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Project directory

Projects

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

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

VGG | Resnet | Alexnet | Squeezenet | Densenet | Inception

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
Jupyter Notebook 5 stars

guy1998/1998-prompts

Fine tuned LLaMA model for prompt enhancing

Overview: This project fine tunes a LLaMA model for prompt engineering, addressing the lack of time and resources for personal prompt enhancement. Key features: - Fine tunes a LLaMA model for prompt engineering - Uses gradient accumulation for training stability - Supports quantization and inference for broader usage Tech stack: Jupyter Notebook Why it matters: The project aims to provide a quick and efficient solution for prompt engineering, addressing the need for prompt enhancement without the limitations of existing solutions. Repository: guy1998/1998-prompts Primary language: Jupyter Notebook GitHub stars: 5 GitHub description: Fine tuned LLaMA model for prompt enhancing
Python 5 stars

harsha-gouru/gpu-inference-playground

Benchmark LLM inference techniques on GPU — KV cache, quantization, FlashAttention, speculative decoding, continuous batching. Runs on H100/MI300X via Modal/RunPod.

### Overview: Benchmark and experiment with LLM inference techniques on GPU. Run a script, get results, learn. ### Key features: - KV cache optimization - fp16 vs int8 vs int4 tradeoff - Throughput scaling with batch size - FlashAttention2 vs SDPA vs eager attention - Draft+verify speculative decoding - Continuous batching for vLLM models ### Tech stack: Python ### Why it matters: Learn and benchmark different LLM inference techniques on GPU, including KV cache optimization, fp16 vs int8 vs int4 tradeoff, batch size scaling, FlashAttention2 vs SDPA vs eager attention, speculative decoding, and continuous batching for vLLM models.
Jupyter Notebook 5 stars

hassanzadehmahdi/Sarcasm-detection-in-social-networks-in-Persian-language

This is an NLP project, where I am attempting to detect sarcasm in social networks in Persian language.

Overview: This project aims to detect sarcasm in social networks using the Persian language, utilizing cutting-edge techniques such as transformers and the ParsBERT model. Key features: - Detecting sarcasm in Persian language social networks - Utilizing cutting-edge NLP techniques - ParsBERT model for efficient sarcasm detection Tech stack: Python, Transformers, ParsBERT Why it matters: This project aims to develop an accurate and efficient solution for detecting sarcasm within various social networks, leveraging the power of natural language processing (NLP).
TypeScript 5 stars

jbogocz/awesome-automated-ai

A curated list of 250+ tools for Automated AI/ML - from hyperparameter optimization to autonomous AI agents.

### Overview: This curated list offers a comprehensive collection of tools and resources for Automated AI/ML, covering everything from hyperparameter optimization and neural architecture search to autonomous AI agents and automated monitoring. The repository is updated weekly, reflecting the rapid evolution of the field, including advancements in foundation models, autonomous AI, and prompt engineering. ### Key features: - **General-Purpose AutoML**: 22 tools - **Neural Architecture Search**: 6 tools - **Hyperparameter Optimization**: 11 tools - **Automated Feature Engineering**: 9 tools - **Tabular Foundation Models**: 8 tools - **Automl Benchmarks**: 7 tools ### Tech stack: TypeScript ### Why it matters: This list serves as a valuable resource for anyone looking to automate their machine learning pipelines, from ML engineers to researchers and product builders. It covers both the classical tools and the emerging trends in the field, ensuring that the latest advancements are included.
Jupyter Notebook 5 stars

julie-berlin/aie-cohort-7

AI Engineering Bootcamp

Overview: This repository is designed to provide comprehensive training on AI Engineering, focusing on the skills required to build, deploy, operate, and improve Large Language Model (LLM) applications in production environments. Key features: - Prototyping and productionizing LLM application prototypes - Understanding Prompt Engineering, Retrieval Augmented Generation (RAG), Agents, and Fine-Tuning - Productionizing includes evaluating RAG and Agent Applications, improving Search and Retrieval Pipelines, monitoring Production KPIs, setting up Inference Servers, and building LLM Applications with Scalable, Production-Grade Components Tech stack: Jupyter Notebook Why it matters: This bootcamp is essential for data science and engineering teams aiming to successfully build, deploy, operate, and improve LLM applications in production environments, ensuring they are able to build, deploy, monitor, and scale complex LLM applications in production environments. Repository: julie-berlin/aie-cohort-7 GitHub stars: 5 GitHub description: AI Engineering Bootcamp
Python 5 stars

kenza-ily/diagnose_treat_bias_llm

NAACL 2025 | How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?

