Star 总榜

This project is used to generate a blog post using Natural Language processing, Hugging Face Transformers and GPT-2 Model.

VettIQ is an AI-powered startup idea validation tool that leverages LangGraph workflows, FastAPI, and Streamlit to deliver actionable insights for entrepreneurs

H.E.I.M.D.A.L.L looks at fleet telemetry and gives you natural-language insights. GPU data loading (cuDF), local LLM inference (Gemma 2), and production NIM on GKE. Open the notebooks, run cells, get answers! Quick start should not take longer than 10 minutes and the T4 path is completely free!

Accelerate talent match, find right talents, amplify quality.

A tool for performing semantic search within pdf documents leveraging sentence transformers.

🚀 The Ultimate Curated List of LLMOps Tools, Frameworks, and Resources - A comprehensive collection of the best tools for Large Language Model Operations

Multi-node distributed LLM training framework

Cloud-agnostic Agentic AI & Multi-Modal RAG platform with GraphRAG, intelligent routing, MCP enterprise integrations (Odoo, PostgreSQL), and pluggable LLM providers.

Self-hostable AI text detector, bring your own model, calibrate on your domain, run locally or on Modal GPU.

Generate multi-speaker conversational datasets for LLM fine-tuning. N-persona support, topic variation, conversation continuation, creative briefs with web search, character pools, Docker support. ShareGPT format with configurable role mapping.

Finetune GPT2 for text summarization

A Non-Official HuggingFace Rest Client for Unity (UPM)

Remove Backgroud from a video 🧤🎥

Text Summarization on Spotify Podcast Transcripts for NLP class at @UNIBO

Query, ask and chat with a document-index via transformer models!

Collection of public datasets from the French administration, vectorized and ready to use in AI projects.

Build a full-stack AI chatbot using Python, React, FastAPI, and GPT-J-6B. Utilize Redis for efficient data storage, enabling real-time conversations and advanced language processing

A PDF Question-Answering App built with RAG (Retrieval-Augmented Generation), allowing users to upload PDFs and ask context-based questions. Powered by Streamlit, LangChain, Ollama, and Chroma for efficient and accurate answers.

Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. 📚🧠🚀

A simple Ruby UI for Chroma database.