Projects

Overview: PulmoScope is a comprehensive AI system designed for pulmonary lesion detection and localization on chest CT scans. It integrates three independent models to provide a multi-stage decision pipeline, ensuring that the user is informed when the model's confidence is not reliable. Key features: - **Multi-model decision pipeline.** Combines three models with different roles to provide a multi-stage decision. - **Disagreement-aware output.** When the classifier and segmenter disagree, the system returns `uncertain` and indicates which model flagged the slice. - **Risk-adaptive sensitivity.** Adjusts the classifier threshold based on the questionnaire risk score. Tech stack: Python, TensorFlow, FastAPI, scikit-learn, OpenCV, MIT License, Educational Use Only. Why it matters: PulmoScope addresses the issue of model failure by providing a clear indication when a model's confidence is not reliable, ensuring that the user is informed and can make an informed decision.

Overview: AIOpsServing 是一个开源项目，旨在为算法为中心的研发、测试、评估和服务提供轻量级流程支持。 Key features: - 支持多种计算后端，包括阿里云函数计算（FC）、大数据平台 ODPS、分布式平台 Ray 和容器 Docker。 - 负责算法迭代、归档、发布和回滚的生命周期管理。 - 提供离线和在线算法性能评估。 - 计划整合异常检测、行为画像等模型仓库。 Tech stack: Python Why it matters: AIOpsServing 为算法中心的研发、测试、评估和服务提供了一个轻量级的流程支持，简化了模型的发布、生命周期管理和性能评估，提高了开发效率和质量。

Overview: AloLab is an iterative optimization tool designed to improve the accuracy and structure of AI agent models, specifically focusing on small language models. It automates the process of refining prompts to ensure models produce reliable and structured output. Key features: - Automatically optimizes prompts for accuracy and structure. - Uses real data to detect and correct errors in the model. - Improves the prompt using an optimizer LLM. - Iterates until performance converges. Tech stack: Python Why it matters: AloLab streamlines the optimization process for AI models, ensuring they become more accurate and structured without the need for fine-tuning, thus reducing fine-tuning costs and improving the overall quality of the output. Repository: alomana-lab/alolab GitHub stars: 6 GitHub description: Iterative optimization for AI agents. Turn models into accurate, structured, production-ready systems.

Overview: This project demonstrates how to stream LLM responses using AWS API Gateway Websockets and Lambda. Key features: - AWS API Gateway Websockets - Lambda function to process and stream LLM responses - Integration with AWS SNS for message delivery Tech stack: Node.js Why it matters: This project provides a practical example for developers looking to integrate LLMs with AWS services for real-time communication.

### Overview: This project focuses on Word Sense Disambiguation (WSD) for the Word-in-Context (WiC) disambiguation task. Two BERT-based models are proposed: a feature-based approach and a fine-tuning approach. The models are trained using GlossBERT, an adaptation of the BERT model for WSD tasks. The project aims to improve disambiguation accuracy by experimenting with different approaches and leveraging pre-trained models. ### Key features: - Two BERT-based models for WiC disambiguation - Feature-based and fine-tuning approaches - GlossBERT adaptation for WSD tasks - Experimenting with different WSD models and approaches ### Tech stack: Python ### Why it matters: This project contributes to the field of natural language processing by improving the accuracy of Word Sense Disambiguation (WSD) tasks, specifically for the Word-in-Context (WiC) scenario. By experimenting with different models and approaches, it aims to enhance the current state-of-the-art in WSD for WiC tasks, potentially leading to better applications in text analysis and information retrieval.

### Overview: This project implements a Universal Fine-Tuning Pipeline for the NVIDIA NitroGen model, designed to work with any gameplay dataset generated via the NitroGen/BizHawk toolchain. It supports the model's Diffusion Transformer architecture, enabling efficient fine-tuning on consumer hardware. ### Key Features: - **Universal Architecture**: Automatically handles datasets from any console (NES, SNES, PS3, Xbox) using a dynamic gamepad serialization strategy. - **DiT Support**: Specifically engineered for NitroGen's Diffusion Transformer architecture, predicting a time-horizon of actions from a single frame. - **Continuous Action Space**: Maps inputs to a rigorous 21-dimensional continuous vector space (Analogs + Buttons). - **LoRA Integration**: Uses Low-Rank Adaptation (PEFT) for efficient fine-tuning on consumer hardware. ### Tech Stack: Python ### Why it Matters: This project is essential for developers looking to integrate artificial intelligence into retro gaming, making it easier to create AI agents that can learn from gameplay data. The universal architecture and DiT support make it adaptable to various consoles, while the continuous action space ensures a robust learning environment.

