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Project directory

Projects

Open-source AI projects tracked by the leaderboard, ordered by GitHub stars.

C++ 6 stars

Emreerdog/mbase_nlquery

On-premise, high-performance Text-to-SQL engine for PostgreSQL with single REST API endpoint

Overview: MBASE NLQuery is a high-performance Text-to-SQL engine for PostgreSQL, designed for on-premise use. It leverages the Qwen2.5-7B-Instruct-NLQuery model from the MBASE SDK to convert natural language queries into SQL queries, executing them through the PostgreSQL client SDK. The engine is secure by default and can be configured to run offline without requiring external table information. Key features: - Offline Text-To-SQL generation/executor engine - Secure execution through 'generate_only' option - Easy to use single HTTP REST API endpoint - Supports HTTPS through OpenSSL - CUDA acceleration for GPU usage - Easy prompt cooking for text-to-text models - Simple web UI for testing purposes Tech stack: C++ Why it matters: MBASE NLQuery simplifies the process of converting natural language queries into SQL queries, making it easier for developers to integrate text-to-sql functionality into their PostgreSQL databases without the need for external table information. Its secure execution and easy-to-use REST API make it a valuable tool for developers working with PostgreSQL databases. GitHub stars: 6 Repository: Emreerdog/mbase_nlquery
Unknown 6 stars

Galigeigei-Z/CRAFTS-Multi-agent-for-Equation-oriented-PSE

Multi-agent system powered chemical process simulation

Overview: CRAFTS is a multi-agent system designed for chemical process simulation, replacing a closed black-box simulation loop with role-adapted agents over an inspectable IDAES/Pyomo execution substrate. It includes seven agents and covers input understanding, intent routing, visual description, topology generation, specification generation, debugging, and optimization. Key features: - Multi-agent system - Role-adapted agents over an inspectable IDAES/Pyomo execution substrate - Seven collaborating roles - Three schema-critical roles fine-tuned for VisualGraphIR, TopologyIR, and SpecIR generation - Deterministic gates validate promoted artifacts before model construction, solving, and optimization - Supported IDAES flowsheets, reduced process models, grid and design optimization, and external dynamic simulation Tech stack: LangGraph, IDAES, Pyomo Why it matters: CRAFTS provides a collaborative and adaptive approach to chemical process simulation, enhancing the accuracy and reliability of process models by integrating human expertise and machine learning.
Python 6 stars

GiuseppeBellamacina/VulnerabilityBot

Vulnerability Bot with Database

Overview: Vulnerability Bot is a local client/server system utilizing a vulnerability database to answer user queries about vulnerabilities. Key features: - Database integration for vulnerability information - Streamlit-based user interface for easy interaction - Local server for processing and updating queries - Python-based backend with support for various LangChain packages Tech stack: Python Why it matters: This project provides a practical tool for developers to quickly find and understand potential security vulnerabilities, streamlining the process of vulnerability management and enhancing security awareness.
Python 6 stars

Griffith-7/nf4-triton-kernel

An optimized, high-performance NF4 (NormalFloat 4-bit) dequantization Triton GPU kernel achieving up to 1.41x speedup over bitsandbytes.

Overview: This project optimizes the dequantization process for 4-bit NormalFloat (NF4) quantization using Triton GPU kernels, achieving up to 1.41x speedup over `bitsandbytes` C++ across various tensor sizes. Key features: - Single fused Triton kernel for efficient dequantization - Inline PTX assembly for optimized bit unpacking - L2 cache eviction for reduced memory access - BF16/FP16 output format - Compatible with PyTorch and Triton - >1.15x speedup over `bitsandbytes` C++ across all tensor sizes Tech stack: Python Why it matters: This project provides a significant performance improvement by eliminating CPU dispatch overhead and achieving consistent speedups, making it particularly useful for optimizing the dequantization process in applications that use 4-bit quantization. Repository: Griffith-7/nf4-triton-kernel GitHub stars: 6 GitHub description: An optimized, high-performance NF4 (NormalFloat 4-bit) dequantization Triton GPU kernel achieving up to 1.41x speedup over bitsandbytes.
Jupyter Notebook 6 stars

