Projects

### Overview: This repository provides interactive visualizations of the GPT-2 Medium model, allowing developers to understand its architecture, attention mechanisms, token activations, and overall structure. ### Key Features: - **Model Summary**: Provides a concise overview of the GPT-2 model's architecture and specifications. - **Token Activations**: Shows the activations of tokens across layers, highlighting the importance of each token. - **Attention Visualization**: Visualizes how the model focuses on different parts of a given text. - **3D Model Architecture**: Offers a spatial representation of the model's layers and neurons. - **Token Embeddings**: Projects token embeddings into 2D or 3D space to understand relationships between tokens. - **Gradients and Saliency**: Visualizes the influence of each token on the model's output using gradients. ### Tech Stack: Jupyter Notebook ### Why It Matters: These visualizations provide valuable insights into the GPT-2 model's operations, helping developers understand its architecture, attention mechanisms, and token processing. They are particularly useful for debugging, interpreting, and appreciating the model's behavior on specific tasks.

Overview: MegaDetector-Classifier is a training toolkit for fine-tuning ResNet-based species classifiers on custom camera-trap image datasets. The output weights integrate directly with the PyTorch-Wildlife framework, making it straightforward to deploy a classifier trained on your own data. Key features: - ResNet-18 and ResNet-50 classifier training using PyTorch Lightning - Three data-splitting strategies designed for camera-trap realities: random, location-based, and sequence-based - YAML-based configuration — no code changes required for most use cases - Demo data included for immediate testing without your own dataset Tech stack: Python Why it matters: MegaDetector-Classifier is part of the Microsoft AI for Good Lab's ecosystem, providing a comprehensive solution for wildlife identification and conservation, making it easier and more accessible for researchers and practitioners to adapt existing classifiers to new geographic regions or add new species to the PyTorch-Wildlife model zoo.

Overview: Ladder is a small open model fine-tuned for competitive programming, designed to run on free GPUs for Codeforces-style problems. It uses QLoRA training and verifiable evaluation, with a focus on running on Kaggle/Colab GPUs. Key features: - Fine-tuned models for competitive programming - Runs on free GPUs for Codeforces-style problems - Verifiable evaluation without relying on another model or BLEU scores - Data prepared using reasoning traces from Codeforces problems - Training on 4-bit QLoRA with loss computed on assistant turns only - Evaluated by generating a program and running it against test cases Tech stack: Python Why it matters: Ladder is designed to provide a reliable and efficient way to fine-tune models for competitive programming, ensuring that the models can be run on free GPUs, making it accessible for a wide range of users. Repository: natedemoss/ladder Primary language: Python GitHub stars: 6 GitHub description: Ladder: small open models fine-tuned for competitive programming. QLoRA training + verifiable eval that runs on Kaggle/Colab GPUs.

### Overview: This is a fine-tuned version of the Mistral-7B model on the Kto-12 dataset, achieving impressive results with a loss of 0.2786, rewards of 5.3903, and logps of -59.0879. The model is designed for LLM continual learning and long-term memory. ### Key Features: - Fine-tuned Mistral-7B model - Achieves high performance on evaluation set - Implements continual learning and long-term memory capabilities - Optimized for LLMs' continuous training and memory retention ### Tech Stack: - Jupyter Notebook ### Why It Matters: This model is designed for LLMs' continual learning and long-term memory, making it ideal for applications requiring continuous adaptation and memory retention. Its performance on the Kto-12 dataset is impressive, indicating its effectiveness in real-world scenarios. ### GitHub Stars: 6 ### Repository: - Repository: neoheartbeats/neoheartbeats-kernel - Primary Language: Jupyter Notebook - GitHub Stars: 6

Overview: MooseNet is an official repository for a trainable metric for synthesized speech, specifically designed with a PLDA module, aiming to improve speech synthesis performance. Key features: - MooseNet: A trainable metric for synthesized speech. - PLDA Module: Enhances the performance of speech synthesis models. Tech stack: Python Why it matters: MooseNet is significant for speech synthesis research, offering a new approach to improving the quality and realism of synthesized speech through the use of a trainable metric and a PLDA module, which can lead to advancements in natural language processing and speech synthesis technologies. Repository: oplatek/moosenet-plda Primary language: Python GitHub stars: 6 GitHub description: Official repo for “MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module” README excerpt:

