Projects

Overview: This repository contains an implementation of a recommendation model using Milvus, a vector database designed to store and search high-dimensional vectors efficiently, deployed with Docker. The recommendation model leverages the capabilities of Milvus to perform fast similarity searches, enabling efficient retrieval of similar items based on user preferences or item attributes. Key features: - Utilizes Milvus for efficient storage and retrieval of high-dimensional vectors. - Implements recommendation algorithms for personalized recommendations. - Supports both user-based and item-based recommendation strategies. - Provides easy-to-use APIs for integrating the recommendation model into existing applications. - Supports scalability for handling large datasets and user bases. - Dockerized deployment for easy setup and management. Tech stack: Jupyter Notebook Why it matters: This project showcases the integration of Milvus, a powerful vector database, with a recommendation model, demonstrating how it can be used to efficiently retrieve similar items based on user preferences or item attributes, making it valuable for applications needing personalized recommendations. Repository: HGSChandeepa/Recommendation-Model-using-Milvus-vector-db GitHub stars: 13 GitHub description: Recommendation Model using Milvus vector db

Overview: Nebula is a Kubernetes control plane designed to manage GPU workloads across various cloud environments, including NeoClouds, hyperscalers, and on-premises infrastructure. Key features: - Kubernetes-native experience for existing Deployments or Jobs. - Multi-provider support with modal, RunPod, and AWS providers. - Policy-driven placement for flexible NodePools. - Cost tracking for per-instance cost. - Observability with rich metrics and logs. Tech stack: Go Why it matters: Nebula simplifies GPU workload management across different cloud providers, ensuring seamless and cost-effective GPU usage without the need for complex per-cloud glue.

Overview: BAGM is a backdoor attack designed to manipulate text-to-image generative models, targeting three stages of the generative process. This attack increases bias in generated outputs by up to 515% without compromising model robustness. Key features: - Targets three unique text-to-image architectures across three stages of the generative process. - Modifies the behavior of the embedded tokenizer and pre-trained language and visual neural networks. - Includes surface, shallow, and deep attack forms. - Quantitative metrics for assessing backdoor attacks on generative AI models. - Utilizes a Marketable Foods dataset for the target domain. Tech stack: PyTorch, NVIDIA GeForce RTX 4090 GPU, cloud computing services. Why it matters: BAGM highlights the security challenges in generative AI, providing a framework for preemptive efforts to address these challenges.

Overview: A simple tool for taking a picture of text on a website and extracting the text. Key features: - Takes a picture of text on a website. - Extracts the text from the image. - Can be used on any website. Tech stack: Vue.js Why it matters: This tool simplifies the process of extracting text from websites, making it easier for developers to integrate text extraction into their applications.

Overview: This project includes ten secure ComfyUI nodes designed for WaveSpeed image generation and editing, featuring an in-canvas key flow, explicit spend control, and native `IMAGE` sockets. The nodes are production-tested and safe by default. Key features: - Secure in-canvas key flow - Explicit spend control - Native ComfyUI `IMAGE` sockets - Safe-by-default workflows Tech stack: Python Why it matters: These nodes offer enhanced security and ease of use for ComfyUI users, ensuring a safer and more controlled workflow environment. Repository: JsonMatrixLab/matrix-power-nodes-wave1 GitHub stars: 13 GitHub description: Ten secure WaveSpeed image generation and editing nodes for ComfyUI.

Overview: This project details the successful implementation of Gemma-4-31B with a TurboQuant KV cache on an AMD RDNA4 GPU (gfx1201), achieving full 256K context loading and prefilling at 735 tok/s. The repository includes a measured study showing that this setup uses only 22.9 GB VRAM and 9 GB free, with a crash-free decode performance of ~22 tok/s at low context and ~9.4 tok/s at 128K context. Key features: - TurboQuant KV cache - HIP-graph-safe Flash-Attention for llama.cpp - Fully measured on real hardware Tech stack: Python Why it matters: This project showcases a novel approach to running large language models on GPUs, specifically targeting the AMD RDNA4 architecture, which significantly reduces the memory footprint and improves performance compared to previous setups.