### Overview This repository contains the **Counterfactual Patient Variations (CPV)** framework, a method for detecting and quantifying gender and ethnicity biases in Large Language Models (LLMs) applied to clinical decision-making. CPV constructs counterfactual patient vignettes from medical QA datasets by varying demographic attributes (gender, ethnicity) while holding the clinical content fixed. Bias is measured by comparing LLM outputs across these variations using a suite of lexical and semantic metrics. ### Key Features - **Counterfactual Patient Variations (CPV)**: Constructs counterfactual patient vignettes from medical QA datasets by varying demographic attributes while holding the clinical content fixed. - **Gender and Ethnicity Bias Detection**: Measures bias by comparing LLM outputs across demographic variations. - **Public Datasets**: Three public datasets available on HuggingFace, each built from different medical QA benchmarks: MedQA CPV, MedMCQA CPV, and PubMedQA CPV. ### Tech Stack - Python ### Why It Matters This framework is essential for ensuring fairness and accuracy in clinical decision-making models, particularly in medical applications where patient outcomes can be significantly impacted by demographic factors. By quantifying and detecting bias, it helps in developing more reliable and unbiased AI systems that can improve patient care. ### Installation ```bash git clone https://github.com/kenza-ily/diagnose_treat_bias_llm.git cd diagnose_treat_bias_llm pip install -e . ``` ### Citation ```bibtex @inproceedings{benkirane20
Python 5 stars

khiembk/RECRAFT

Official Implementation of paper "Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting", which is accepted at AISTATS 2026

Overview: RECRAFT is an algorithm designed to optimize the interaction between feature alignment and target fitting for cross-modal fine-tuning, achieving state-of-the-art performance on NAS-Bench-360 and PDEBench benchmarks. Key features: - Feature Alignment (FA) + Feature-Label Distortion (FLD) - Target Predictor Optimization Tech stack: Python Why it matters: RECRAFT provides a principled approach to cross-modal fine-tuning that minimizes the semantic gap by optimizing the interaction between feature alignment and target fitting, making it highly effective for adapting pre-trained models to unseen feature modalities. Repository: khiembk/RECRAFT GitHub stars: 5 GitHub description: Official Implementation of paper "Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting", accepted at AISTATS 2026.
Python 5 stars

kimdanny/event-driven-model-serving

Unified API of Apache Kafka and Google PubSub

### Overview: Unified API for Apache Kafka and Google PubSub, enabling efficient data handling and model serving. ### Key Features: - **Unified API**: Simplifies integration of Kafka and PubSub for event-driven model serving. - **Cloud Configuration**: Configures Kafka and PubSub for seamless integration. - **Model Checkpoints**: Stores model checkpoints for model server. - **Queue Simulation**: Simulates Redis queue for model server. - **Database Simulation**: Simulates a simple database for application 2. ### Tech Stack: Python ### Why it Matters: This project provides a unified and efficient solution for integrating Kafka and PubSub, making it easier to manage and scale event-driven model serving systems.
Jupyter Notebook 5 stars

klay-liu/Financial-Intent-Understanding-with-LLMs

🎯 Fine-tuning LLMs using LlamaFactory for financial intent understanding | Evaluating open-source models on OpenFinData benchmark | Full implementation with multiple models (Qwen2.5/ChatGLM3/Baichuan2/Llama3)

