Project directory

Projects

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

Python 5 stars

alireza13742010/LIAMA3_chatbot_Arabic_Engligh

This repository is for developing the chatbot on health care question and answer

Overview: This repository is for developing a chatbot that can answer health care questions in multiple languages, including Arabic, Persian, English, and Spanish. Key features: - Supports multiple languages: Arabic, Persian, English, and Spanish. - Uses a large pre-trained model with 7 billion parameters. - Trained on 20 million questions and answers. - Available on GitHub with 5 stars. Tech stack: Python. Why it matters: This project showcases the potential of large language models in healthcare, providing accurate and multilingual answers to health-related queries. Repository: alireza13742010/LIAMA3_chatbot_Arabic_Engligh Primary language: Python GitHub stars: 5

Jupyter Notebook 5 stars

alpcansoydas/turkish-hate-speech-analysis

Turkish Hate Speech Detection

Overview: This project implements a Turkish hate speech recognition system using a fine-tuned version of YSKartal/bert-base-turkish-cased-turkish_offensive_trained_model and dbmdz/bert-base-turkish-cased. The dataset consists of labeled data from the Turkish Offensive Language Detection dataset. Key features: - Fine-tuned hate speech detection model - Uses a pre-trained model for improved performance - Labeled dataset for training and evaluation Tech stack: Python Why it matters: This project is significant for its contribution to the field of hate speech detection in Turkish, which is crucial for maintaining a safe and inclusive digital environment.

Python 5 stars

amjadmajid/llm_toaster

LLM Toaster enables you to train and fine-tune mini-GPTs.

### Overview LLM Toaster is a library designed for training, fine-tuning, and running inference on Transformer-based language models. It streamlines the process of dataset loading, model configuration, and execution. ### Key features: - **Model Configuration**: Configurable model architecture with options for depth, width, and heads. - **Training and Fine-Tuning**: Supports training on various datasets and fine-tuning on pre-trained models. - **Inference**: Provides efficient inference capabilities for models trained with LLM Toaster. ### Tech stack: Python ### Why it matters: LLM Toaster simplifies the process of training and fine-tuning large language models, making it easier for developers to experiment with different architectures and configurations.

Python 5 stars

arkodeepsen/helix

Professional training stack for 100M parameter language models optimized for AMD Radeon RX 7000/9000 series GPUs with ROCm. Complete pipeline: tokenization, pretraining, fine-tuning, DPO, and GGUF export.

Overview: Helix is a complete training stack designed for building small language models optimized for AMD GPUs using ROCm. It includes tokenization, pretraining, fine-tuning, data preparation, and export to GGUF format. Key features: - Tokenization and pretraining - Fine-tuning with supervised fine-tuning (SFT) - Data preparation and export to GGUF format - Agent demonstration with tool calls - Hugging Face deployment Tech stack: Python Why it matters: Helix is optimized for AMD GPUs, making it suitable for high-performance language model training on modern graphics cards.

Jupyter Notebook 5 stars

aryanap07/Timeless

Before the prompt, there was the architecture.

Overview: Timeless is a project that involves building a large language model from scratch, inspired by the architecture described in "Building a Large Language Model (From Scratch)" by Sebastian Raschka. Key features: - Large Language Model from Scratch - Architecture Reference Tech stack: Jupyter Notebook Why it matters: Timeless serves as a practical demonstration and educational resource for understanding the foundational principles of large language models, providing insights into the architecture and implementation of such models.

Jupyter Notebook 5 stars

athlohangade/qa-system-using-tl-legal-domain

A Question Answering System built for answering specific questions on Indian Legal Acts following the Transfer Learning approach.

Overview: This repository is a Question Answering System (QAS) designed to answer specific questions on Indian Legal Acts using a Transfer Learning approach. The system focuses on legal documents, particularly those amended by the Indian Constitution and Judiciary, and is built on Jupyter Notebook. Key features: - Fine-tuned BERT-Large model for legal domain dataset - Preprocessing and context creation - Question generation using Google T5-base - Batch processing with a batch size of 32 for question answering Tech stack: Jupyter Notebook Why it matters: This QAS system provides a practical application of Transfer Learning in the field of NLP, specifically for legal documents, enhancing efficiency and reducing domain-specific dataset size.