Overview: PaglaMLX is a macOS menu-bar application designed to orchestrate local and cloud-based machine learning models, providing a smart reverse proxy for efficient model access and management. Key features: - Local text generation models - Cloud fallback routing - Native protocol translation on Apple Silicon - Smart Gateway (`:2525/v1`) supporting OpenAI and Anthropic - Model orchestration with one-click load/unload from a directory - Live route table updates for model availability - One-click integrations with popular development tools Tech stack: Swift, Python 3.12+, macOS 14.0+, Apple Silicon (M1/M2/M3/M4) Why it matters: PaglaMLX simplifies the process of managing and accessing machine learning models on macOS, providing a unified API that integrates seamlessly with existing workflows, making it easier to test and deploy local models without the need for manual server setup and port management. Repository: paglaai/PaglaMLX GitHub stars: 6 GitHub description: Local LLM orchestration for Apple Silicon — native macOS menu-bar app serving MLX models through a smart reverse proxy with Auto-Router, BYOK, and 14+ integration targets.

### Overview: RelayOps is a production-shaped control plane designed for AI support agents in telecom and subscription billing. It includes scoped permissions, route safety, decision traces, audit export, and human handoff features to ensure safe, auditable, and handoff-ready outcomes in regulated domains. ### Key Features: - **Scoped Permissions**: Ensures that agents can only perform actions within predefined scopes. - **Route Safety**: Ensures that routes are safe and efficient. - **Decision Traces**: Records every state and decision made by the broker. - **Audit Export**: Provides an audit trail of every action taken. - **Human Handoff**: Allows for the final reply to be generated from the broker decision and recorded in the audit trail. ### Tech Stack: - Python: The primary programming language used for development. - Streamlit: Used for the live demo and interactive features. ### Why It Matters: RelayOps treats AI support as a control system, ensuring safe, auditable, and handoff-ready outcomes in regulated domains. It mirrors production controls, including access gates, scoped tools, guardrails, audit trails, and human handoff, making it a robust solution for AI support agents in telecom and subscription billing. ### GitHub Stars: 6 ### Repository: - **Repository**: patibandlavenkatamanideep/relayops - **Primary Language**: Python - **GitHub Stars**: 6 ### Live Demo: - **Demo**: [relayops-production.up.railway.app](https://relayops-production.up.railway.app) ### Additional Resources: - **

### Overview: FinChain is an AI-powered chat-driven expense tracker designed to help you manage your finances in any language without the need for explicit category selection. By simply typing your expenses in any language, FinChain automatically categorizes and analyzes them using an LLM-based NLP backend powered by Hugging Face Transformers. This innovative approach eliminates the need for clunky forms or manual categorization, making expense tracking more intuitive and efficient. ### Key features: - **Chat Your Expenses, Any Language:** Type "Spent 670 on burgers today", "বার্গার খাইসি ১০০০ টাকা দি

### Overview: This project implements Reinforcement Learning from Human Feedback (RLHF) on custom data, replicating the pipeline described in the paper "Learning to Summarize from Human Feedback" by OpenAI. The RLHF process involves three key steps: Supervised Fine-Tuning (SFT), Reward Model Training, and Policy Optimization, enabling the model to generate summaries that align with human preferences. ### Key Features: - Supervised Fine-Tuning (SFT) - Reward Model Training - Policy Optimization ### Tech Stack: Jupyter Notebook ### Why It Matters: Traditional supervised learning for summarization relies on human-labeled datasets, but RLHF addresses the limitations of this approach by using a reward model to capture human preferences, fine-tuning the model with reinforcement learning, and dynamically adapting to evolving human feedback. This method ensures the model generates responses that are not only accurate but also highly aligned with what users value, making it suitable for custom domains and handling ambiguous or subjective tasks. ### Repository: ranzeet013/RLHF-CustomData ### GitHub Stars: 6 ### GitHub Description: Building an LLM with RLHF involves fine-tuning using human-labeled preferences. Based on Learning to Summarize from Human Feedback, it uses supervised learning, reward modeling, and PPO to improve response quality and alignment.