Overview: This comprehensive toolkit provides a wide range of tools and resources for building AI-powered applications, covering model selection, deployment, and everything in between. Key features: - OpenAI API - Grok (xAI) - Google Gemini - Hugging Face - Anthropic Claude - DeepSeek - Together AI - OpenRouter - Mistral AI - LLaMA (Meta) - AWS Bedrock - NVIDIA NeMo - FastAPI - Flask Tech stack: Python Why it matters: This toolkit offers a diverse set of tools and resources to support the entire AI development process, from model selection to deployment, ensuring that developers can create high-quality AI applications efficiently.

### Overview: The MCP Vision Bridge is a text-only LLM coding agent that provides visual insights into images through multimodal models. It integrates seamlessly with various Claude Code, Codex, opencode, Kimi, PI, and other compatible agents, enhancing their capabilities to analyze and debug code more effectively. ### Key Features: - **Image Analysis**: Routes images through a chosen multimodal model to provide detailed textual descriptions. - **Versatile Models**: Works with Claude Code, Codex, opencode, Kimi, PI, and any MCP client. - **Auto-Loop Hook**: Automatically updates the MCP server and skill on startup, ensuring the latest version is available. - **No Image Input**: The agent only needs to provide the image file path, which is automatically inserted into the agent's context. ### Tech Stack: - Node.js ≥ 18 - TypeScript ### Why It Matters: - **Enhanced Debugging**: Provides a detailed textual description of images, enabling agents to debug and fix issues more effectively. - **Versatility**: Works with multiple compatible agents and models, enhancing the overall functionality of the agent. - **Efficiency**: No need to manually transcribe or paste images, making the process more efficient and error-free. ### Repository: - **Repository**: KuaaMU/mcp-vision-bridge - **GitHub Stars**: 13 - **GitHub Description**: MCP server that gives text-only LLM coding agents vision — analyze images via any multimodal model (mimo, Claude, Gemini, OpenAI-compatible). Works with Claude Code,

Overview: ChessApp is an Android application designed for detecting and classifying chessboard images and chess pieces using deep learning techniques. Key features: - Chessboard detection - Chess piece classification - Virtual board visualization - Collaboration with Simone Mattioli Tech stack: Java Why it matters: This project showcases the application of deep learning and computer vision in detecting and classifying chessboard images and chess pieces, demonstrating the effectiveness of neural networks in real-world scenarios. Repository: MatteoLucchi1998/ChessApp Primary language: Java GitHub stars: 13 GitHub description: Deep Learning and Computer Vision application for chessboard detection and chess pieces classification. Datasets: Created datasets of 12.500 and 110.600 images Models: Generated up to 13 models using different approaches and training techniques All checkpoints: Available for download at <a href="https://drive.google.com/drive/folders/1Tp4o3D6_EMaBr4yM0hf4jzWkTPj1ZNeC">here</a>

Overview: EduMate RAG is a conversational question-answering service that retrieves answers from indexed course materials, powered by LangChain, Qdrant Cloud, and Groq LLM inference. It supports both integrated app flow and standalone service modes. Key features: - Answers only from indexed course materials - Remembers context across questions - Semantic retrieval using Qdrant Cloud or local ChromaDB - AI-powered generation with Groq's 120B model - Multilingual support (Arabic and English) - Source attribution in every answer - Runs on Groq's free tier - Incremental indexing with file change detection, embedding cache, and parallel PDF processing Tech stack: Python 3.11, FastAPI 0.109+, LangChain, Groq, Qdrant Cloud / ChromaDB, PyPDF 4.0.1+, and python-dotenv. Why it matters: This service enhances the EduMate platform by providing accurate and contextually relevant answers, improving user experience and educational effectiveness.

Overview: This repository contains the code and dataset needed to fine-tune the Qwen 2.5 AI model to serve as a smart customer service assistant, utilizing Python and Unsloth on Google Colab. Key features: - Fine-tuning of the Qwen 2.5 AI model - Utilizing Python and Unsloth - Training and testing on a dataset - Saving the trained model Tech stack: Python, Unsloth, Google Colab Why it matters: This project demonstrates how to adapt an AI model for customer service tasks, showcasing the potential of fine-tuning existing models for specific applications.