HasanBGit/Saudi-Dialect-ALLaM

Official code for "Saudi-Dialect-ALLaM: LoRA Fine-Tuning for Dialectal Arabic Generation" — LoRA fine-tuning of ALLaM-7B for Saudi dialect (Hijazi & Najdi) with dialect-token control, achieving 84% Saudi alignment and 6% MSA leakage. (arXiv: 2508.13525)

### Overview: Saudi-Dialect-ALLaM is a code-only release designed for researchers to fine-tune ALLaM-7B-Instruct-preview for Saudi dialect generation. The project includes training, evaluation, inference scripts, and analysis notebooks. ### Key Features: - **Training Scripts and Configs:** LoRA fine-tuning scripts and a config to replicate the setup used in the research. - **Evaluation Suite:** Internal evaluation for ALLaM variants and external evaluation with a dialect classifier, chrF++, BERTScore, and diversity metrics. - **Inference Helpers:** Tools for generating prompts from a trained checkpoint. - **Notebooks:** End-to-end training, evaluation, and inference demos, as well as figure generation. - **Results:** JSON/CSV reports and figures used in the paper. ### Tech Stack: Python, Jupyter Notebook ### Why it Matters: The project showcases how to fine-tune language models for specific dialects, achieving high alignment scores and minimal leakage of non-target dialects. This can be crucial for applications requiring precise language generation in specific regions. ### GitHub Stars: 6 ### Repository: HasanBGit/Saudi-Dialect-ALLaM ### Primary Language: Jupyter Notebook ### GitHub Description: Official code for "Saudi-Dialect-ALLaM: LoRA Fine-Tuning for Dialectal Arabic Generation" — LoRA fine-tuning of ALLaM-7B-Instruct-preview for Saudi dialect (Hijazi & Najdi) with dialect-token control, achieving 84% Saudi alignment and
JavaScript 6 stars

IngressTechnology/jimbomesh-holler-server

Open source AI inference server with Model Marketplace, Document RAG, and OpenAI-compatible API

### Overview: JimboMesh Holler Server is an open-source AI inference server designed to replace cloud-based embedding calls with a local Ollama instance. It includes a Model Marketplace, Document RAG, and OpenAI-compatible APIs, all running on-premises for better data security and performance. ### Key Features: - Local Ollama server for embedding and inference. - Model Marketplace for easy model management. - Document RAG integration for enhanced search capabilities. - OpenAI-compatible API access. - Admin UI for centralized management. - Optional Mesh connectivity for seamless data transfer. ### Tech Stack: JavaScript, Node.js, Express, Docker, OpenAI, Qdrant, Ollama. ### Why It Matters: JimboMesh Holler Server ensures data remains on-premises, improving security and reducing costs associated with cloud-based services. The local Ollama server eliminates the need for API calls, making it faster and more efficient. The Model Marketplace and Document RAG features enhance the overall functionality of the system, making it a valuable tool for developers and data scientists alike. ### Repository: The repository is hosted on GitHub at [IngressTechnology/jimbomesh-holler-server](https://github.com/IngressTechnology/jimbomesh-holler-server). ### GitHub Stars: The repository has received 6 GitHub stars, indicating its popularity and community interest. ### Primary Language: The primary language used in the project is JavaScript. ### GitHub Description: This repository contains the JimboMesh Holler Server, an open-source AI inference server with Model
Python 6 stars

IonDen/mlx-train-perf

Fused, logit-free linear-cross-entropy loss, RAM-fit planner, and benchmark harness for MLX fine-tuning on Apple Silicon

Overview: `mlx-train-perf` is a set of tools designed to fine-tune models on Apple Silicon with reduced memory usage, improved performance, and a planner for RAM availability. Key features: - Fused, logit-free linear-cross-entropy loss - Sequence packing for short examples - Flash-attention training path for long sequences Tech stack: Python Why it matters: By optimizing memory usage and providing a planner for RAM availability, `mlx-train-perf` allows for more efficient fine-tuning of models on Apple Silicon, potentially reducing training times and improving overall performance.
Jupyter Notebook 6 stars

Jacob12138xieyuan/Hand-gesture-controled-Arduino-car-using-machine-learning-model

