Projects

Overview: This project is a Streamlit application designed to process multimodal documents and query a Milvus database. It utilizes LangChain and EasyOCR for text extraction from various file types, and HuggingFaceEmbeddings for vector representation generation. The system supports multiple file types and offers a natural language query interface for AI-driven responses. Key features: - Upload file processing: Extracts text from audio, video, images, documents, and more. - Milvus integration: Stores and queries document embeddings. - Query interface: Implements a Retrieval-Augmented Generation (RAG) pipeline. Tech stack: Python, Streamlit, LangChain, EasyOCR, HuggingFaceEmbeddings, Milvus. Why it matters: This project provides a comprehensive solution for processing and querying multimodal documents, leveraging cutting-edge AI and database technologies to enhance the efficiency and accuracy of information retrieval and processing. Repository: pacificrm/RAG-Architecture GitHub stars: 11 GitHub description: Multimodal Document Processing RAG with LangChain GitHub URL: https://github.com/pacificrm/RAG-Architecture

### Overview: Seedance 1.0 Pro API (`seedance-1.0-pro`) is a fast AI video model designed for high-quality video generation and rapid previews. It offers a single API key that covers 300+ models including images, videos, and multiple languages, without the need for monthly subscriptions or additional fees. ### Key features: - **Single Key for All Catalogs**: A single API key that covers all models in the Seedance catalog, including 300+ image and video models, and multiple languages. - **Pricing by Second**: The API is billed based on the number of generated seconds, with a tiered pricing model. - **No Monthly Subscription**: No need for a monthly subscription; pay only for usage. - **Real-Time Billing**: Each task reports the exact cost, and tasks with failed attempts are refunded. - **Asynchronous Processing**: Tasks start processing immediately, and you can track their status using `GET /v1/tasks/{task_id}`. ### Tech Stack: - Python ### Why it Matters: - **Unified Key for All Models**: A single key covers all models, eliminating the need to manage multiple keys. - **Flexible Pricing**: No need for monthly subscriptions, and pay only for usage. - **Real-Time Billing**: Tasks report costs accurately, and failed attempts are refunded. - **Asynchronous Processing**: Tasks start processing immediately, making it easy to track their status. ### Repository: - **Repository**: pcrgss12wsx7w9/doubao-seedance-1-0

Overview: This repository, `ask-pesu`, is a project designed to answer questions about PES University discussions using a retrieval-augmented question answering system. It consists of two services: `AskPESU` for the API backend and `AskPESU DB` for the database backend. Key features: - The `AskPESU` service provides a FastAPI + LangChain RAG backend for answering questions. - The `AskPESU DB` service streams new r/PESU comment threads into Qdrant and holds scripts for backfilling history. Tech stack: TypeScript Why it matters: This project is crucial for providing quick and accurate answers to questions about PES University, enhancing the user experience by leveraging the power of language models and Qdrant for efficient information retrieval. GitHub stars: 11 Repository: `https://github.com/pesu-dev/ask-pesu` Primary language: TypeScript GitHub description: A RAG pipeline for question answering about PES University, answering from r/PESU discussions.

Overview: A simple mobile interface for generating images from text prompts using OpenAI's API. Key features: - Generates images from text prompts - Uses OpenAI's API for image generation - Quick setup and minimal UI - Instant outputs Tech stack: Java Why it matters: The app provides a user-friendly interface for generating images from text, making it accessible and efficient for developers and designers looking to quickly create visual content.

Overview: This project integrates Pinecone with Neo4j to analyze and explore Supreme Court cases, providing a comprehensive tool for Supreme Court case analysis. Key features: - Pinecone integration for case analysis - Neo4j account setup for case processing - MongoDB database for data storage - Node.js application for application setup Tech stack: TypeScript Why it matters: This project offers a powerful way to analyze Supreme Court cases by integrating cutting-edge technologies like Pinecone and Neo4j, providing a robust and efficient solution for Supreme Court case analysis.

