators to craft their video content quickly. The application generates the video title and script by providing a topic, saving creators valuable time. You will design a user-friendly interface with Streamlit, ensuring a smooth experience for creators. Using prompt templates and chat history storage, creators can easily customize the generated scripts to suit their specific requirements. The language model doesn't just create the script and generate the video title, offering a comprehensive solution for YouTube creators. This tool streamlines the script-writing process, enabling creators to focus more on generating engaging video content for their audience.

Source: YouTube Script Writing Assistant Using LangChain And Streamlit

18. Build a Podcast Summarization App with Whisper Jax, Azure OpenAI, And Langchain

This project will help you learn how to build a Lex Fridman Podcast Summarization App using powerful technologies like Whisper Jax for fast transcription, Langchain for adaptable prompt templates, and the Azure OpenAI GPT-3.5 Turbo Model as a robust language tool. By integrating these advanced tools, you will discover how to transcribe and summarize podcast episodes automatically and precisely, revolutionizing our interaction with audio content through AI-driven summarization.

With the ability to set prompts for LinkedIn, you can easily share these summaries on professional platforms, allowing for quick and efficient dissemination of insightful podcast highlights. This project offers a hands-on opportunity to explore the latest AI technologies and their application in automating the summarization of podcast content for broader accessibility and engagement.

19. Article/Blog Generation App Using Llama2, Langchain, And Pexels

This project guides you in creating an Article/Blog Generation App using the powerful Llama2 model. You will explore how to develop high-quality content effortlessly, tapping into the advanced abilities of the open-source Llama2 model. Additionally, you will learn to incorporate eye-catching images from Pexels using their API.

The app will have a user-friendly interface built with Streamlit, ensuring a smooth and easy experience. One notable feature is the ability to export your work as a DOCX file, allowing you to tweak further and refine your content. This project opens doors to a new world of content creation by enabling seamless and high-quality content generation for articles and blogs. It's an exciting opportunity to dive into the world of AI-powered content creation, making the process smoother and more efficient.

20. Deploy Llama 2 on AWS SageMaker using DLC (Deep Learning Containers)

This project solution is a step-by-step guide to smoothly deploying the Llama2 large language model on AWS SageMaker using Deep Learning Containers (DLC). Whether you are just starting with Generative AI or already have experience, this project will help you better understand the world of LLMs.

You will explore accessing pre-built DLC images, simplifying the setup process. Then, you will learn to configure SageMaker to deploy the Llama2 model, making it accessible for various tasks, regardless of your expertise level in the field of Generative AI. This project offers a user-friendly approach to deploying advanced language models on AWS SageMaker, ensuring a smooth experience for beginners and experienced professionals in the field. Working on this project enables you to dive into the world of deploying LLMs on the cloud, leveraging the power of AWS SageMaker for your AI projects.

Bonus LLM Project Idea: Hospital Readmission Prediction System

LLMs can potentially transform medical practice in numerous ways, including improving diagnostic accuracy, predicting disease progression, and supporting clinical decision-making. By analyzing large medical data, LLMs can develop specialized knowledge in different medical fields, such as radiology, pathology, etc. One such model is the ‘ClinicalBERT,’ fine-tuned on the MIMIC-III dataset of EHRs from intensive care unit patients, demonstrating enhanced performance in clinical NLP tasks, including patient mortality prediction and diagnosis classification.

Tip: In this beneficial healthcare project, you will leverage the ClinicalBERT, a specialized LLM designed for medical text analysis. This project aims to predict hospital readmission risk by evaluating clinical notes and enabling healthcare providers to use proactive patient management strategies. By leveraging ClinicalBERT and the insights from EHR data containing clinical notes, patient demographics, medical history data, hospital readmission records (binary classification labels), etc., this project will help you identify high-risk patients who might require additional care, leading to improved patient outcomes and reduced healthcare costs.

Remember to choose the right LLM architecture for your specific project, whether GPT-3, BERT, RoBERTa, or similar models. Leverage pre-processing techniques, natural language processing libraries, and other relevant tools to preprocess the data and integrate the LLMs into your data science projects. By combining the magic of LLMs with your creativity and problem-solving skills, you will create applications that showcase the true potential of data science tools and technologies.

Bring Your LLM Project Ideas To Life With ProjectPro

Large Language Models (LLMs) stand as the towering gateways to revolutionary data science and machine learning adventures! But theory alone won't suffice when it comes to mastering LLMs. You must gain hands-on experience by working on a wide range of LLM projects that will help you understand the application of LLMs in the real world. ProjectPro provides over 270 innovative and unique end-to-end solved reusable project templates in data science and machine learning specifically designed to suit various real-world use cases. Working on these industry-level projects from the ProjectPro repository will help you deepen your understanding of various aspects of AI and ML and gain valuable insights into their capabilities and limitations to solve real-world challenges.

So, pick your favorite project idea, gather your tools, and go on this exciting journey of building LLM-powered applications with ProjectPro. Happy coding, and may the LLM magic be with you!

FAQs on LLM Project Ideas

1. How do I use an LLM in my project?

To use a Large Language Model (LLM) in your project, follow these steps-

• First, choose a suitable LLM like GPT-3, BERT, or Llama2 based on your project needs. Then, access the model through platforms like Hugging Face or OpenAI API. 

• Next, fine-tune the model on your specific data, if required, to improve its performance for your task.

• Finally, integrate the LLM into your project by utilizing its API or libraries for your programming language, enabling text generation, summarization, or other NLP tasks as needed.

2. What is a LLM project?

A Large Language Model (LLM) project involves leveraging advanced language models like GPT-3, BERT, or others to solve complex NLP tasks. These projects focus on leveraging the features of these models for various applications such as text generation, summarization, sentiment analysis, chatbots, and more. LLM projects often involve fine-tuning models on specific data, deploying them in applications, and exploring their abilities to enhance language-related tasks across various domains.

3. What are the LLM Frameworks?

LLM frameworks are software architectures or platforms that facilitate developing, deploying, and utilizing large-scale language models for NLP tasks. These frameworks offer tools, libraries, and APIs that enable researchers and developers to work with foundational models like GPT, BERT, and T5. Some examples of LLM frameworks include Hugging Face Transformers, TensorFlow, PyTorch, and OpenAI API, which provide pre-trained models, fine-tuning capabilities, and interfaces to integrate these models into various applications and projects.

About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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