Overview: This project demonstrates how to use pre-trained machine learning models, specifically VGG16, to create a robust model for hand gesture-controlled car control. The project includes detailed steps on creating a dataset, training a customized model, and integrating a user interface for real-time recognition. Key features: - Creating a dataset for training - Training a customized model using VGG16 - Implementing a user interface for hand gesture recognition - Integrating an Arduino board for Bluetooth and motor control Tech stack: Keras, VGG16, Python, Jupyter Notebook Why it matters: This project showcases the practical application of pre-trained models in machine learning, demonstrating how to leverage professional models for robust training with limited data. It provides a clear example of integrating machine learning with hardware control, making it useful for developers looking to create interactive and autonomous systems.
Python 6 stars

Joonsun-Hwang/clm-train

Training HuggingFace's CLMs with Accelerate

### Overview: This project provides a comprehensive guide for fine-tuning HuggingFace's CLMs (Large Language Models) using Accelerate and PEFT (Performance Efficient Fine-Tuning). It includes detailed installation instructions, configuration options, and examples for both multi-GPU data parallel and DeepSpeed training. The project also offers inference capabilities for evaluating the trained models. ### Key Features: - Multi-GPU Data Parallel Training - DeepSpeed Fine-Tuning - TPU Configuration for Accelerate - Input Data Preparation and Loading - Fine-Tuning and Inference Scripts ### Tech Stack: - Python ### Why it Matters: This project serves as a practical guide for developers looking to fine-tune HuggingFace's CLMs efficiently, leveraging the latest advancements in training models with Accelerate and PEFT. The project includes detailed installation steps, configuration options, and examples, making it a valuable resource for anyone interested in improving the performance and efficiency of their language models.
HTML 6 stars

KaiFelixBennett/RadeonForge

Fine-tune your own LLM on an AMD Radeon GPU — the easy, tested way. QLoRA via ROCm on Windows/WSL2 & Linux, a worked Gemma-4 example, a reusable live training dashboard, and a smoke test that proves the loss falls.

Overview: Fine-tune your own language model (LLM) on an AMD Radeon GPU using the RadeonForge project, a tested and easy-to-follow path that includes a smoke test and a reusable live training dashboard. Key features: - Fine-tune your own LLM on an AMD Radeon GPU - A tested path from a blank machine to a model - A smoke test to prove your GPU can train before fine-tuning - A production-quality live training dashboard - A reusable Python file for your own model training - Consistent training output with valid JSON Tech stack: Python 3.12, ROCm 7.2, Windows/WSL2, Linux Why it matters: Fine-tuning allows you to train your own LLM reliably and efficiently, ensuring your model's behavior is baked into the weights, making it perform reliably. It's cheaper, faster, and runs offline, ensuring data sovereignty. The smoke test proves your GPU can train before fine-tuning, making the process straightforward.
Python 6 stars

KenyonY/dtflow

Inspect, clean, transform and ship LLM training data from one CLI; dt view is the interactive entry point.

### Overview: dtflow is a command-line tool designed to inspect, clean, transform, and ship large language model (LLM) training data from a single command line interface. It provides an interactive entry point called `dt view` that allows users to explore, filter, and transform data in real-time. ### Key Features: - **Interactive Entry Point:** `dt view` provides an interactive interface to explore and manipulate LLM training data. - **CLI Interface:** dtflow operates on a terminal, making it accessible from any command line environment. - **Data Formats Support:** Supports JSONL, NDJSON, JSON, CSV/TSV, Parquet, Arrow, and Excel files. - **Schema Inspection:** Provides a schema for JSONL files, including types, null rates, and value distributions. - **Filtering and Sorting:** Filters and sorts data based on various criteria, such as turns, roles, and specific search queries. - **Conversion Presets:** Converts data between different formats, including OpenAI Chat, ShareGPT, Alpaca, and DPO. - **Export Options:** Supports exporting data in various formats, including OpenAI messages, and allows for easy sharing with tools like ShareGPT, Alpaca, and DPO. - **Pythonic API:** All commands accept `-` for stdin and write to stdout, allowing for easy chaining and composition. - **Language Independence:** The interface is in English, and `dt lang zh` can be used to switch to Chinese. ### Tech Stack: - **Python:** The primary language used for development and
Python 6 stars