Overview: This tutorial guides users through the process of fine-tuning a stable diffusion model using HuggingFace's diffusers library. It includes advice on suitable hardware requirements, data preparation using the BLIP Flowers Dataset, and detailed instructions for fine-tuning the model. Key features: - Fine-tuning a stable diffusion model - Using HuggingFace's diffusers library - Advice on hardware and data preparation - Detailed instructions for fine-tuning the model Tech stack: Jupyter Notebook Why it matters: This tutorial provides a comprehensive guide for fine-tuning a stable diffusion model, making it valuable for developers looking to enhance their generative models.

Overview: OpenProxy is a lightweight LLM proxy server designed to forward API requests to OpenAI and Anthropic compatible endpoints. It includes comprehensive logging, cost tracking, and PostgreSQL integration. Key features: - Seamless API request forwarding - Comprehensive logging - Cost tracking - PostgreSQL integration Tech stack: TypeScript Why it matters: OpenProxy simplifies the process of integrating LLM models like OpenAI and Anthropic, providing robust logging and cost tracking, making it an essential tool for developers looking to integrate these models into their projects efficiently and transparently.

### Overview: GPT Image 1.5 Prompts: A Collection of 8 Recipes for Stunning Image Renders ### Key Features: - **Model ID:** `gpt-image-1.5-official` - **Aspect Ratios:** 1:1, 4:3, 16:9 - **Cost per Image:** $4.00 ### Tech Stack: - Python ### Why It Matters: - **Efficient Pricing:** $4 per image, with no subscription fees. - **Versatile Usage:** Access to OpenAI-compatible endpoints via a single `model` field. - **Clear Instructions:** Simple prompt structure ensures consistent and effective results. ### Repository: - **Repository:** pwmz6qjcw/gpt-image-1-5-prompts-ko - **GitHub Stars:** 9 - **GitHub Description:** GPT Image 1.5 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`gpt-image-1.5-official`) and the cost per image. ### Team Usage: - **Key Features:** - **Catalog Management:** Access to all models and APIs via a single `model` field. - **Cost Efficiency:** No need for pre-paid plans, just $1 to start. - **Clear Billing:** Only charges for completed tasks, refunds for failed ones. - **Asynchronous Design:** Tasks are completed by polling `GET /v1/tasks/{id}`. ### Gallery: - **Recipes:** - **Product / E

Overview: GPT Image 1 API is an OpenAI-compatible endpoint for generating images using the GPT-1 model, offering token-based billing and easy integration. Key features: - Supports token-based billing - Provides OpenAI-compatible endpoint - Offers a wide range of models and routes - Supports multiple image resolutions and sizes Tech stack: Python Why it matters: GPT Image 1 API simplifies the process of integrating image generation capabilities with OpenAI's models, making it accessible and cost-effective for developers. Repository: qra6o0p/gpt-image-1-api-ru Primary language: Python GitHub stars: 9 GitHub description: GPT Image 1 API (`gpt-image-1`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint.

### Overview: This repository contains three Jupyter Notebooks designed to fine-tune BERT models for Amharic text classification. The models include `xlm-roberta-base`, `bert-small-amharic`, and `bert-mini-amharic`, each pretrained on unlabelled Amharic text data. The notebooks classify Amharic news articles into six categories: Local News, Entertainment, Sports, Business, International News, and Politics. ### Key Features: - Fine-tuned BERT models for Amharic text classification. - Pretrained on unlabelled Amharic text data. - Six categories: Local News, Entertainment, Sports, Business, International News, and Politics. - Models: `xlm-roberta-base`, `bert-small-amharic`, `bert-mini-amharic`. ### Tech Stack: Jupyter Notebook ### Why it Matters: This project is essential for researchers and developers working with Amharic language data, providing a practical solution for text classification tasks in Amharic.

Overview: This project is designed to guide developers through the process of building a question answering (QA) service using Ray and Anyscale, with the aim of scaling the application both locally and in the cloud. The project covers the use of libraries such as Ray, HuggingFace, and LangChain, as well as modern deployment tools for production-grade applications. Key features: - Building a local QA service - Scaling the application using Ray and Anyscale - Using modern deployment tools for production Tech stack: Ray, HuggingFace, LangChain, Anyscale Why it matters: This project provides a comprehensive guide for developers interested in leveraging modern tools and libraries to build and deploy LLM-based applications, focusing on the practical aspects of scaling and production deployment.