Overview: This project is a simulator designed to explore and optimize batching strategies and load patterns in Large Language Model (LLM) inference. It provides various configurations and metrics for easy comparison and analysis. Key features: - Multiple batching strategies (Static, In-Flight, Chunked Context, One Prefill Per Batch) - Different load generation patterns (Batch, Concurrent, Request Rate) - Rich metrics visualization - Configurable batch sizes and request parameters - Queue growth analysis for long-running simulations Tech stack: Python Why it matters: This simulator helps developers and researchers understand and optimize the performance of LLM inference by providing a comprehensive tool for exploring different strategies and patterns. Repository: Muhtasham/llm-inference-simulator Primary language: HTML GitHub stars: 13

### Overview: LoomRAG is a multimodal retrieval- augmented generation system that leverages OpenAI's CLIP and Whisper models for seamless text, image, and audio search. ### Key Features: - **Cross-Modal Retrieval**: Search text to retrieve both text and image results using deep learning. - **Image-Based Search**: Search the database by uploading an image to find similar content. - **Embedding-Based Search**: Uses OpenAI's CLIP, Whisper, and SentenceTransformer's Embedding Models for embedding the input data. - **CLIP Fine-Tuning**: Supports custom model training with configurable parameters including test dataset split size, learning rate, optimizer, and weight decay. - **Fine-Tuned Model Integration**: Seamlessly load and utilize fine-tuned CLIP models for enhanced search and retrieval. - **Upload Options**: Allows users to upload images, PDFs, and audio files (including real-time recording) for enhanced interaction and intelligent retrieval capabilities. ### Tech Stack: Python ### Why it Matters: LoomRAG streamlines the process of finding relevant information by combining text, images, and audio, making it a powerful tool for AI-powered search and retrieval.

### Overview: GALLO-3XL is a high-quality image generation model powered by NVIDIA A100, designed to replicate the style of DALL-E 3 with a focus on space themes. This project showcases the capabilities of AI in creating visually stunning and realistic images. ### Key Features: - **Space Theme**: The model is specifically designed to generate images inspired by space, making it ideal for projects requiring a futuristic or cosmic aesthetic. - **Gradio Integration**: The model is integrated with Gradio, a Python-based framework for building user interfaces, allowing for easy deployment and accessibility. - **Zero GPU Processing**: The model can process images without requiring a GPU, making it suitable for environments with limited computational resources. ### Tech Stack: Python ### Why it Matters: Gallo-3XL leverages AI to create stunning, high-quality images, making it a valuable tool for artists, designers, and anyone looking to enhance their visual storytelling. Its ability to replicate the style of DALL-E 3 with a focus on space themes makes it particularly appealing for projects requiring a futuristic or cosmic aesthetic. Repository: PRITHIVSAKTHIUR/GALLO-3XL Primary language: Python GitHub stars: 13 GitHub description: High Quality Image Generation Model - Powered with NVIDIA A100 README excerpt: ...

Overview: This repository contains the source code for a chatbot designed to assist customers with clothing advice, website navigation, and store information at a clothing store. Key features: - Provides clothing advice and recommendations - Guides users on how to navigate the store's website - Uses advanced language processing to understand and respond to user queries Tech stack: Jupyter Notebook Why it matters: This chatbot is a valuable tool for enhancing customer experience and improving the efficiency of a clothing store's operations by providing personalized and accurate information through a conversational interface. Repository: QuangNguyen711/AutClothingChatbot Primary language: Jupyter Notebook GitHub stars: 14 GitHub description: PTIT's Major Project: Website Programming - This repo contains a chatbot for a clothing store. The chatbot acts as an employee with specific knowledge about clothing consultation, website support, and store information. README excerpt:

Overview: This kit provides a reproducible setup for deploying DeepSeek-V4-Flash on a 2× NVIDIA DGX Spark cluster, featuring vLLM TP=2, NVFP4 KV, DSpark speculative decoding, and 1M context. The repository includes configs, scripts, and systemd units for a quick start. Key features: - DGX Spark setup - QSFP cable and control host - vLLM TP=2 configuration - NVFP4 KV integration - DSpark speculative decoding - 1M context support - systemd self-heal Tech stack: Python Why it matters: This kit provides a reliable and reproducible environment for deploying DeepSeek-V4-Flash, ensuring consistent results and ease of deployment.