Python 5 stars

aws-samples/sample-vla-mobile-manipulation-on-aws

Bring-your-own VLA robot learning on AWS: custom task spec, teleop data collection, Bedrock annotation, openpi/OpenVLA-OFT fine-tuning on EC2, rolling-window evaluation, and phase-routed multi-model deployment for a quadruped + 6-DoF arm

Overview: This sample demonstrates the process of bringing your own VLA stack on AWS, focusing on a custom task specification, teleop data collection, Bedrock annotation, fine-tuning on EC2, rolling-window evaluation, and phase-routed multi-model deployment for a quadruped + 6-DoF arm robot. Key features: - Custom four-phase mobile pick-and-place task - Self-built leader-follower teleop rig with dual cameras and operator keypress phase labels - Custom locked-vocabulary schema and multi-frame voting on Amazon Bedrock - Forked openpi: 10-D action fusing locomotion + manipulation, 7-D state, dual-camera input - Rolling-window EE-error protocol (mean / k=0 / k=last) over 30+ checkpoints - Phase-Routed VLA (PR-VLA): route each phase to the model that earns it, async chunk client, on-device safety guards Tech stack: Python Why it matters: This sample provides a comprehensive guide on how to customize every layer of the VLA stack on AWS, including task definition, data collection, annotation, model configuration, evaluation, and deployment, making it a valuable resource for developers looking to build their own VLA stack on the platform. GitHub stars: 5 Repository: aws-samples/sample-vla-mobile-manipulation-on-aws Primary language: Python Architecture: ![Architecture](assets/architecture.png) How this differs from other AWS samples: This sample focuses on customizing every layer of the VLA stack, including task definition

Python 5 stars

blackccpie/GweLLM

Journey towards Fine-Tuning a Breton speaking Chat Model

### Overview: A developer-focused project aimed at fine-tuning a Breton speaking Chat Model. The project began with an initial motivation to create open-source lightweight language models for Breton, focusing on local deployment and ease of use. The primary language used is Python, and the project has gained 5 GitHub stars. ### Key Features: - Fine-tuning of a pretrained Conversational LLM to a new language, specifically Breton. - Development of a French -> Breton translation model called **Gallek**. - Creation of a Breton Instruction Dataset called **Goulenn**. - Development of a Breton Conversational LLM called **GweLLM**. ### Tech Stack: - Python ### Why It Matters: This project aims to bridge the gap between Breton language and technology, making it easier for local developers and enthusiasts to interact with Breton through conversational models. By providing a practical tool for language translation and instruction dataset creation, it democratizes the use of Breton in everyday applications.

Python 5 stars

caiotheodoro/specula

Fine-tune a VL Qwen on FDA food-label compliance: image in, cited PASS|FLAG out. Seeded synthetic labels + CFR verifier-as-oracle + SFT/GRPO.

### Overview: Fine-tune a multimodal Qwen on FDA food-label compliance: image in, cited PASS|FLAG out. This project fine-tunes a 27B Qwen model on FDA regulations to improve compliance with 21 CFR Part 101 and Part 104, using a deterministic oracle for verification. The model is trained on free credits on Modal/GCP, making it accessible and cost-effective. ### Key features: - Multimodal fine-tuning of a Qwen model. - Training on FDA regulations, including 21 CFR Part 101, Part 104, FALCPA allergen rules, and 2026 "healthy" and FOP rules. - Use of a deterministic oracle for verification, ensuring exact flag computation. - Full process automation, including intake, rule application, verdict, and report generation. - Benchmark contracts for fixed taxonomy, scoring functions, and contamination scheme. ### Tech stack: Python ### Why it matters: This project leverages multimodal fine-tuning to improve regulatory compliance, making it approachable and cost-effective. The deterministic oracle ensures that the model's performance is aligned with regulatory standards, providing a high level of accuracy and reliability.