### Overview: This repository contains recipes and guides for running large language models on pre-Ampere NVIDIA hardware, specifically the Tesla V100 (sm_70) and RTX 2080 Ti (sm_75). The focus is on real-world measurements and compatibility with specific forks of upstream software. ### Key features: - **Compatibility Matrix**: Detailed compatibility matrix for different models and hardware configurations. - **Real Benchmarks**: Measured tokens/s across V100, 2080 Ti, A100, and 4090-48GB. - **Software Forks**: Documentation for specific forks of upstream software to address compatibility issues. - **Attention Backend**: FlashAttention-2, FlashQLA, and Triton for different hardware configurations. - **dtype**: `float16` only for Volta and `float16` only for Turing. - **FlashAttention-2**: Needs `sm_80` for FlashAttention-2. - **FP8 / FP4**: Needs Hopper/Ada for FP8 / FP4. - **Real Tensor Parallelism**: ✅ for fork, or `llama.cpp -sm tensor` for MoE-AWQ. - **Quantization that works**: AWQ (W4A16) via fork for Volta, AWQ dense + AWQ MoE via fork for Turing. ### Tech stack: NVLink on SXM2 carrier boards, driver traps, cooling, used-kit acceptance. ### Why it matters: Upstream v

Overview: The Requesty integration package for the Vercel AI SDK allows access to over 300 large language models through the Requesty chat and completion APIs, providing comprehensive support for AI SDK v5 with stable features. Key features: - Full support for AI SDK v5 - Compatibility with AI SDK v4 for backward compatibility - Easy installation via npm, yarn, or pnpm - Importing the default provider instance `requesty` - Basic example for text generation - Tool calling (function calling) for complex interactions - Object generation for detailed responses - Streaming with tools for real-time feedback Tech stack: TypeScript Why it matters: This package simplifies integration with AI models, making it easier to build applications that leverage advanced natural language processing capabilities, enhancing user experience and decision-making processes.

Overview: CellTypeAI automates cell identification in scRNA-seq datasets using local LLMs, enhancing accuracy and scalability. Key features: - Local LLM hosting - Integration with scanpy-based pipelines - Context-dependent cell type annotation Tech stack: Jupyter Notebook Why it matters: Streamlines and improves cell type identification in scRNA-seq datasets, enhancing analysis efficiency and accuracy. Repository: rhdaw/CellTypeAI GitHub stars: 11 GitHub description: Automated scRNA-seq cell annotation via local LLM

### Overview: Qwen-Image 2.0 API (`qwen-image-2.0`) is a powerful tool for generating high-quality images using text prompts or transforming existing images. It offers a flexible and efficient way to access a wide range of models and features, including the Qwen standard and Pro models. ### Key Features: - **Model ID and Routes**: The API supports multiple models, including the Qwen standard and Pro models, with a flexible routing system. - **Pricing**: The pricing is straightforward, with a per-image cost of $0.02, and no need for subscription plans or pre-paid fees. - **Model Page**: The pricing and model details can be found on the [model page](https://go.apimart.ai/k-abd367). - **Request Parameters**: The API accepts various parameters such as `model`, `prompt`, `size`, `resolution`, `n`, `image_urls`, and `nsfw_check`. - **Quickstart**: A simple command-line example in Bash and a Python example are provided to get started quickly. ### Tech Stack: - **Python**: The API is built using Python, leveraging the OpenAI-compatible endpoint provided by APIMart. ### Why It Matters: - **Cost Efficiency**: The API offers a cost-effective solution for generating images, with a per-image cost of $0.02, making it ideal for both small and large projects. - **Model Flexibility**: Users can access a variety of models through a flexible routing system, including the Qwen standard and Pro models.