Overview: This project profile details the performance of a 26B mixture-of-experts model running on a CPU, achieving a token throughput of 124 tokens per second (tokens/sec) with no GPU. The model was built from scratch using only the CPU, achieving a speed of 40 tokens/sec for a single stream and 124 tokens/sec for serving multiple requests simultaneously. Key features: - Running Gemma-4 26B at 124 tokens/sec on a CPU with no GPU - Lossless token throughput, meaning the output tokens are exactly the same as those produced by a regular Q4_0 model - Speculative decoding unlocks significant performance gains - Running 3 out of 8 experts to improve perplexity on raw text Tech stack: Shell Why it matters: This project showcases the potential of self-hosting large language models without relying on GPUs, demonstrating the feasibility of achieving high performance on a regular desktop setup.

**Overview:** This project provides a framework for evaluating and comparing different customization techniques for foundation models, including RAG, fine-tuning, and a hybrid approach combining both methods. **Key features:** - Evaluates performance, cost, and latency across RAG, fine-tuning, and hybrid approaches. - Utilizes a synthetic dataset for experimentation. - Supports customization of the environment settings, knowledge base configuration, data source configuration, RAG, fine-tuning, and evaluation configurations. **Tech stack:** Python **Why it matters:** This framework helps developers determine the most effective approach for specific use cases by analyzing performance, cost, and latency across different customization techniques, ultimately aiding in the optimization of foundation models for business needs.

Overview: This project is a PyTorch implementation for fine-tuning the Segment Anything Model (SAM) on medical images, specifically designed for gastrointestinal endoscopy image segmentation. It includes comprehensive data preprocessing, multi-GPU distributed training, and model evaluation tools. Key features: - Fine-tuning SAM on custom medical datasets - Single-GPU and multi-GPU distributed training - Comprehensive data preprocessing and augmentation - Model evaluation and visualization tools Tech stack: Python Why it matters: This project provides a complete pipeline for fine-tuning SAM on medical images, making it a valuable resource for researchers and developers working in medical image segmentation.

### Overview A reasoning critic evaluates the quality of reasoning behind an answer, not just its correctness. This project fine-tunes open-source LLMs, such as Qwen and Mistral, into this role using a corpus of hand-written critique examples and external instruction data. The models are evaluated against a held-out benchmark to assess their performance. ### Key Features - Fine-tuning of open-source LLMs into reasoning-process evaluators. - Evaluates the quality of reasoning behind an answer. - Uses a corpus of hand-written critique examples mixed with external instruction data. - Evaluates against a held-out benchmark. - Two models were trained on identical data and compared head-to-head. ### Tech Stack Python ### Why it Matters Fine-tuning pulled both models toward the capability level embedded in the training data, from opposite directions. Qwen's trap detection came down (0.975 → 0.900, mild forgetting) while Mistral's came up (0.800 → 0.850, a gain): they converged toward the corpus. Format proved base-dependent: Mistral followed the output contract from the system prompt alone (base verdict rate 1.000), while Qwen needed the fine-tune to learn it (0.525 → 0.950). At n=40 a 0.05 delta is two questions, so the convergence is consistent but not statistically proven — and because it rests on the trap-detection metric, it is now best read as suggestive rather than

Overview: A toolkit designed to explore and build with NVIDIA NIMs, including LLMs, 3D generation, and speech recognition. Key features: - Local exploration of NVIDIA NIMs - Support for multiple NIM services including LLMs, image generation, 3D generation, and speech recognition Tech stack: FastAPI, Celery, Nuxt.js, Vue 3, Redis, Prometheus, Grafana, Flower Why it matters: Simplifies the process of experimenting with NVIDIA NIMs and enhances the development experience with enhanced functionality and monitoring tools. Repository: briancaffey/nvidia-nim-kit GitHub stars: 6 GitHub description: A toolkit to explore and build with NVIDIA NIMs README excerpt: 