### Overview: 本项目展示了如何使用LlamaFactory微调大语言模型以解决金融领域的特定任务。我们选取了开源金融评测数据集[OpenFinData](https://github.com/open-compass/OpenFinData)中的金融意图理解任务作为示例，评估了主流开源大模型在该任务上的能力，并探索了LoRA微调对模型性能的提升效果。 ### Key features: - 使用LlamaFactory微调大语言模型 - 评测主流开源模型在金融意图理解任务上的能力 - 探索LoRA微调对模型性能的提升效果 - 提供一个完整的LLM微调实践案例 - 评估主流开源模型在金融意图理解任务上的能力 - 探索LoRA微调对模型性能的提升效果 - 为金融领域LLM应用提供参考 ### Tech stack: Python 3.8+ ### Why it matters: 通过本项目，开发者可以了解如何利用开源工具和模型进行金融领域的微调，从而提升模型在实际应用中的表现。此外，通过对比不同模型的性能，开发者可以更好地选择适合自己需求的模型，提高应用效果。
Jupyter Notebook 5 stars

knetic0/turkish-store-reviews-sentiment-analyses

🔥 Sentiment analysis on Turkish store reviews using LSTM, BiLSTM, and GRU models. Interactive UI built with Gradio.

### Overview: This project analyzes sentiment (positive, neutral, negative) on Turkish store reviews using LSTM, BiLSTM, and GRU models. The user interface is built with Gradio for interactive analysis. ### Key features: - Sentiment analysis on Turkish store reviews - Multiple models: LSTM, Bidirectional LSTM, GRU - Gradio for interactive UI - 440,679 training data, 48,965 test data ### Tech stack: Jupyter Notebook ### Why it matters: This project provides a comprehensive solution for sentiment analysis of Turkish store reviews, utilizing advanced machine learning techniques and an interactive user interface for easy analysis.
Python 5 stars

l11x0m7/LMPresent

Including pre-trained language models for fine-tuning on other NLP tasks

Overview: This repository includes pre-trained language models (BERT, ELMo, and GPT) for fine-tuning on various NLP tasks, such as the RACE dataset, WikiQA, SQuAD, and ROCStories. Key features: - Pre-trained models for fine-tuning on the RACE dataset - Fine-tuning experiments on WikiQA and SQuAD - Detailed results and analysis for BERT, GPT, and ELMo models - Experiments on the ROCStories dataset Tech stack: Python Why it matters: This repository provides a comprehensive set of pre-trained models and fine-tuning experiments, making it a valuable resource for extending pre-trained models to other NLP tasks. Repository: l11x0m7/LMPresent GitHub stars: 5 GitHub description: Including pre-trained language models for fine-tuning on other NLP tasks
Python 5 stars

leonvanbokhorst/friction-reasoning

A friction reasoning conversational data set generator and reasoning model fine-tuning

### Overview: Pushback-AI is a comprehensive toolkit designed for generating and fine-tuning datasets for disagreement-driven AI. It includes synthetic dialogue generation, persona orchestration, dataset packaging, fine-tuning using LoRA and DeepSeek-R1, GGUF export, and deployment via Hugging Face and Ollama. ### Key Features: - **Disagreement-first dataset synthesis**: Six contrasting agent personas (skeptical, memory-rich, mechanical, contrarian, humble, synthesizer) to create productive disagreement. - **Prompt templating system**: Enforces gestures, hesitation, and persona tone. - **LoRA fine-tuning pipeline**: Targeting DeepSeek-R1 distilled models using Unsloth. - **Deployment tooling**: GGUF export for Hugging Face/Ollama publication. ### Tech Stack: Python ### Why it Matters: The end-to-end workflow (generation → training → export) is tested on **Windows 11 WSL2** with Python 3.10–3.12. macOS builds may require alternative GPU backends (e.g., replacing `bitsandbytes`). ### Repository: - **GitHub**: [leonvanbokhorst/friction-reasoning](https://github.com/leonvanbokhorst/friction-reasoning) - **GitHub Stars**: 5 - **GitHub Description**: A friction reasoning conversational data set generator and reasoning model fine-tuning ### Documentation Hub: - **Phase Docs**: - 01: Synthetic dataset pipeline - 02: Prompt
HTML 5 stars

m1ns09/Llama

🌐 Run GGUF models directly in your web browser using JavaScript and WebAssembly for a seamless and flexible AI experience.