Overview: This project is a sample implementation of FastAPI serving a PyTorch model using micro batching. It demonstrates the use of Service Streamer for efficient model serving. Key features: - Micro batching for model serving - FastAPI integration - PyTorch model with micro batch processing - Service Streamer for deployment Tech stack: Python, FastAPI, PyTorch, Service Streamer Why it matters: This project showcases how to efficiently deploy and serve PyTorch models using FastAPI and micro batching, highlighting the benefits of using micro batching for better performance and resource utilization.

### Overview: tunelab is a Claude Code plugin designed to fine-tune, distill, and evaluate Large Language Models (LLMs) locally. It helps developers decide whether fine-tuning is necessary, distills LLM logs into small local models using MLX/LoRA, and verifies the models' performance on held-out data before deployment. ### Key Features: - **Fine-Tuning Locally**: Tunelab fine-tunes LLMs on local models using MLX/LoRA, ensuring the models are more accurate and efficient. - **Distillation of Logs**: It distills LLM logs into small local models, making it easier to fine-tune. - **Accuracy Verification**: Before deploying, it verifies the models' performance on held-out data, ensuring the best model is chosen. - **Versatile Use Cases**: Suitable for various tasks such as classification, extraction, and more. ### Tech Stack: Python ### Why It Matters: - **Accuracy and Efficiency**: Tunelab ensures that the models are more accurate and efficient, reducing the cost of fine-tuning. - **Versatility**: It can be used with Claude Code, Gemini CLI, or any other tool that reads skills, making it highly versatile. - **Cost-Effective**: It provides a cost-effective solution by fine-tuning models locally, reducing the need for expensive fine-tuning on the cloud. ### Repository: rchaz/tunelab ### GitHub Stars: 6 ### GitHub Description: Claude Code plugin for LLM fine-tuning, distillation, and evaluation —

Overview: CTF_Agents is a multi-agent framework designed for authorized Capture The Flag work, supporting various security challenges across different domains. Key features: - Cryptography - Quantum protocols - Adversarial AI/ML - Industrial control and operational technology - Reverse engineering - Web challenges - Pwn challenges - Hardware challenges - Forensics - Blockchain challenges - Secure coding challenges - Networking challenges - OSINT challenges - Log-analysis challenges - General coding challenges Tech stack: Python Why it matters: CTF_Agents provides a comprehensive framework for authorized CTF work, supporting a wide range of security challenges across different domains. The project's ability to route challenges to the appropriate specialist and execute bounded tools and playbooks makes it highly versatile and useful for both training and real-world scenarios. Repository: rmjohnson12/CTF_Agents Primary language: Python GitHub stars: 6 GitHub description: Agentic CTF security tooling project with natural language support README excerpt: # CTF_Agents CTF_Agents is a multi-agent framework for authorized Capture The Flag work. It classifies a challenge, selects a specialist, executes bounded tools and playbooks, records evidence, and reports either a verified result or a concrete failure reason. The project supports cryptography, quantum protocols, adversarial AI/ML, industrial control and operational technology, reverse engineering, web, pwn, hardware, forensics, blockchain, secure-coding, networking, OSINT, log-analysis, and

## Overview: This project fine-tunes OpenAI’s primary models to produce JSON-formatted output, enhancing cost-efficiency and data consistency for applications that utilize the OpenAI API. ## Key features: - Fine-tuning of OpenAI models - JSON output format - Node.js implementation ## Tech stack: JavaScript (Node.js) ## Why it matters: Improves efficiency and consistency in applications that use the OpenAI API, reducing costs and ensuring data quality.

Overview: This repository contains a comprehensive collection of ML-related active learning datasets, including algorithms, Jupyter Notebook (.ipynb) pipelines, Python (.py) scripts, and curated, ethically aligned synthetic data. The repository is structured into three main modules for easy understanding, reproducibility, and experimental training. Key features: - Synthetic Datasets: Ethically aligned synthetic datasets for active learning experiments. - Inference Generation Scripts: Python scripts for generating synthetic datasets and executing active learning runs. - Raw Reasoning (Jupyter Notebooks): Detailed Jupyter Notebook files for experimental active learning setups, providing context and full visibility into the reasoning and decisions made. Tech stack: Jupyter Notebook Why it matters: This repository serves as a valuable resource for researchers and practitioners in the field of active learning, offering a structured and transparent way to access and experiment with ML datasets and active learning algorithms.