Python 5 stars

caiotheodoro/suture

Fine-tune a VL Qwen on insurance policy-issuance QC: binder vs issued policy images in, structured FLAG/PASS out. Seeded synthetic + verifier-as-oracle + SFT/ReST-EM.

### Overview: Fine-tune a VL Qwen model on insurance policy-issuance QC: compare binder and issued policy images, generating a structured flag/pass output. The model is trained on synthetic data with a verifier-as-oracle and uses structured JSON predictions. ### Key Features: - **Model:** Fine-tuned Qwen/Qwen3-VL-8B-Instruct model. - **Dataset:** Seeded synthetic data for training. - **Oracle:** Verifier-as-oracle for high accuracy. - **Output:** Structured JSON with PASS/FLAG and discrepancy list. - **Recall/Precision:** 0.959, 0.969. ### Tech Stack: - **Model:** Qwen/Qwen3-VL-8B-Instruct - **Dataset:** Seeded synthetic data - **Oracle:** Verifier-as-oracle - **Adapter:** QLoRA on Qwen/Qwen3-VL-8B-Instruct ### Why It Matters: Policy issuance QC is a closed, finite ontology task with exact ground truth. The combination of finite ontology, exact ground truth, and no dedicated model makes it solvable with a small adapter. The same pattern used on ReconForge also works for this task, demonstrating the power of small adapters in closed tasks. ### Result: - **Recall/Precision:** 0.959/0.969 - **High Recall/Precision:** 0.969 - **Deductible Direction:** Closed from 0.507/0.

TypeScript 5 stars

cedrickdev/NeoCraft-Agency-Landing

### Overview: Neocraft Agency Landing est un site professionnel développé avec Next.js 16, React 19, TypeScript, et Tailwind CSS. Il est conçu pour l'agence NeoCraft basée à Lausanne, Suisse, et comprend un système internationalisé, un blog géré par Wisp CMS, et une gestion des erreurs via Sentry. ### Key features: - Next.js 16 pour l'application front-end - React 19 pour l'interface utilisateur - TypeScript pour le type statique - Tailwind CSS pour les styles - Internationalisation via next-intl - Envoi d'emails transactionnels via Resend - Gestion du blog via Wisp CMS - Animations via Framer Motion - Composants UI via shadcn/ui ### Tech stack: Next.js 16, React 19, TypeScript, Tailwind CSS, next-intl, Sentry, Resend, Wisp CMS, Framer Motion, shadcn/ui ### Why it matters: Cette application est un site professionnel conçu pour l'agence NeoCraft, offrant une interface utilisateur moderne, une gestion internationalisée, et un système de monitoring d'erreurs pour assurer la fiabilité du site.

Python 5 stars

chaeminyoon/AIS-Traffic-Ops

An MLOps project for forecasting maritime traffic patterns from AIS data and operating the model through evaluation, serving, and monitoring workflows.

### Overview: AIS Traffic Ops is an MLOps project designed to forecast maritime traffic patterns from AIS data. It preprocesses AIS inflow/outflow arrays into 66x66 grid sequences, experiments with various architectures, tracks model versions, manages the active production model through a `production` symlink, supports hot reload for model replacement, and provides serving, monitoring, model registry, inference logs, and dashboard views. ### Key Features: - Preprocesses AIS inflow/outflow arrays into 66x66 grid sequences. - Experiments with ConvLSTM, temporal attention, multi-head attention, spatial UNet, and gated UNet variants. - Tracks model versions from `v1` through `v16`. - Manages the active production model through a `production` symlink. - Supports hot reload for model replacement without restarting the API server. - Provides serving, monitoring, model registry, inference logs, and dashboard views. ### Tech Stack: - Python 3.10+ - FastAPI serving - Prometheus and Grafana monitoring - MLflow model registry - SQLite inference logs - Custom dashboard ### Why It Matters: This project is crucial for maritime traffic forecasting, enabling more accurate predictions and improved operational efficiency. The use of various architectures and the ability to track and manage model versions ensure that the system remains up-to-date and effective. The integration with Prometheus, Grafana, and MLflow enhances monitoring and reproducibility, making it easier to understand and reproduce the system structure.

