Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.

Examples of chatbot implementations with Langchain and Streamlit

Empower Truly Autonomous AI Agents through our Experimental Adversarial and Evolutionary Reinforcement Learning Framework. Join our Community: https://t.me/evolveRL_portal

RAG Based LLM Chatbot Built using Open Source Stack (Llama 3.2 Model, BGE Embeddings, and Qdrant running locally within a Docker Container)

Shuffle LLama-3.2 layers and have it prompt an image. Node works with any model - Flux, SD3, SDXL...

This Repo Contains Script To Fine Tune Open Source Models Using Unsloth by using UI with simple click and progress

A gradio web UI demo for Llama3.2-Vision Model

Context-aware chatbot using LLaMA 3.1 embeddings and a vector database for efficient query understanding and response generation, with NLP techniques like entity extraction and intent recognition.

Simple tool for RAG (Retrieval Augmented Generation) applications to be run over locally stored files

The Apollo Adlux Hospital RAG Chatbot system, Built on the Ollam 3.2 LLM, this chatbot leverages Retrieval-Augmented Generation (RAG) to offer accurate, contextually relevant answers by pulling information from hospital resources and FAQs.

Effortless Data Extraction, Powered by : Generative AI

Code for Paper Submitted in GRADES-NDA 2025

Customized multi-language Twit generator using llama-3.2-90b-text-preview api

Streamlit ChatBot App built with LangGraph, Memory management, Groq LLM Inference

Developing a system to match eligible patients to ongoing clinical trials using Vector Embeddings and LLMs!

a simple Job exteactor from job posting website that uses llama3.2 model to process and extract information from job postings.

Smartloop is an open-source SLM platform to train and run models on an edge device

A similar project to RAGChatbot, substituting ChromaDB with Milvus.

This repository provides a chatbot application powered by Llama 3.2 using Streamlit. The chatbot is designed to generate responses to user inputs efficiently, utilizing the power of advanced language models.

An experimental project to deepen my understanding of LLM architectures and applications through practical implementations.