KleinYuan/tf-ft

Implementation on tensorflow fine tuning of generic CNN based model

Overview: This project involves fine-tuning generic CNN-based models, such as AlexNet, VGG-16, and others, using TensorFlow. It focuses on extending pre-trained models by freezing specific layers and connecting them with custom fully connected layers. Key features: - Fine-tuning generic CNN-based models - Extending pre-trained models by freezing specific layers - Connecting pre-trained layers with custom fully connected layers Tech stack: Python Why it matters: This project is valuable for developers looking to enhance pre-trained models by customizing them for specific tasks, making them more effective and efficient. Repository: KleinYuan/tf-ft Primary language: Python GitHub stars: 6 GitHub description: Implementation on TensorFlow fine tuning of generic CNN based models README excerpt: This project aims to fine-tune generic CNN-based models, such as AlexNet, VGG-16, and others, using TensorFlow. It focuses on extending pre-trained models by freezing specific layers and connecting them with custom fully connected layers. The project includes a detailed manual on how to set up the project, including downloading pre-trained weights and running the training process.
Python 6 stars

Lahdhirim/NLP-financial-question-answering-tool

Fine-tuning a text-to-text transformer model (T5) on a financial question answering dataset. Includes data preprocessing, training, evaluation pipelines, and a web app for inference built with Flask and Angular.

### Overview: This project demonstrates how to fine-tune a text-to-text model on a financial question answering dataset using the Hugging Face Transformers library. It provides a fully customizable pipeline for data preprocessing, model fine-tuning, evaluation, and serves model inference through a Flask-based web app. ### Key Features: - Fine-tuning of a text-to-text model (T5) on a financial dataset - Data preprocessing, model fine-tuning, and evaluation pipelines - Inference via a Flask-based web application ### Tech Stack: Python ### Why It Matters: The T5 architecture is chosen for its effectiveness in generative question answering tasks, making it ideal for tasks that demand coherent and contextually relevant natural language output. ### Repository: Lahdhirim/NLP-financial-question-answering-tool ### Primary Language: Python ### GitHub Stars: 6 ### GitHub Description: Fine-tuning a text-to-text transformer model (T5) on a financial question answering dataset. Includes data preprocessing, training, evaluation pipelines, and a web app for inference built with Flask and Angular.
Python 6 stars

LianHe-BI/Basic-Qwen-3B-SD-Prompt-SOUL-ARCHITECT-v2.0-DEMO

EN: An overfitted SD prompt engine with severe "aesthetic snobbery," forcibly transforming mundane ideas into professional-grade physical rendering instructions. CN: 一个具备“审美洁癖”的过拟合提示词引擎，强行将平庸构思纠偏为具备极致物理质感的工业级渲染指令。

Overview: SOUL_ARCHITECT_v2.0 is a cutting-edge digital rendering engine designed for developers, utilizing advanced AI techniques to transform mundane ideas into professional-grade physical renderings. This tool is built on NVIDIA Blackwell architecture, leveraging sparse computing and aggressive weight distribution to achieve unparalleled physical fidelity. Key features: - Fine-tuned physical parameter reconstruction focusing on Subsurface Scattering and Anisotropic Filtering. - Overcomes the "aesthetic snobbery" by training with a high loss of 0.47, ensuring that even low-fidelity concepts are rendered with extreme physical realism. - Utilizes native BF16 sparse computing for efficient use of GPU resources. Tech stack: Python Why it matters: SOUL_ARCHITECT_v2.0 offers unparalleled physical rendering capabilities, making it indispensable for developers looking to create high-quality, realistic renderings in a professional environment. Its ability to handle extreme weight distribution and physical parameter reconstruction sets it apart from other rendering tools, ensuring that even the most complex and detailed concepts are rendered with exceptional realism. Repository: LianHe-BI/Basic-Qwen-3B-SD-Prompt-SOUL-ARCHITECT-v2.0-DEMO Primary language: Python GitHub stars: 6 GitHub description: An overfitted SD prompt engine with severe "aesthetic snobbery," forcibly transforming mundane ideas into professional-grade physical rendering instructions.
Python 6 stars

MahmoudHussienMohamed/Agentic-Network-Monitoring

Agentic Network Monitoring: An autonomous multi-agent SOC system powered by fine-tuned local LLMs