Jupyter Notebook 5 stars

chriss1245/mlflow_packaging_samples

Worked examples of packaging PyTorch models with MLflow — native flavour vs custom pyfunc, and what each costs at serving time.

Overview: This repository provides examples of packaging PyTorch models with MLflow, showcasing the differences between the native flavor and a custom `pyfunc` approach. It highlights the costs associated with each choice at serving time. Key features: - Native flavor with `mlflow.pytorch` - Custom `pyfunc` with `mlflow.pyfunc` Tech stack: Jupyter Notebook Why it matters: Understanding the trade-offs between these packaging methods is crucial for optimizing the performance and cost of serving models, especially when dealing with complex preprocessing and postprocessing requirements.

Python 5 stars

daekeun-ml/encoder-serving-sagemaker

Benchmarked comparison of multiple serving patterns for Transformer encoder models (BERT-style classification, NLI) on Amazon SageMaker AI — ONNX CPU, multi-container, inference component, LMI, scale-to-zero

Overview: This repository provides a comprehensive guide on deploying Transformer encoder models, such as BERT-style classification and NLI, on Amazon SageMaker AI. It benchmarks multiple serving patterns to determine the most efficient and cost-effective method for real-time endpoint deployment. Key features: - Benchmarks multiple serving patterns for Transformer encoder models. - Compares latency, throughput, and cost for different deployment scenarios. - Includes real-time endpoint deletion warning. Tech stack: Python Why it matters: This repository helps developers understand the best practices for deploying Transformer encoder models on SageMaker AI, considering factors like latency, throughput, and cost efficiency.

Python 5 stars

daekeun-ml/frontier-moe-serving-benchmark

Serving and benchmarking 9 frontier MoE checkpoints (490 GiB+) with vLLM on a single 8x B200 node, plus a pre-download HBM fit checker

### Overview: This repository serves and benchmarks nine frontier MoE checkpoints, totaling 490 GiB or larger, across eight B200 GPUs. It provides comprehensive serving configurations, benchmark results, and detailed documentation for optimal deployment on a single node. ### Key Features: - Serving configurations for nine frontier MoE checkpoints. - Benchmark results across five scenarios (A.X-K2 supports uv only). - Detailed documentation and reproducibility with one command. - Measured performance and memory usage across multiple checkpoints. - Optimal GPU count and memory estimation for different hardware configurations. ### Tech Stack: Python ### Why It Matters: This repository provides essential information for deploying frontier MoE models on a single node, ensuring accurate performance and memory usage predictions.

Python 5 stars

devjothish/laya-forge

Fine-tune, calibrate and gate Laya (open-weights System One decision model) on your own decisions, then guard production agents with it

Overview: Laya-forge is a tool designed to fine-tune, calibrate, and gate Laya's open-weights System One decision model, allowing developers to secure their agents with a custom-trained model that guards against production issues. Key features: - Fine-tune Laya's open-weights System One decision model on custom decisions. - Calibrate probabilities for production-like questions. - Guard against stock Laya's issues with custom thresholds. Tech stack: Python Why it matters: Laya-forge simplifies the process of securing agents with a custom-trained model, addressing common issues like domain-specific guesses, uncalibrated probabilities, and lack of training data, making it easier for developers to deploy secure agents. Repository: devjothish/laya-forge GitHub stars: 5 GitHub description: Fine-tune, calibrate and gate Laya (open-weights System One decision model) on your own decisions, then guard production agents with it.