### Overview DINOv2-LoRA Segmentation is a remote sensing semantic segmentation framework that leverages pretrained DINOv2 visual representations and parameter-efficient fine-tuning through Low-Rank Adaptation (LoRA). ### Key Features - **DINOv2 Foundation Model**: Pretrained visual representations for efficient transfer learning. - **CNN-Based Decoder**: Integrates CNN-based architectures for effective semantic segmentation. - **Decoder Architecture Benchmarking**: Evaluates multiple decoder architectures for optimal performance. - **Linear Probing**: Trains the decoder only, reducing the number of parameters to be fine-tuned. - **Full Fine-Tuning**: Fine-tunes the entire network, updating all parameters for better performance. - **LoRA-Based Adaptation**: Low-Rank Adaptation (LoRA) for efficient parameter-efficient transfer learning. - **Parameter-Efficient Transfer Learning**: Reduces the number of trainable parameters while preserving knowledge from the pretrained model. - **Remote Sensing Segmentation**: Land-cover semantic segmentation on remote sensing imagery. ### Why It Matters This project explores and benchmarks various decoder architectures and adaptation strategies, including linear probing, full fine-tuning, and LoRA-based transfer learning, to improve remote sensing semantic segmentation on the LOVEDA dataset.

### Overview: Plumb is a cutting-edge project that fine-tunes a multimodal Qwen3.8-27B model to excel at AIA G702/G703 pay-application review. The project focuses on extracting and verifying structured data from invoices, lien waivers, and change orders, ensuring every math error and discrepancy is corrected. This approach aims to improve accuracy and speed, making it a valuable tool for contractors and project managers. ### Key Features: - Fine-tuning a multimodal Qwen3.8-27B model on Modal/GCP free credits. - Extraction of SOV lines and G702 summary totals. - Cross-checking with contract SOV, prior approved apps, and change orders. - Emitting PASS or line-level exceptions with corrected figures. - Verifying arithmetic and cross-referencing with entitlements and waivers. - Emitting dense, deterministic RLVR rewards with every check recomputable. ### Tech Stack: Python ### Why It Matters: - The exact recipe that beat a frontier model at 1.7B in ReconForge, now 16× bigger. - Pure document-to-structured-data + deterministic reconciliation. - Real, dollar-denominated pain: 15% of pay apps are rejected for math errors. - Competitive gap: Siteline/Rabbet/Procore digitize but don't adjudicate against contract entitlements. - Perfect-level target: Every check is recomputable arithmetic. - The verifier gives dense, deterministic RLVR rewards. ### Directory Layout:

### Overview: A minimal language model pipeline designed to run on a laptop, from raw text to a tokenizer, pretraining, inference, fine-tuning, and reinforcement learning techniques. ### Key features: - Tokenizer - Model - Pretraining - Inference - Fine-tuning - Reinforcement Learning for Human Preferences (RLHF) and Verifiable Rewards (RLVR) ### Tech stack: JavaScript ### Why it matters: The pipeline is designed to be simple and accessible, making it suitable for developers and researchers alike. It includes all stages necessary for building and fine-tuning a language model, from raw text to a trained model that can be used with other tools. ### Repository: GitHub: changkun/minllm ### GitHub stars: 6 ### GitHub description: A minimal pipeline for making a language model, small enough to run on a laptop and written to be read. It goes from raw text to a tokenizer, a pretrained model, an inference engine, a fine-tuned chat model, and a model post-trained with reinforcement learning, first from human preferences (RLHF) and then from verifiable rewards (RLVR), and ends with that model in the format that other tools run. ### Stages: - **Tokenizer**: Byte-level BPE: training, encoding, decoding, special tokens - **Model**: Decoder-only transformer with RMSNorm, RoPE, SwiGLU and grouped-query attention - **Pretraining**: Data pipeline, AdamW, learning rate schedule, checkpoints: a model that writes stories

Overview: Vietnamese math reasoning distillation is a project that distills step-by-step math reasoning from Phi-3 (teacher) into TinyLlama-1.1B (student) using QLoRA and judge-filtered reasoning traces. Key features: - Vietnamese math reasoning distillation - Distilling from Phi-3 to TinyLlama-1.1B - Using QLoRA and judge-filtered reasoning traces Tech stack: Python, PyTorch, QLoRA, llama.cpp Why it matters: This project is crucial for improving math reasoning capabilities in language models, making them more effective in educational and research contexts.

