This project uses a multimodal LLM for medical image analysis to aid in diagnostics.

Multimodal AI, medical image analysis, diagnostic applications.

Develop a system that answers questions based on visual input using the IDEFICS 9B model. It involves managing visual data and answering questions based on the content of an image.

Visual Question Answering (VQA), multimodal models, image understanding.

Create a voice assistant that understands voice and visual inputs using Llava and Whisper. Combines voice recognition, natural language processing, and visual understanding.

Multimodal AI, voice recognition, natural language processing, assistant applications.

Build a model specialized for optical character recognition and visual question answering using the Qwen2-VL model. Requires understanding of text extraction from images and answering questions about visual content.

OCR, VQA, multimodal models, image and text processing.

Create an application that allows users to interact with video content by asking questions, leveraging Qwen2-VL. Involves processing video data to understand its content.

Video understanding, multimodal AI, question answering.

Combines multimodal models with Retrieval Augmented Generation to answer questions based on images, utilizing Qwen-2 and ColPali. It involves not only processing images but also integrating them with a retrieval system.

Multimodal RAG, image and text processing, retrieval systems.

This project uses Janus 1.3B for image generation and retrieval-augmented generation tasks. Requires understanding image generation and RAG systems with a smaller language model.

Image generation, RAG, smaller LLM implementation.

Focused on video understanding, allowing users to chat, search, and summarize video content. Involves complex processing tasks for video understanding, summarization, and searching.

Video processing, summarization, search, multimodal models.

Develop a model to automate radiology reporting, integrating image and text data. Requires in depth knowledge of medical imaging and report generation.

Multimodal AI, medical imaging, report generation.

Builds a system that allows for querying of video content using LanceDB and LlamaIndex. Involves using these tools for video content processing and retrieval.

Video processing, RAG, vector databases, LlamaIndex.

Build a multimodal Retrieval-Augmented Generation (RAG) pipeline using LangChain and the Unstructured library to query complex PDFs containing various data types, leveraging LLMs like GPT-4 with vision.

Multimodal AI, data extraction.

Create a summarization application that processes different types of media. Requires knowledge of summarization techniques and processing different media types.

Multimodal AI, summarization, media processing.

Uses Google's Gemini Pro Vision with Langchain for multimodal RAG applications. Requires a deep understanding of both technologies.

Multimodal RAG, Google Gemini, Langchain.

Creates an ATS that leverages multimodal models to process resume content, including images and text. This is a full application for understanding and processing resume content for tracking.

Multimodal AI, resume processing, ATS development.

Tailor a smaller language model, Phi-2, for specific tasks using fine-tuning. Requires understanding of model architectures and training procedures.

LLM fine-tuning, model adaptation, smaller model optimization.

Adapts the IDEFICS 9B model for visual question answering through fine-tuning. Requires knowledge of fine tuning and Visual Question Answering.

Multimodal fine-tuning, visual question answering, LLM customization.

Uses QLoRA to fine-tune the multimodal Idefics 2 model. Requires deep understanding of both multimodal models and QLoRA technique.

Multimodal fine-tuning, QLoRA, model optimization.

Fine-tunes the Qwen2 VL model for specific applications using Llama Factory. Requires experience with both the model and the factory tool.

Multimodal fine-tuning, Llama Factory, model adaptation.

Uses fine-tuning techniques to create a medical diagnosis model that generates emojis. Involves creatively applying fine-tuning to a medical and creative task.

Fine-tuning, medical diagnosis, creative LLM applications.

Trains the DeepSeek model on a custom dataset to tailor it for specific tasks. Requires dataset management skills as well as experience with fine-tuning.

LLM fine-tuning, model customization, dataset management.

Optimizes the DeepSeek model for math-related tasks using GRPO. Requires an understanding of math with LLMs and group optimization techniques.

LLM fine-tuning, GRPO, mathematical reasoning with LLMs.

Optimizes the Llama 3 model using the ORPO technique. Requires a strong understanding of fine tuning.

LLM fine-tuning, ORPO, model optimization.

Creates a model specifically for identifying disease symptoms. Involves medical knowledge with a fine-tuned language model.

LLM fine-tuning, medical applications, symptom identification.

Improves LLM fine-tuning speeds by using Unsloth. Requires an understanding of optimization techniques for fine tuning.

LLM fine-tuning, performance optimization, Unsloth.

Fine-tunes large language models using multiple GPUs with DeepSpeed and Accelerate. Requires advanced knowledge of distributed training techniques.

LLM fine-tuning, distributed training, DeepSpeed, Accelerate.

Creates a full RAG pipeline with ingestion, retrieval, and generation stages. Requires experience with the full RAG pipeline.

RAG, end-to-end pipeline development, data ingestion, retrieval, generation.

Builds a RAG pipeline using OLlama, Python, and Llama 2. Uses core libraries to create a RAG system.

RAG, OLlama, Llama 2, Python.