Overview: Formless is a proof-of-concept application designed to simplify client onboarding by utilizing AI prompts and a Rails application. It eliminates the need for extensive form filling, making the process more efficient and user-friendly. Key features: - AI-driven client onboarding process - User prompts for structured responses - Secure and privacy-focused data handling - Rails-based application for client object creation and user request management Tech stack: Ruby on Rails, OpenAI API, Rails Caching, Sidekiq, Octokit, Devise, Kaminari, RSpec, Factorybot-rails, Ruby-openai Why it matters: Formless addresses the inefficiencies of traditional client onboarding processes by automating the initial data entry, reducing the burden on users and streamlining the onboarding experience.

Overview: This repository contains two independent crates, `bonsai-pot` and `bonsai2-pot`, designed to run from-scratch, dependency-light inference engines for the Bonsai model family. These crates utilize wgpu compute shaders to achieve fast performance on consumer GPUs, with realistic throughput numbers. The repository is structured with a shared workspace manifest and includes benchmarks, architecture notes, and usage details. Key features: - No dequantization of weights, directly consuming packed weights for matvec/matmul kernels. - Fast performance on consumer GPUs with realistic throughput. - Small, embeddable library with tokenization kept separate from Rust crates. - Tokenization is handled by `scripts/bpe.py` in each crate and can be piped into CLI. Tech stack: Rust + wgpu. Why it matters: This repository provides a lightweight, fast inference engine for the Bonsai model family, suitable for consumer GPUs, and includes detailed benchmarks and architecture notes for optimal performance. Repository: ruihe774/bonsai-pot GitHub stars: 6 GitHub description: A from-scratch, dependency-light, dequant-free Bonsai (Qwen3-architecture) Q1_0 inference engine running on wgpu compute shaders

Overview: This script provides a framework for miners to fine-tune models using the dataset generated by validators in the Bittensor ecosystem. Key features: - Dataset generated by validators - Models used by miners - Miners train models on their own compute (GPU) - Model can be used as the base for Bittensor miners Tech stack: Python Why it matters: This script offers a practical solution for miners to fine-tune models using the validated dataset, potentially improving the performance of their Bittensor miners. Repository: salahawk/bittensor-model-finetune Primary language: Python GitHub stars: 6 GitHub description: Model Fine-tuning for Bittensor miners using the dataset generated by validators

Overview: This repository contains an implementation of YOLOv8 trained to detect fire, using the YOLO algorithm for real-time object detection. Key features: - Fire detection using YOLOv8 - Real-time object detection - Video stream processing Tech stack: Python Why it matters: This project showcases the application of YOLOv8 for real-time fire detection, which is crucial in ensuring safety and security in various environments.

Overview: This project aims to develop a streamlit application that utilizes deep learning neural networks to classify diseases in chicken fecal images. The application will help detect diseases such as Salmonella, Newcastle, and Coccidiosis, which are common in poultry farming systems, and contribute to early detection and control of diseases, thereby improving farm productivity and reducing mortality rates. Key features: - Streamlit application for real-time disease classification - Convolutional Neural Network (CNN) model for image classification - Transfer Learning and Fine-tuning techniques for improved accuracy - Image augmentation for balanced data distribution Tech stack: Python (Jupyter Notebook), TensorFlow, Keras, Streamlit Why it matters: This project is crucial for enhancing the efficiency and productivity of small-scale poultry farming in Thailand, particularly in regions like Arusha and Kilimanjaro in Tanzania. By providing an early detection system, it helps prevent the spread of diseases and ensures better quality control, ultimately benefiting the Thai poultry industry and the economy as a whole.

### Overview: This project demonstrates how to fine-tune the PaliGemma model for image captioning, a model developed by Google Research designed to handle images and generate corresponding captions. ### Key features: - Fine-tuning of PaliGemma model - Customizable dataset for training and validation - Streamlit interface for uploading images and generating captions ### Tech stack: Jupyter Notebook ### Why it matters: This project provides a practical guide for developers interested in image captioning and fine-tuning machine learning models, offering a step-by-step approach to training and using the PaliGemma model effectively.