Overview: This repository implements a two-stage training pipeline for chart question answering using Qwen3-VL, fine-tuned on the ChartQA dataset. It includes supervised fine-tuning and reinforcement learning for improved format compliance and accuracy. Key features: - Supervised fine-tuning on ChartQA dataset - Reinforcement learning (GRPO) for format compliance and accuracy improvement - Base model: Qwen/Qwen3-VL-2B-Instruct - SFT model: Nhaass/Qwen3-VL-2B-ChartQA - GRPO model: Nhaass/Qwen3-VL-2B-ChartQA-GRPO Tech stack: Python Why it matters: This project showcases the potential of Qwen3-VL for chart QA, combining supervised learning with reinforcement learning to enhance the model's performance and format compliance. Repository: cudnah124/ChartQA Primary language: Python GitHub stars: 6 GitHub description: Vision-language AI for chart question answering using Qwen3-VL with SFT and GRPO training

Overview: This project is a real-time voice AI assistant for the SAM 2 No Code Finetuning web application, providing users with natural conversation to learn about LoRA fine-tuning, manufacturing datasets, and the training configuration UI. Key features: - Real-time voice interaction through LiveKit agents - Access to a RAG knowledge base via MongoDB Atlas and Voyage AI - Integration with PostgreSQL for user authentication - Generation of conversation summaries for follow-up emails Tech stack: Python, LiveKit, Deepgram, OpenAI, Cartesia, Lemonslice, MongoDB Atlas, Voyage AI Why it matters: This project simplifies complex technical information into a user-friendly, conversational format, enhancing the user experience and making it easier for developers to understand the intricacies of LoRA fine-tuning and SAM 2 architecture.

### Overview: Mayaku is a real-time object detection, segmentation, and keypoint library designed for fine-tuning and exporting. It provides high accuracy and efficiency, making it suitable for various applications. ### Key features: - **Highest Fine-Tune AP at Nano Size**: Across all 100 datasets of RF100-VL, Mayaku-n averages 58.9 AP, surpassing RF-DETR nano (57.4) and YOLO26n (54.8). - **True Int8 Quantization**: The deploy graph uses only convolutions, ReLU, add, max-pool, and nearest resize, ensuring end-to-end quantization with quantization-aware training enabled by default. - **Runs and Deploys Everywhere**: No compiled extensions, deformable convolutions, or attention. Parity-tested exports to ONNX, Core ML, OpenVINO, and TensorRT. - **Detection and Instance Segmentation**: One API and one training loop for both detection and instance segmentation. ### Tech Stack: Mayaku is built with Pure PyTorch and is compatible with CUDA, Apple Silicon, and CPU. ### Why it Matters: Mayaku is designed to be the most accurate nano detector for fine-tuning on your own data, offering high accuracy and efficiency. It simplifies the process of fine-tuning models on your own datasets, making it ideal for real-world applications where speed and accuracy are crucial.

Overview: This repository provides a straightforward way to fine-tune OpenAI's `gpt-3.5-turbo` model using Python. It also requires the use of Google Sheets to prepare the training data. Key features: - Fine-tuning of `gpt-3.5-turbo` model - Utilizing Python for the process - Need for Google Sheets to prepare training data Tech stack: Python, Google Sheets Why it matters: This method simplifies the fine-tuning process, making it accessible to developers without extensive knowledge in machine learning or natural language processing.

### Overview: This project is a real-time disaster tweet detection system using TensorFlow, Keras, and Hugging Face Transformers. It fine-tunes a BERT model to classify tweets accurately and efficiently. ### Key Features: - **Fine-tuned BERT Model**: High-precision classification for disaster-related tweets. - **Real-Time Analysis**: Processes tweets quickly and accurately. - **Actionable Insights**: Provides timely information for emergency response. - **Scalable Solution**: Easily adaptable to different datasets and tasks. ### Tech Stack: - **TensorFlow**: Core framework for implementing and fine-tuning the BERT model. - **Keras**: Framework for building and training models. - **Hugging Face Transformers**: Pre-trained BERT models and utilities for NLP tasks. - **scikit-learn**: Traditional ML tasks for classification and evaluation. - **Matplotlib**: Visualization tools for model performance. - **Pandas**: Data manipulation and preprocessing for tweet analysis. ### Why It Matters: This system is crucial for decision-makers in emergency management, enabling them to respond quickly to disasters by providing accurate and actionable insights from real-time tweets.