Harness LLMs with Multi-Agent Programming

🔍 LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

State of the Art Natural Language Processing

Flexible classic and NeurAl Retrieval Toolkit

👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.

Minimalist web-searching app with an AI assistant that runs directly from your browser. Uses Web-LLM, Ratchet-ML, Wllama and SearXNG. Demo: https://felladrin-minisearch.hf.space

CompAct: Compressing Retrieved Documents Actively for Question Answering

In this Project We perform NLP tasks like QA Pair Generation, Question Answering, Text Summarization and Data Extraction from webpages using Large Language Models (Like Gemini ) and Langchain

DevTalks is a Question-Answer website similar to stackoverflow for professional and enthusiast programmers.

Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

The PyTorch Library for LLM Applications.

Supabase for RAG - Build and scale production-ready user facing AI apps

A single application with more than 100 Topics related Programming Languages, General Subjects , Computer Science MCQ'S, True-False, Short Questions type quiz.

Reasoning in Large Language Models: Papers and Resources, including Chain-of-Thought, Instruction-Tuning and Multimodality.

Designed a user-friendly quiz app with interactive multiple-choice questions, smooth navigation, and detailed results summary, leveraging Flutter for dynamic UI.

Welcome to my daily collection! Here, I keep track of all the questions I tackle on LeetCode. It's like my personal journal of learning. Take a look around to see how I'm improving over time. Feel free to join me on this journey of getting better every day

[Neurocomputing 2023] Relational Graph Transformer for Knowledge Graph Representation

Fine-tune the Gemma2B language model on a climate-related question-answer dataset to improve its domain-specific knowledge using LoRA (Low Rank Adaptation).

PicQ: Demo for MiniCPM Llama3 to answer questions about images using natural language.

Large language models (LLMs), pre-trained on a large corpus of text, have been proven to be powerful tools for designing natural language processing applications. The goal of this project is to evaluate various methods to efficiently adapt LLMs for question answering about a given text.