Hands-On tutorials building zero shot AI Applications with Hugging Face Open Source Models

An Open-source, self-hosted AI model hub with Hugging Face compatibility, accelerating vLLM/SGLang performance.

open-source, local-first AI CLI for developers. It connects to your local Ollama models or remote providers like OpenAI, Anthropic, Gemini, and Hugging Face, letting you chat with your code, review changes, and craft commits — all from the terminal.

"Open Source Models with Hugging Face" course empowers you with the skills to leverage open-source models from the Hugging Face Hub for various tasks in NLP, audio, image, and multimodal domains.

In this repository, you will discover how Streamlit, a Python framework for developing interactive data applications, can work seamlessly with the Open-Source Embedding Model ("sentence-transformers/all-MiniLM-L6-v2") in Hugging Face and Llama 2 🦙🦙 model.

Smaug-72B topped the Hugging Face LLM leaderboard and it’s the first model with an average score of 80, making it the world’s best open-source foundation model. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>

An open-source visual manager for models with high-performance downloading, purpose-built for the Hugging Face ecosystem. ｜基于 Hugging Face 生态定制的开源可视化模型管理与高性能下载工具

In this project, a Voice Assistant Application has been built using Streamlit in Python with Langchain, RAG and Llama3.3 model (Open Source LLM). pyttsx3 (text-to-speech library), speech_recognition (speech-to-text library), Hugging Face Instruct Embeddings and Chroma (open-source vector database), PyPDFLoader etc.

Open Source Models With Hugging Face

Open Source Models with Hugging Face

Rust-based AI Chatbot powered by the Leptos framework and Hugging Face model, delivering intelligent and engaging conversations. Explore the future of conversational AI with this efficient and versatile open-source project. 🚀 #Rust #AI #Chatbot

Forkit Core is an open source passport layer for AI models and agents with GitHub CI validation, local verification, and Hugging Face-compatible export.

A free prompt engineering sandbox for financial use cases using open-source LLMs. Compare how different prompts affect outputs from models like Mistral and Phi-2. No API keys needed. Built with Gradio and Hugging Face Transformers.

A smart, conversational chatbot to showcase my projects, skills, and experience in an interactive way. Built with Python, FastAPI, and an open-source Large Language Model from the Hugging Face ecosystem. Ask it anything about my portfolio!

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Deeplearning.ai Short Practical Course on how to use open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library.

Open Source Models with Hugging Face

Python based Experiments with open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library.

AI-powered text analysis tool by TheDarkSyntax. Performs sentiment detection, keyword extraction, language identification, and text summarization using open-source NLP models. Built with Python and Hugging Face Transformers.

An open-source Streamlit app that fetches the latest news from multiple sources, summarizes articles using Hugging Face models, and categorizes them by topic. Built with free tools and deployable on Streamlit Cloud or Hugging Face Spaces.

An end-to-end guide to building robust LLM pipelines with Hugging Face and LangChain. Explores Transformers pipelines, Hugging Face Hub integration, secure token handling, and practical access to both open-source and gated large language models.

AI Agent as a Service - Go Implementation A production-grade AI Agent service built with Go, designed for customer support automation using GPT (OpenAI) or open-source models (Hugging Face).

Question Answer chatbot application with frontend and backend code together, which uses hugging face open source model for getting context(file uploaded) to answer to user query.

A complete end-to-end Large Language Model (LLM) repository covering everything from basics to advanced implementations. Includes RAG pipelines, vector databases, LangChain workflows, Hugging Face models, fine-tuning, and chatbot applications — a full package for learning and building with open and closed-source models.

Explore generative AI with Hugging Face models and LangChain. This project provides Jupyter notebooks for setting up, fine-tuning, and deploying models for tasks like text generation, question answering, and instruction following. Ideal for developers and AI enthusiasts aiming to build robust, scalable NLP solutions with open-source tools.

"this is a Text Paraphrasing App! Built using Flutter & Hugging Face's Transformers, this open-source project empowers text paraphrasing. Input text, tap to paraphrase, and witness the magic of transformer models. Experience NLP in action with this user-friendly app.

AI-Powered Graph & Workflow Generator 🚀 This Next.js app dynamically generates animated graphs and interactive workflows from any text by understanding its context. Supports open-source models (Hugging Face) for free users and premium AI models (OpenAI, Gemini) for paid users. Fully serverless with Next.js API routes. 🎨📊

Nectar-X-Studio is a powerful, Local AI-Inferencing application that allows the user download, create, run agents and run large language models on their own machine. With no internet connection required, Nectar ensures privacy-first, high-performance inference using cutting-edge open-source models from Hugging Face, Ollama, and beyond.

An open-source library for hardware-aware model optimization. It learns soft gates to prune and fine-tune neural networks for specific GPUs, balancing latency and accuracy. Includes tools for latency-driven training, export with kernel-aligned sizes, and a Hugging Face model-zoo integration.

This project is a Medical Advice Chatbot built using LangChain, Tavily API, and open-source models from Hugging Face. It provides users with trusted medical information by answering their health-related queries. The chatbot fetches relevant data using the Tavily API and generates human-like responses using a language model.