Overview: SwarnDB is a unified data layer for AI that combines vector and graph databases into a single, efficient engine. It simplifies the integration of vector and graph databases by treating them as the same object, eliminating the need for synchronization and glue code. Key features: - A single engine for vector and graph databases - Production-grade Rust implementation - Inbuilt vector and graph functionalities - Opt-in graph functionality with explicit edges and provenance - Composable hybrid queries for efficient similarity and graph traversal Tech stack: Rust Why it matters: SwarnDB simplifies the integration of vector and graph databases, making it easier to build AI applications that require both vector and graph capabilities. The single engine approach reduces complexity and improves performance by eliminating the need for synchronization and glue code.

Overview: This project demonstrates the implementation of text summarization using Python, deep learning, machine learning, and transformer techniques. It integrates Hugging Face's Transformers library and Langchain for advanced summarization capabilities. Key features: - Text summarization using deep learning and machine learning models - Utilization of the Transformer architecture for improved summarization quality - Integration with Hugging Face's Transformers library for advanced summarization techniques - Compatibility with Langchain for seamless integration with other AI tools Tech stack: Python, deep learning, machine learning, transformer, Hugging Face Transformers, OpenAI, Langchain Why it matters: This project showcases the practical application of cutting-edge AI techniques in text summarization, making it valuable for developers looking to enhance their text processing capabilities.

### Overview: This Rust project runs the Qwen3.8 27B language model on a single CPU with at least 8GB of RAM, achieving up to 7.4 tokens per second. It is a pure Rust implementation that avoids BLAS, GPU, and ML frameworks, making it highly efficient and compatible with minimal hardware requirements. ### Key features: - **Pure Rust Implementation**: The entire model is written in Rust, without the use of BLAS, GPU, or ML frameworks. - **Quantized GEMM/GEMV Kernels**: Hand-written AVX2+FMA intrinsics for the quantized GEMM and GEMV kernels, optimized for performance. - **Scalar Stages Optimization**: The scalar stages around the quantized kernels are moved to the host feature set, enhancing compatibility and performance. - **Single 16.5 GB GGUF File**: The entire model is stored in a single 16.5 GB GGUF file, mapped directly from disk, ensuring minimal overhead. ### Tech stack: Pure Rust. ### Why it matters: This project is designed to be highly efficient and compatible with minimal hardware requirements, making it suitable for a wide range of environments, including single-core CPUs with modest RAM. ### Repository: StepfenShawn/qwen_inference_in_rust ### Primary language: Rust ### GitHub stars: 13 ### GitHub description: Runs the Qwen3.8 27B LLM on a single CPU with >=8GB of RAM, delivering up to ~7.4 tok/s

### Overview: The Model-to-NPU pipeline for Qualcomm Snapdragon is a groundbreaking project that enables the execution of Stable Diffusion XL (SDXL) on-device, specifically on the Snapdragon 8 Elite (SM8750) and Hexagon V79 HTP NPU. This project aims to eliminate the need for cloud servers, Termux, rooting, or Python runtime, making it accessible to a wide range of users. ### Key features: 1. **Native NPU Execution**: The project runs the SDXL pipeline natively on the Qualcomm Hexagon NPU, achieving high performance and low latency. 2. **Unified UNet Model**: The UNet model is no longer split into separate encoder and decoder halves, eliminating bottlenecks and improving efficiency. 3. **Dynamic Resolution Engine**: The engine supports arbitrary aspect ratios and resolutions, enabling seamless scaling without recompiling models or context switching. 4. **No Root, No Termux, No Python**: The project is designed to be executed without rooting, Termux, or Python runtime, making it accessible on any Android device. 5. **Standalone Android APK**: A standalone Android application is provided, allowing users to run the pipeline directly on their device. ### Tech stack: The project utilizes the following technologies: - **Snapdragon 8 Elite (SM8750)**: The SoC on which the pipeline runs. - **Hexagon V79 HTP NPU**: The Qualcomm Hexagon NPU used for execution. - **QNN (Qualcomm Neural Network)**: The neural network system