Jupyter Notebook 5 stars

drisskhattabi6/Obstacle-Detection-with-Alert-System-for-Visually-Impaired

This Repo contains a Deep Learning project, 'Obstacle Detection with Alert System for the Visually Impaired Using Deep Learning and Node-Red'

### Overview This project introduces a robust solution to assist visually impaired individuals in navigating their environment using an obstacle detection and alert system. The system combines custom-trained deep learning models for obstacle classification and real-time audio feedback via Node-RED, integrated with a Flask API for processing predictions. The dataset used is a custom-built collection of 10 distinct classes of obstacles frequently encountered by visually impaired individuals, ensuring comprehensive coverage of real-world scenarios. ### Key Features - **Custom-Trained Deep Learning Models**: A Convolutional Neural Network (CNN) trained from scratch and fine-tuned MobileNetV2 for obstacle classification. - **Real-Time Alerts**: Audio feedback through Node-RED, integrated with a Flask API to process predictions. - **Flask API**: Backend for processing images and returning obstacle classifications. ### Tech Stack - **Python Libraries**: Flask, OpenCV, requests, torch, torchvision, Pillow ### Why It Matters This project offers a practical and accessible solution for visually impaired individuals, enhancing their safety and independence in everyday environments. The use of deep learning and Node-RED ensures efficient and effective obstacle detection, providing real-time alerts to guide users through potentially dangerous situations. ### Repository Information - **Repository**: drisskhattabi6/Obstacle-Detection-with-Alert-System-for-Visually-Impaired - **GitHub Stars**: 5 - **Primary Language**: Jupyter Notebook - **GitHub Description**: This Repo contains a Deep Learning project, 'Obstacle Detection with Alert System for the Visually Impaired Using Deep Learning and Node-Red

Jupyter Notebook 5 stars

engurs/PyTorch_Notebooks

Overview: This repository contains a collection of Jupyter Notebooks focused on deep learning models such as VGG, ResNet, AlexNet, SqueezeNet, DenseNet, and Inception. These notebooks are designed to be educational and practical, providing a hands-on approach to understanding and implementing these models. Key features: - Implementing VGG, ResNet, AlexNet, SqueezeNet, DenseNet, and Inception models using PyTorch - Detailed explanations and code examples for each model - Practical applications and examples to enhance understanding Tech stack: PyTorch Why it matters: These notebooks serve as a valuable resource for developers and students looking to learn and implement popular deep learning models using PyTorch, a popular library for deep learning in Python.

Python 5 stars

ethicalabs-ai/ouroboros

Self-Improving LLMs Through Iterative Refinement

Overview: This project explores the development of a self-improving artificial intelligence system through recursive refinement, aiming to demonstrate how AI can autonomously refine its own prompts and questions without brute-force scaling. Key features: - Recursive refinement process - Structured reasoning steps extracted from LLM-generated interactions - Structured breakdown of LLM's reasoning process - Final AI-generated response after refinement - Sequence of improvements tracking iterative enhancement Tech stack: Python Why it matters: This project challenges conventional approaches to AGI by demonstrating that intelligence can evolve in self-contained cycles of learning and refinement, exploring the way for a new paradigm of self-sustaining, recursive AI. Repository: ethicalabs-ai/ouroboros GitHub stars: 5 GitHub description: Self-Improving LLMs Through Iterative Refinement

Go 5 stars

gpustack/gpustack-operator

Manage heterogeneous accelerators, vGPU slicing, shared KV cache, model delivery, and inference workloads.

### Overview: GPUStack Operator enables the management and utilization of heterogeneous accelerators on Kubernetes, including GPUs, NPUs, and other accelerators. It provides features such as device discovery, RDMA networking, topology placement, shared cache, model delivery, and model deployment, making it a comprehensive solution for accelerating machine learning workloads. ### Key Features: - **Heterogeneous Devices**: Discover and allocate whole devices, shared devices, logical slices, or hardware partitions. - **RDMA Networking**: Provide RDMA network interfaces alongside accelerators. - **Topology**: Place related workloads within the requested network or location domain. - **KV Cache**: Share inference cache across workloads. - **Model Delivery**: Fetch, verify, and cache model weights on nodes. - **Model Deployment**: Run model serving replicas with routing and optional prefill/decode roles. - **GPU Instances**: Enter accelerator-backed container workspaces over SSH. ### Tech Stack: Go ### Why it Matters: GPUStack Operator simplifies the management and utilization of accelerators on Kubernetes, making it easier to deploy and scale machine learning workloads.