Overview: This repository demonstrates how to fine-tune a Vision Transformer model from TensorFlow Hub for image scene classification. Key features: - Fine-tuning of a Vision Transformer model - Available models: ViT-S16, ViT-B8, ViT-B16, ViT-B32, ViT-L16, ViT-R26-S32 (light augmentation), ViT-R26-S32 (medium augmentation), ViT-R50-L32 - Benchmark results from a newly collected dataset Tech stack: TensorFlow Hub Why it matters: This repository provides a practical guide for fine-tuning a Vision Transformer model for image scene classification, making it useful for developers and researchers looking to improve their image classification models.

Overview: This repository contains sample applications demonstrating how to use ScyllaDB for vector search use cases and more complex LLM apps such as retrieval-augmented generation (RAG) and semantic caching. Key features: - Vector search applications - RAG chatbot integration - Semantic caching layer - Mem0 memory integration Tech stack: Python Why it matters: This repository provides practical examples of how to leverage ScyllaDB for efficient vector search and advanced LLM applications, making it valuable for developers looking to enhance their search and AI capabilities.

### Overview: Stratum is a pure-C transformer inference engine designed specifically for Apple Silicon. It leverages wired memory independence, allowing the model to run efficiently on the device without requiring significant RAM. The engine is lightweight, with a binary size of approximately 752 KB, making it ideal for applications that require low memory footprint. ### Key Features: - **Weight Management**: The model stores weights in a file, allowing for efficient anonymous memory usage. - **Hot and Cold Layers**: The engine runs in two layers: a hot layer for immediate computation and a cold layer for prefetching. - **Multi-Stream Decode**: Supports up to 64 decode streams, enhancing throughput. - **Dense and Hybrid Architectures**: Supports both dense and hybrid (MoE) architectures, with experimental MoE support. - **Metal GPU Registration**: A low-confidence draft can be skipped to improve efficiency. ### Tech Stack: - **Language**: C - **Platform**: Apple Silicon - **Dependencies**: `metal` for Metal support, `metal` for Metal support, `metal` for Metal support ### Why It Matters: - **Efficiency**: The engine runs efficiently on Apple Silicon, utilizing wired memory to keep the model lightweight. - **Scalability**: Supports up to 64 decode streams, making it suitable for high-throughput applications. - **Performance**: Measured on an M4 Pro, the engine can achieve impressive throughput, particularly with the 27B model, which runs in just 77 MB of anonymous memory. ### Tech Stack: - **

### Overview: Rectified-CFG++ is a training-free, geometry-aware guidance scheme designed for flow-based text-to-image (T2I) models. It replaces the naive extrapolation of classifier-free guidance (CFG) with a predictor-corrector integrator that stays on the learned data manifold, eliminating structural artifacts while improving prompt alignment and over-generation quality. ### Key Features: - **On-Manifold Sampling**: Predictor-corrector updates keep trajectories on the data manifold. - **Training-Free**: Drop-in replacement for standard CFG. No additional training or fine-tuning required. - **Model-Agnostic**: Works with any transformer-based rectified-flow T2I backbone (e.g., Flux, SD3/3.5, Lumina). - **Stable Across Scales**: Maintains visual fidelity and prompt alignment even at high guidance strengths. - **Efficient**: Achieves state-of-the-art FID and CLIP-Score. ### Tech Stack: Python ### Why It Matters: Rectified-CFG++ is a significant advancement in the field of flow-based T2I models, offering improved prompt alignment, visual quality, and stability across various scales without the need for additional training. It is particularly useful for enhancing the quality of generated images in applications such as art generation and creative design. ### Repository: shreshthsaini/Rectified-CFGpp ### GitHub Stars: 11 ### GitHub Description: Repository for Rectified-CFG++ Project. ### GitHub License: MIT License ### Installation: 1. Clone the repository: ```bash git

Overview: This repository contains a system for generating question-answer pairs for fine-tuning Large Language Models (LLMs) using the provided data. It leverages various modules to extract text, generate questions, and save the generated questions. Key features: - Automatically processes HTML as input files. - Removes duplicate and similar questions. - Simplifies the JSONL output format cleaning process. Tech stack: Python Why it matters: This system is essential for improving the performance and accuracy of LLMs by fine-tuning them with relevant and diverse question-answer pairs derived from existing data.