Overview: Llama is a web application that allows you to chat with a friendly AI character directly in your browser. It utilizes cutting-edge artificial intelligence for seamless and flexible conversations. Key features: - Interactive Conversations - User-Friendly Interface - Customizable Settings - Fast Response Times Tech stack: JavaScript, WebAssembly Why it matters: Llama provides a unique and engaging way to interact with AI, offering a natural and flexible chat experience directly in your browser.
Python 5 stars

marcelo-earth/light-tuning

🤖 CLI to fine-tune GPT-3.5 Turbo models rapidly. 💬

Overview: LightTuning is a CLI tool designed to rapidly fine-tune GPT-3.5 Turbo models, making it easier to scale and enhance models from small conversations to high-performance models that outperform GPT-4. Key features: - Generate dataset - Upload dataset - Fine-tune model - All Tech stack: Python Why it matters: LightTuning simplifies the process of fine-tuning GPT-3.5 Turbo models, making it accessible and efficient for developers and enthusiasts alike. Repository: marcelo-earth/light-tuning Primary language: Python GitHub stars: 5 GitHub description: 🤖 CLI to fine-tune GPT-3.5 Turbo models rapidly. 💬 README excerpt: 🚀 Concept: LightTuning is a quick way to fine-tune GPT-3.5 Turbo models. 🧑‍💻 Usage: Install the required packages, run the script, and choose actions like generating a dataset, uploading a dataset, or fine-tuning the model. 📃 License: Distributed under the MIT License.
Jupyter Notebook 5 stars

merishnaSuwal/nep-off-langdetect

Offensive language detection and sentiment analysis on Nepali text

Overview: This repository contains the code and datasets for the Nepali Offensive Language Detection and Sentiment Analysis project, focusing on the identification of offensive entities and sentiment classification in Nepali text. Key features: - In-depth exploration of linguistic challenges in offensive language detection for a multilingual context. - Evaluation of pre-trained BERT architectures for Nepali, with a particular emphasis on Aspect term extraction using Named Entity Recognition (NER) and comprehensive analysis of sentiment classification in detecting Offensive Nepali language. Tech stack: Python 3.x, Jupyter Notebook, PyTorch, Transformers, scikit-learn, pandas, numpy, matplotlib, seaborn, and any other dependencies mentioned in the notebooks. Why it matters: This project aims to enhance content moderation tools and create safer online spaces for the Nepali-speaking community by addressing the limitations of pre-trained models in multilingual contexts.
Jupyter Notebook 5 stars

morteza89/NLP-LLM

Overview: This repository explores cutting-edge methods in Natural Language Processing (NLP) and Large Language Models (LLMs), focusing on techniques such as Combined Topic Modeling (CTM), fine-tuning Llama 2 for sentiment analysis, and constructing multimodal RAG pipelines. Each section delves into specific aspects of NLP-LLM, from topic discovery to sentence sentiment classification and translation, showcasing the practical applications of advanced NLP techniques. Key features: - Fine-tune Llama 2 for sentiment analysis - Construct multimodal RAG pipelines - Sentence sentiment classification with DistilBERT - Sentence-to-sentence translation - Design of RAG using Llama-2, Langchain, and Chromadb Tech stack: Jupyter Notebook Why it matters: These projects highlight the practical applications of cutting-edge NLP techniques and demonstrate the synergistic integration of various tools and frameworks to solve complex linguistic challenges, pushing the boundaries of what can be achieved in the field of natural language understanding and generation. Repository: morteza89/NLP-LLM GitHub stars: 5 GitHub description: No description provided. README excerpt: # NLP-LLM ### This repository is an ongoing project aimed at exploring state-of-the-art (SOTA) methods in NLP, and will continue to evolve with more practical algorithms and approaches... For Natural Language Processing (NLP) and Large Language Models (LLMs), the exploration and implementation of innovative methodologies like Combined Topic Modeling (CTM), fine-tuning Llama 2 for sentiment analysis, constructing multimodal