Implements RAG using Langchain, OpenAI, and the FAISS vector database. Uses popular libraries to create a RAG system.

RAG, Langchain, OpenAI, FAISS.

Uses Mistral AI and Weviate as part of the RAG architecture. Requires a good understanding of both libraries.

RAG, Langchain, Mistral AI, Weaviate.

Utilizes LlamaIndex and Mistral-AI for a RAG system. Uses LlamaIndex as an open source framework and Mistral-AI for its models.

RAG, LlamaIndex, Mistral AI, open-source framework.

Builds a RAG pipeline with the Haystack framework and OpenAI. Requires understanding the haystack framework as well as OpenAI.

RAG, Haystack, OpenAI.

Uses Haystack, MistralAI, Pinecone, and FastAPI to create an end-to-end RAG application. Uses multiple technologies to make a more full app.

RAG, Haystack, MistralAI, Pinecone, FastAPI.

Implements a an end to end Document Q&A RAG App with Google Gemma And GRoq API

RAG, Google Gemma, Groq API.

A RAG pipeline for real-time applications using MongoDB and Pinecone. Includes building a real time pipeline.

RAG, real-time processing, MongoDB, Pinecone.

Creates a chatbot that can process multiple document types using AstraDB and Langchain. Uses these two tools to make an application.

RAG, chatbots, AstraDB, Langchain.

Uses Vertex AI, AstraDB, and Langchain to build a multimodal RAG. Incorporates multiple cloud services in a RAG system.

Multimodal RAG, Vertex AI, AstraDB, Langchain.

Creates a RAG-based chatbot with chat history. Includes the extra level of complexity of chat history.

RAG, chatbots, chat history management, memory implementation.

Build AI agents from scratch using open-source AI tools to summarize, write, and sanitize sensitive information without pre-built frameworks.

AI agent development, open-source AI, summarization, information sanitization.

Build an AgentOps library in Python to monitor AI agents effectively, including installation, key metrics monitoring, and data visualization.

AI agent monitoring, AgentOps library development, Python, data visualization.

Build a Multi-Agent AI System from Scratch using Python and OpenAI's GPT-4o model with a Streamlit web interface for specialized tasks.

Multi-agent systems, Python, OpenAI GPT-4o, Streamlit web interface.

Use Autogen to enable LLMs like Claude and GPT-4o to debate "Which is tastier, Pizza or Sushi?".

Multi-agent systems, Autogen framework, LLM collaboration, agent role management.

Leverage the LangGraph framework to build robust, stateful, multi-actor applications using map-reduce patterns.

LangGraph framework, stateful applications, multi-actor applications, map-reduce patterns.

Enhance AI capabilities by combining retrieval mechanisms with generative models to create intelligent, autonomous agents using Crew AI.

Agentic RAG, Crew AI, retrieval mechanisms, generative models, autonomous agents.

Create a multi-agent AI system for investment risk analysis. Configure agents to monitor market data, develop trading strategies.

Agentic RAG, Crew AI, generative models.

Build a simple AI agent for healthcare research using the Crew.ai framework.

Agentic RAG, Crew AI, retrieval mechanisms, generative models, autonomous agents.

Build an advanced multi-agent AI app through Python, Langflow, Astra DB, Streamlit, and more.

Agentic RAG, Streamlit, Langflow.

Build an intelligent multi-agent system that transforms the way students manage their academic life using LangGraph's workflow framework.

Multi-agent systems, workflow orchestration, personalized academic support.

Develop an AI agent to assist with legal clause analysis and management.

Legal AI, text analysis, clause management.

Create an AI agent for content analysis and intelligence gathering.

Content analysis, intelligence gathering, NLP techniques.

Build a FAQ bot to assist with EU Green Compliance queries.

Compliance assistance, FAQ bots, question answering systems.

Develop an AI shopping assistant to help users find and compare products.

Shopping assistance, product comparison, AI recommendations.

Create an AI agent for managing and responding to weather disasters.

Disaster management, weather forecasting, emergency response.

Develop an AI-powered mentor designed to simplify and support your journey in Generative AI learning, Resume preparation, Interview assistant and job hunting.

Hackathon preparation, career assistance, AI guidance.

Create an AI agent to provide insights and analysis using LangGraph. AInsight automatically collects, processes, and summarizes AI/ML news for general audiences.

Data analysis, insights generation, LangGraph framework.

Develop a swarm of AI agents to assist with blog writing and content creation.

Content creation, blog writing, swarm intelligence.

Build an AI agent to generate business-themed memes for marketing and social media.

Meme generation, marketing assistance, AI creativity.

Creates an application that generates songs, including vocals, from text using generative AI.

Audio generation, music generation, generative AI.

Builds an application that generates music from text using generative AI.

Music generation, generative AI.

Generates music from text using the Text2Music AI Model MusicGen by Meta AI.

Music generation, generative AI.

Build an application that generates music and speech by cloning voices.

Music generation, generative AI.