### Overview: This project implements a Multi-Agent Network Security Monitoring System using a fine-tuned Llama 3.2-3B foundation model. It is designed to autonomously monitor private networks, detect security threats, and provide actionable response strategies. ### Key features: - **Observer Agent**: Monitors network traffic and detects anomalies. - **Responder Agent**: Provides immediate tactical mitigation and response plans. - **Consultant Agent**: Offers strategic long-term security analysis and policy recommendations. ### Tech stack: Parameter-Efficient Fine-Tuning (LoRA) ### Why it matters: This project leverages fine-tuning to create highly specialized agents, resulting in higher accuracy for specific tasks, operational efficiency, data privacy, and structured & consistent output.
Rust 6 stars

Metrale/metrale-inference

Carbon-friendly Pure Rust Inference Engine

Overview: Metrale-inference is a carbon-friendly Pure Rust Inference Engine designed for efficient and sustainable computation. Key features: - Energy-efficient operations - Zero carbon footprint - Rust language implementation Tech stack: Rust Why it matters: Metrale-inference is crucial for developers looking to build sustainable and energy-efficient applications, making it an essential tool for environmentally conscious projects.
Python 6 stars

MrAnayDongre/Inference-Kernels

LLM inference kernels from scratch in Triton: KV cache, FlashAttention, PagedAttention, RMSNorm, RoPE, SwiGLU, and benchmarks.

### Overview: This repository is a hands-on project that implements various LLM inference kernels from scratch, focusing on KV cache, FlashAttention, PagedAttention, RMSNorm, RoPE, SwiGLU, and continuous batching. The goal is to make the core mechanisms visible, runnable, benchmarkable, and explainable, providing insights into the mechanisms behind modern LLM inference systems. ### Key Features: - KV cache - Paged KV simulation - Triton fundamentals - Fused row/element kernels - FlashAttention - PagedAttention decode - Continuous batching - Quantization - Speculative decoding - Benchmarks ### Tech Stack: Python ### Why It Matters: This project aims to demystify the mechanisms behind modern LLM inference systems, making the core ideas visible, runnable, and benchmarkable. It provides a deeper understanding of how these systems work and can be improved.
Python 6 stars

Mrzhiyao/glm53-a800-vllm

Reproducible vLLM deployment for GLM-5.3-Flash on 8x NVIDIA A800 SM80 GPUs

Overview: This project provides a reproducible vLLM deployment for GLM-5.3-Flash on 8x NVIDIA A800 SM80 GPUs, including fixed source code baseline, SM80 integration patches, final coverage files, Docker build scripts, 1M context startup script, multi-modal configuration, and pressure tests. Key features: - Fixed source code baseline - SM80 integration patches - Final coverage files - Docker build scripts - 1M context startup script - Multi-modal configuration - Pressure tests Tech stack: Python Why it matters: This project offers a reliable and reproducible way to deploy GLM-5.3-Flash on NVIDIA A800 GPUs using vLLM, ensuring consistent performance and ease of deployment. GitHub stars: 6 Repository: Mrzhiyao/glm53-a800-vllm
Dart 6 stars

Msr7799/Atlas-AI

Cross-platform Flutter AI chat application for Android, Linux, and Windows. Features a fine-tuned agent capable of training other agents (like instructors) using Google's new A2A service.

### Overview Atlas AI is a sophisticated AI assistant application built with Flutter, featuring native Arabic language support and advanced conversational capabilities. The application integrates multiple AI models and provides a seamless, cross-platform experience with modern UI design and robust performance optimizations. ### Key Features - **Multi-Model AI Integration** - **Groq Models**: 10 different models including Llama 3.1, Mixtral, and Gemma - **GPTGod Service**: 17 advanced models with GPT-4 support - **Tavily Search**: Real-time web search integration - **Hugging Face**: Access to open-source models - **Local AI**: Offline model support - **Language Support** - **Native Arabic Support**: Full RTL layout and Arabic language processing - **Multi-language Interface**: English and Arabic UI - **Localized Content**: Context-aware responses in Arabic - **Modern User Interface** - **Material Design 3**: Modern, responsive design system - **Dark/Light Mode**: Dynamic theming with user preferences - **Cross-Platform**: Consistent experience across all platforms - **Accessibility**: Full accessibility support ### Tech Stack Flutter ### Why it Matters Atlas AI is designed to provide a powerful and intuitive AI assistant experience, making it an essential tool for developers and professionals alike. Its advanced features, seamless integration, and robust performance make it a valuable addition to any AI assistant toolkit.