Projects

### 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: vchord is a PostgreSQL extension designed for scalable, fast, and disk-friendly vector similarity search, serving as a successor to `pgvecto.rs`. It combines RaBitQ quantization with inverted-file and single-layer graph indexes for efficient storage and retrieval of up to 400,000 vectors per dollar, offering 26x more vectors than in-memory `pgvector`/`pgvecto.rs` HNSW indexes. Key features: - RaBitQ quantization for compact storage of high-dimensional vectors. - Inverted-file and single-layer graph indexes for fast query performance. - Scalable storage of 100M × 768-dim vectors on AWS instances. - Fast index construction using hierarchical K-means or external precomputation. - Bounded memory growth through dimensionality reduction and sampling. Tech stack: Rust. Why it matters: vchord provides a cost-effective solution for vector similarity search, enabling efficient storage and retrieval of large datasets while maintaining high performance.

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: Seedance 1.0 Pro Quality API offers a high-fidelity video route on APIMart, providing text-to-video and image-to-video capabilities in various resolutions, billed per generated second. The API supports a wide range of models and parameters, including a pricing model that updates in real-time. Key features: - Supports text-to-video and image-to-video in 480p, 720p, and 1080p resolutions. - Billed per generated second, with a minimum charge of $1 for each call. - Asynchronous by design, with `task_id` returned for tracking progress. - Pricing is updated in real-time on the live pricing page. Tech stack: Python Why it matters: Seedance 1.0 Pro Quality API simplifies the process of generating high-quality videos using OpenAI-compatible endpoints, making it ideal for developers looking to integrate video generation into their applications without the complexity of traditional APIs. GitHub stars: 9

### 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: Blaze-DB is a Rust-native, standalone, and self-managed vector database optimized for efficient hard disk storage and fast retrieval of embeddings. It leverages the HNSW indexing technique for approximate nearest neighbor search and supports multiple distance metrics including Cosine, Euclidean, and Dot Product. The database is designed to be fast and reliable, with features such as memory-mapped files, parallel processing, and efficient indexing and caching mechanisms. ### Key Features: - **Server and Client Wrappers:** `blaze-server` and `blaze-client` binaries for server and client operations. - **Batch/Chunks Processing:** Embedding generation in the CLI wrapper. - **Disk Storage:** Stores and indexes embeddings in binary and JSON formats. - **Index Caching:** Uses LRU cache for improved I/O performance. - **HNSW Indexing:** Implements Hierarchical Navigable Small World (HNSW) graph for fast query processing. - **Semantic Similarity Search:** Supports multiple distance metrics for accurate search. - **Performance Benchmarks:** Benchmarks show <1ms per search on War and Peace dataset and <5ms on Amazon Product Dataset. - **Backup and Restore:** Supports backup and restore functionality with some caveats. - **Lazy Deletion:** Background job for low disk space usage. - **Global Locks and Metadata:** No more global locks for metadata storage, improving concurrency and performance. - **Configurable Settings:** Environment variables and .env file support for server settings. - **Docker Support:** Docker image available for easy hosting and management.

### 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 features curated open-source Large Language Model (LLM) projects that are production-ready and suitable for solving real-world problems. It focuses on high-performance, scalable solutions across various industries and applications. Key features: - Production-ready LLM projects for training, fine-tuning, and post-training. - Support for multiple configurations and architectures. - Optimization libraries for distributed training and inference. - Frameworks for fine-tuning, deployment, and chatbots. - Unified efficient fine-tuning of 100+ LLMs. - GPU optimized techniques for training transformer models at-scale. - Framework for fine-tuning (CPT/SFT/DPO/GRPO) 600+ LLMs and 300+ multimodal models. Tech stack: Python Why it matters: This repository provides essential tools and frameworks for deploying large language models in production environments, bridging the gap between research prototypes and production-grade projects.

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.