Projects

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.

### Overview: DeepSeek-V4-Flash-0731 is a high-performance language model optimized for 1,048,576-token context on two NVIDIA DGX Spark units with TP=2, achieving peak decode speeds of up to 653.2 tok/s (c=16) and 398.2 tok/s (c=16) for list and JSON, and 291.1 tok/s (c=32) for code and prose. The model features a 4-bit MLA KV cache and is designed for speculative decoding and deterministic KV-pin sizing, with a 3.23x concurrency at 1M tokens. ### Key features: - **Max Token Context**: 1,048,576 tokens - **GPU KV Cache Pool**: 3,386,611 tokens (3.23x @ 1M) - **Peak Decode Speed**: 653.2 tok/s (c=16) for list, 398.2 tok/s (c=16) for JSON, 291.1 tok/s (c=32) for code and prose - **Mixed-Kind Aggregate**: ~310 tok/s at c=12 - **Speculative Decoding Acceptance**: 0.63 / 4.16 tokens per step - **Long-Context Retrieval**: 994,397-token prompt verified on this config

### Overview: This project is an AI-powered book recommendation engine that uses semantic understanding and emotion intelligence to deliver highly relevant and personalized book suggestions. Leveraging LangChain, OpenAI embeddings, and emotion-aware NLP across 7,000 titles, it offers an interactive Gradio experience for intuitive exploration. ### Key Features: - Semantic vector search for content-based similarity. - Emotion and sentiment analysis from book descriptions. - Interactive Gradio dashboard for user-friendly book recommendations. - Integration of Python, Pandas, LangChain, OpenAI embeddings, transformers, Chroma vector database, and Gradio. ### Tech Stack: Python, Pandas, LangChain, OpenAI embeddings, transformers, Chroma vector database, Gradio. ### Why it Matters: By combining embedding-based retrieval, zero-shot text classification, sentiment and emotion analysis, and an interactive Gradio dashboard, this project demonstrates a complete end-to-end AI pipeline for an intelligent, emotion-aware semantic recommendation system.

Overview: Kurtis is a fine-tuning, inference, and evaluation tool designed for Small Language Models, specifically Huggingface's SmolLM2. It focuses on creating a space for genuine interaction and understanding, rather than complex tasks. Key features: - Fine-tuning for specific language models - Inference capabilities - Evaluation tools for various models Tech stack: Python Why it matters: Kurtis aims to provide a supportive and empathetic conversational AI, focusing on understanding and meaningful dialogue, rather than complex tasks or generating specific outputs like images or code.

### Overview: MiniChat is a minimalistic LLM chat UI inspired by Fooocus and Pi. It focuses on content and simplicity, using bold typography and a minimal design. ### Key features: - **Minimalistic Design**: Focuses on content with minimal design elements. - **OpenAI Compatible**: Supports OpenAI-compatible APIs, including Ollama, Portkey, and HuggingFace Text Generation Inference. - **Docker Support**: Runs with Docker, providing a lean image size of less than 2MB. ### Tech stack: TypeScript ### Why it matters: By keeping configuration minimal and focusing on content, MiniChat aims to provide a focused and efficient chat interface.

Overview: lollm is a small, readable inference engine designed specifically for studying how LLMs and related models run inference. It includes a thin loader, router, and generate loop, along with self-contained family packages optimized for clarity and verification against `transformers`. Key features: - Thin loader and router - Shared generate loop - Self-contained family packages (qwen2, qwen3, gemma2, gemma3, gemma4, qwen3_5) - GGUF and safetensors support for all families - Tokenizer optimized for clarity and verification against `AutoTokenizer` Tech stack: Python Why it matters: lollm provides a clear and verified way to study LLM inference, ensuring that the implementation is as close as possible to the original models while maintaining readability and verification against established benchmarks. Repository: giapnguyen74/lollm GitHub stars: 6 GitHub description: an AI inference for learning README excerpt: <img src="docs/lollm-logo.svg" alt="lollm — learn only LLM" height="72">

### Overview: This project explores the development and evaluation of Convolutional Neural Networks (CNNs) for skin lesion classification using the HAM10000 dataset. The dataset includes 7 skin cancer classes, including melanoma, which is the primary focus. Techniques such as data augmentation, generative adversarial networks, and transfer learning are utilized to improve model performance. ### Key features: - Data augmentation with image transformations and Generative Adversarial Networks. - Segmentation and transfer learning techniques. - Metrics extracted for accuracy, loss, sensitivity (Recall), specificity, F1-score, AUC, and precision. - Utilization of ConvNeXt, ResNetV2, ViT_B/32, and Xception architectures. ### Tech stack: Jupyter Notebook ### Why it matters: This project provides a comprehensive analysis of various CNN models and techniques for skin lesion classification, demonstrating the impact of configurations and transfer learning on model performance. The use of HAM10000 dataset and detailed metrics extraction ensures a thorough evaluation of the models' effectiveness.

### Overview: Fine-tuning ResNet50 for Food Classification means adapting a pre-trained ResNet50 model to recognize specific food categories by adjusting its final layers and training it on a specialized dataset of food images for improved accuracy. ### Key Features: - Fine-tuning ResNet50 model - Training on a specialized dataset of food images - Adjusting final layers for better accuracy - Predicting food classes and estimating calorie content ### Tech Stack: Jupyter Notebook ### Why It Matters: This project enables users to track their dietary intake and make informed food choices by accurately recognizing food items from images and estimating their calorie content.

Overview: This project demonstrates how to enhance Retrieval-Augmented Generation (RAG) systems by implementing Anthropic's Contextual Retrieval approach. It shows how to add crucial context before embedding documents into a vector database, improving the accuracy and relevance of retrieved information. Key features: - Enhancing RAG systems with Contextual Retrieval - Using Exxa batch inference API for efficient cost-effectiveness Tech stack: Python Why it matters: This method addresses the issue of losing context in traditional RAG systems by adding chunk-specific explanatory context before embedding, making the information more useful for retrieval.

