Search Results for "high" - Page 63
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Showing 5835 open source projects for "high"
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Tile38
Ultra Fast Geospatial Database & Geofencing Server
When you need the best performance for your location-based applications, you can rely on Tile38. Tile38 is an ultra-fast, open source geospatial database and geofencing server capable of real-time geofencing, fast spatial indexing and more. It supports a variety of object types including lat/lon, Geohash, bbox, GeoJSON, QuadKey, and XYZ tile; and is capable of operations like Nearby, Within, and Intersects. There’s also built-in support for many popular tools. Tile38 is made up of 3 main... -
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Angstrom
Parser combinators built for speed and memory efficiency
Angstrom is a parser-combinator library in OCaml designed for high-performance applications. It provides monadic and applicative interfaces for composing parsers and supports incremental input processing. -
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Orpheus TTS
Towards Human-Sounding Speech
...The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research preview, and includes data-processing scripts so users can train or finetune their own variants. Inference is provided through a Python package that uses vLLM under the hood for high-throughput, low-latency generation, including streaming examples that show how to generate audio chunks in real time. The maintainers provide Colab notebooks, a standardized prompting format, and one-click deployment via Baseten for production-grade, FP8/FP16 optimized inference with ~200 ms streaming latency. -
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VibeVoice ComfyUI
ComfyUI integration for Microsoft's VibeVoice text-to-speech model
...It exposes VibeVoice as a set of custom nodes so you can build single-speaker and multi-speaker voice generation pipelines visually, combining TTS with other audio or generative components. The integration supports high-quality single-speaker synthesis as well as scripted multi-speaker conversations, with optional voice cloning from audio samples for each speaker. It includes advanced control over generation parameters like attention backend, diffusion steps, sampling temperature, guidance scale, and quantization settings, allowing users to tune the trade-offs between quality, VRAM usage, and speed. ... -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides native Java development experience and functions like any other regular Java library. You don't have to be a machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. -
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Bowtie 2
A fast and sensitive gapped read aligner
...It is widely used in bioinformatics pipelines for RNA-seq, DNA-seq, metagenomics, variant analysis, and other sequencing-based research tasks. Overall, Bowtie 2 remains a foundational command-line tool for high-throughput sequence alignment and reproducible computational biology workflows. -
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Kyoo
A portable and vast media library solution
...The system allows users to access their content from a browser while maintaining control over storage and playback infrastructure. Kyoo supports hardware acceleration options for efficient video processing, making it suitable for high-resolution streaming environments. Its configuration relies on environment variables and Docker Compose, enabling flexible deployment across different systems. The platform emphasizes modularity, allowing users to customize storage locations, streaming settings, and performance parameters. With a focus on self-hosting and scalability, Kyoo provides an alternative to commercial media servers for users who want full control over their content. -
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MiniCPM4.1
Achieving 3+ generation speedup on reasoning tasks
...It builds upon the same efficiency-focused philosophy but further optimizes decoding performance, achieving substantial speed gains in reasoning-intensive tasks while maintaining high-quality outputs. One of its key innovations is the hybrid reasoning mode, which allows developers to control whether the model engages in deeper reasoning processes or faster responses depending on the use case. The model also supports both dense and sparse attention mechanisms, enabling more efficient computation depending on the selected inference framework. ... -
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Phlex
Object-oriented views in Ruby
Phlex is a Ruby-based framework for building HTML and SVG views using object-oriented programming principles, offering a unique alternative to traditional template systems like ERB. It allows developers to write UI components entirely in Ruby, providing full control over structure, logic, and rendering without mixing HTML and templating syntax. One of its key advantages is performance, as it can render HTML extremely quickly while maintaining predictable scaling even with complex component... -
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Bootstrap Blazor Component
Bootstrap Blazor is an enterprise-level UI component library
Bootstrap Blazor Component is an enterprise-grade UI component library built on top of the Bootstrap framework and Microsoft’s Blazor platform, designed to accelerate the development of modern web applications using C#. It provides a comprehensive set of pre-built, high-quality UI components that follow responsive design principles, enabling developers to create consistent and visually appealing interfaces without starting from scratch. The library supports both Blazor Server and WebAssembly hosting models, allowing flexibility in how applications are deployed and executed. BootstrapBlazor integrates deeply with the .NET ecosystem, leveraging C# and Razor syntax to create interactive web experiences without requiring JavaScript-heavy workflows. ... -
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Colab-MCP
An MCP server for interacting with Google Colab
...Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. By exposing Colab as an MCP server, the tool enables seamless integration with a wide range of AI assistants and agent frameworks, creating a standardized interface for tool use and execution. -
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Browserbase MCP Server
Allow LLMs to control a browser with Browserbase and Stagehand
...The project provides a standardized interface for connecting AI systems to real-world web environments, allowing them to navigate pages, extract structured data, and perform user-like actions such as clicking, typing, and form submission. It leverages Browserbase infrastructure along with Stagehand to deliver high-performance browser automation with improved speed and efficiency through caching and optimized execution pipelines. The system supports multiple AI models and integrates seamlessly into agent workflows, making it suitable for applications such as web scraping, testing, and intelligent automation. It also includes advanced capabilities such as screenshot capture, DOM analysis, and session persistence, enabling complex interactions across multiple browsing sessions. -
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HelixDB
Graph-vector database for building unified AI backends fast
...It combines graph and vector data models, allowing developers to manage relationships and embeddings within the same system without relying on separate services. HelixDB is built from scratch in Rust and uses LMDB as its storage engine, enabling high performance and low-latency query execution. HelixDB also supports additional data formats such as key-value, document, and relational data, making it flexible for a wide range of backend architectures. A central feature of the project is its custom query language, HelixQL, which is fully type-safe and compiled to ensure reliability and correctness in production environments. ... -