Overview: This monorepo is designed for the EA platform, focusing on a developer-focused workflow engine built on Kubernetes. Key features: - Node-based AI agent/workflow builder for integrating with ChatGPT, Grok, Claude, Gemini, and Ollama, along with third-party APIs via a low-code/no-code JSON interface. - Kubernetes-native job engine for distributed agent workflows across nodes. - Designed with Kubernetes patterns like custom resources and operators. - Implements best practices for platform scaling and security, including JWT/JWKS auth, NetworkPolicy, Istio, and Kubernetes Gateway API. Tech stack: Go Why it matters: This monorepo streamlines the development and management of AI workflows and agent engines, making it easier for developers to build, test, and scale their applications using Kubernetes. Repository: im-knots/ea-monorepo GitHub stars: 6 GitHub description: A thing do-er workflow engine designed for kubernetes

### Overview: `hf-providers` is a comprehensive tool designed to help developers and researchers compare different API providers, local GPUs, and cloud services for running any model. It provides real-time pricing, throughput, and status updates for models, as well as detailed information about the models themselves, including their parameter counts, weight sizes, and licensing details. ### Key Features: - **Model Comparison**: Shows the live status, pricing, throughput, and tool support for any model provider. - **Fastest and Cheapest Providers**: Routes API calls through the cheapest or fastest available provider. - **Local GPU Estimates**: Estimates the performance of a model on various GPUs. - **Model Metadata**: Provides detailed metadata about the model, including parameter counts and weight sizes. - **Variant Detection**: Automatically detects related models from the same family. - **Interactive Tree Browser**: Allows users to expand models into providers and providers into language options. ### Tech Stack: JavaScript ### Why It Matters: `hf-providers` is essential for developers who need to quickly and efficiently compare different options for running models, whether it’s through local hardware, cloud services, or GPU providers. It streamlines the process of finding the best solution for their needs, saving time and reducing the risk of errors. ### Repository: - **Repository**: `https://github.com/jadnohra/hf-providers` - **GitHub Stars**: 6 - **GitHub Description**: Compare API providers, local GPUs, and cloud for any model - **Primary Language**: JavaScript ### Installation: - **Brew Install**: `brew install

### Overview: Groq QA Generator is a Python library and CLI tool designed to automate the creation of question-answer pairs from text, specifically for fine-tuning large language models (LLMs). It leverages Groq and Hugging Face datasets to generate high-quality QA pairs, offering customizable train/test split ratios and prompt templates for flexible question generation. ### Key Features: - **CLI Tool**: Use the `groq-qa` command for quick and easy QA pair generation. - **Python Library**: Import directly into your project and extend your code. - **Advanced Models**: Supports large models like LLaMA 3.1 (70B parameters). - **Automated QA Generation**: Generate question-answer pairs from input text. - **Hugging Face Datasets**: Convert QA pairs to Hugging Face datasets and export/upload datasets to Hugging Face. - **Custom Split Ratios**: Define custom train/test split ratios when generating QA pairs. - **Prompt Templates**: Flexible question generation through customizable prompt templates. ### Tech Stack: - Python ### Why It Matters: Groq QA Generator streamlines the process of generating high-quality QA pairs, making it an essential tool for researchers, educators, and domain-specific applications. Its customizable features and integration with Groq and Hugging Face datasets ensure efficient and effective fine-tuning of large language models.

Overview: This project fine-tunes the wav2vec2 model for pathological speech processing, addressing the need for accurate and sensitive analysis of speech in medical contexts. Key features: - Fine-tuning of wav2vec2 model - Integration with pathological speech datasets - Enhanced accuracy and sensitivity for speech analysis Tech stack: Jupyter Notebook Why it matters: The project addresses a critical need in medical speech processing, providing a more accurate and sensitive analysis of speech in pathological conditions, which can significantly improve diagnostic accuracy and patient care.

Overview: Atlas RAG is a document retrieval and augmented generation application designed for PDF-based Q&A workflows, research assistance, and knowledge retrieval. Key features: - Upload and ingest PDF documents - Text chunking and embedding generation - Vector store creation with ChromaDB or FAISS - Semantic retrieval for relevant document chunks - Question answering on extracted knowledge - Streamlit-based web interface - Groq model integration for fast LLM responses Tech stack: Python, LangChain, Streamlit, ChromaDB, FAISS, PyMuPDF / PyPDF, Sentence Transformers, Groq Why it matters: This project streamlines the process of extracting and answering questions from PDF-based documents, making it ideal for researchers, support assistants, and anyone needing quick access to information stored in PDFs. Repository: kabirnagdev/atlas Primary language: Jupyter Notebook GitHub stars: 6 GitHub description: RAG Pipeline with Chroma DB & Chatgroq

Overview: This project compiles a stable LangGraph/YAML agent into a small model that self-orchestrates with no runtime orchestrator, offering a 100× cheaper inference cost compared to traditional methods. Key features: - Compiles LangGraph/YAML agent procedures into small open models - Removes runtime orchestrator, enabling private, offline inference - Reports near-frontier quality at 128-462× lower inference cost - Supports LangGraph and YAML procedures Tech stack: Python Why it matters: This project provides a solution for developers looking to reduce inference costs and streamline their workflows, offering a 100× cheaper inference cost compared to traditional methods. Repository: kamaalg/agent2model GitHub stars: 6 GitHub description: Compile a stable LangGraph/YAML agent into a small model that self-orchestrates with no runtime orchestrator — private + offline, ~100× cheaper inference (per the paper; reproduction in progress).