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Databend
Cloud-native open source data warehouse for analytics and AI queries
Databend is an open source cloud-native data warehouse designed for large-scale analytics and modern data workloads. Built in Rust, the system focuses on high performance, scalability, and efficient data processing for analytical queries. It is designed with a separation of compute and storage, allowing compute nodes to scale independently while storing data in object storage systems. This architecture enables cost-efficient storage and elastic scaling for workloads that involve large datasets and complex queries. ... -
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videodl
Lightweight Python tool for downloading videos from many platforms
Videodl is a lightweight video downloader implemented entirely in Python that allows users to retrieve videos from a wide range of online media platforms. It focuses on providing a fast and simple way to parse video pages and download media files, often prioritizing high-definition versions without watermarks when available. It supports numerous video platforms across both Chinese and international streaming ecosystems, enabling users to fetch content from many popular services through a unified interface. Videodl works by implementing platform-specific client modules that extract video information and download links from supported services. ... -
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Kaggle Solutions
Collection of Kaggle Solutions and Ideas
...The repository acts as a knowledge base for competitive machine learning by collecting solution write-ups, discussion threads, code notebooks, and tutorial resources shared by top Kaggle participants. Each competition entry typically includes information about the dataset, evaluation metrics, modeling strategies, and techniques used by high-ranking competitors. The repository also highlights important machine learning concepts such as feature engineering, cross-validation strategies, ensemble modeling, and post-processing methods commonly used in winning solutions. Because the content is organized by competition categories such as computer vision, natural language processing, tabular data, and time-series forecasting, users can explore techniques relevant to specific problem types. -
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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. This approach allows learners to study the mathematical and algorithmic details behind widely used models in a transparent and readable way. The repository includes implementations of both supervised and unsupervised learning techniques, along with dimensionality reduction and clustering methods. Many of the algorithms are written in a simplified style that prioritizes clarity and educational value over production-level optimization. ... -
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mllm
Fast Multimodal LLM on Mobile Devices
mllm is an open-source inference engine designed to run multimodal large language models efficiently on mobile devices and edge computing environments. The framework focuses on delivering high-performance AI inference in resource-constrained systems such as smartphones, embedded hardware, and lightweight computing platforms. Implemented primarily in C and C++, it is designed to operate with minimal external dependencies while taking advantage of hardware-specific acceleration technologies such as ARM NEON and x86 AVX2 instructions. ... -
19
Korvus
Korvus is a search SDK that unifies the entire RAG pipeline
Korvus is an open-source retrieval-augmented generation (RAG) pipeline designed to run entirely inside PostgreSQL, allowing developers to build AI search and knowledge systems directly within a database environment. The project consolidates the typical steps of a RAG pipeline—including embedding generation, document retrieval, reranking, and text generation—into a single query executed within the Postgres ecosystem. By leveraging PostgresML and vector extensions such as pgvector, Korvus... -
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mistral.rs
Fast, flexible LLM inference
mistral.rs is a fast and flexible LLM inference engine implemented in Rust, designed to run and serve modern language models with an emphasis on performance and practical deployment. It provides multiple entry points for developers, including a CLI for running models locally and an HTTP server that exposes an OpenAI-compatible API surface for easy integration with existing clients. The project includes hardware-aware tooling that can benchmark a system and choose sensible quantization and... -
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DeepSearcher
Open Source Deep Research Alternative to Reason and Search
DeepSearcher is an open-source “deep research” style system that combines retrieval with evaluation and reasoning to answer complex questions using private or enterprise data. It is designed around the idea that high-quality answers require more than top-k retrieval, so it orchestrates multi-step search, evidence collection, and synthesis into a comprehensive response. The project integrates with vector databases (including Milvus and related options) so organizations can index internal documents and query them with semantic retrieval. It also supports flexible embeddings, making it easier to choose different embedding models depending on domain requirements, latency targets, or accuracy goals. ... -
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
...Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources, including consumer-grade GPUs. The project includes specialized optimizers and quantized matrix operations that significantly reduce the memory footprint of training and inference workloads. ... -
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Nano-vLLM
A lightweight vLLM implementation built from scratch
...Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. The engine is intended primarily for educational use, experimentation, and lightweight deployments where a full production-grade inference stack may be unnecessary. Its API closely mirrors that of the original vLLM framework, allowing developers familiar with vLLM to adopt the tool with minimal changes. -
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Relaticle
The Next-Generation Open-Source CRM Platform written with Laravel
...The interface lets you write plain text notes and tag or connect them dynamically, making it easier to uncover patterns and connections over time instead of losing insights in a long, unstructured list. Because it’s built with productivity and exploration in mind, Relaticle offers fast search, semantic context awareness, and the ability to zoom from high-level overviews down to specific node details. It also supports self-hosting so users retain full control over their data without relying on third-party servers or cloud subscriptions. -
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zpdf
Zero-copy PDF text extraction library written in Zig
zpdf is a high-performance PDF text extraction library written in Zig that focuses on speed, low overhead, and modern parsing techniques. It leans heavily on memory-mapped file reading and zero-copy patterns where possible, so it can scan large PDFs without repeatedly copying data around in memory. The library supports streaming extraction using efficient arena allocation, making it well suited for workloads that need to process big documents quickly or in batches.
