Search Results for "teams-for-linux" - Page 87

Showing 16040 open source projects for "teams-for-linux"

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  • 1
    Data Science Interviews

    Data Science Interviews

    Data science interview questions and answers

    Data Science Interviews is an open-source repository that collects common data science interview questions along with community-provided answers and explanations. The project serves as a preparation resource for students, job seekers, and professionals who want to review the technical knowledge required for data science roles. The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or...
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  • 2
    GPU Puzzles

    GPU Puzzles

    Solve puzzles. Learn CUDA

    GPU Puzzles is an educational project designed to teach GPU programming concepts through interactive coding exercises and puzzles. Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively...
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  • 3
    Jina-Serve

    Jina-Serve

    Build multimodal AI applications with cloud-native stack

    Jina Serve is an open-source framework designed for building, deploying, and scaling AI services and machine learning pipelines in production environments. The framework allows developers to create microservices that expose machine learning models through APIs that communicate using protocols such as HTTP, gRPC, and WebSockets. It is built with a cloud-native architecture that supports deployment on local machines, containerized environments, or large orchestration platforms such as...
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  • 4
    TTRL

    TTRL

    Test-Time Reinforcement Learning

    TTRL is an open-source framework for test-time reinforcement learning in large language models, with a particular focus on reasoning tasks where ground-truth labels are not available during inference. The project addresses the problem of how to generate useful reward signals from unlabeled test-time data, and its central insight is that common test-time scaling practices such as majority voting can be repurposed into reward estimates for online reinforcement learning. This makes the...
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  • 5
    MiroThinker

    MiroThinker

    MiroThinker is an open source deep research agent

    MiroThinker is an open-source deep research AI agent designed to perform complex reasoning, information gathering, and predictive analysis tasks. The system focuses on enabling long-horizon research workflows by allowing the agent to interact repeatedly with external tools, search systems, and data sources while refining its reasoning through iterative steps. Rather than simply generating responses from a single prompt, the agent performs structured multi-step reasoning processes that...
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  • 6
    FEAPDER

    FEAPDER

    Powerful Python crawler framework for scalable web scraping tasks

    feapder is a Python-based web crawling framework designed to simplify the process of building scalable and efficient web scrapers. It focuses on providing a developer-friendly environment that makes it easier to create, run, and manage crawlers for a variety of data collection tasks. It includes several built-in spider types, such as AirSpider, Spider, TaskSpider, and BatchSpider, which address different crawling scenarios ranging from lightweight scraping to distributed and batch-based...
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  • 7
    RecAI

    RecAI

    Bridging LLM and Recommender System

    RecAI is an open-source research platform developed by Microsoft to explore how large language models can be integrated into modern recommender systems. Traditional recommender systems rely on structured behavioral data such as user interactions and item embeddings, while large language models excel at understanding language and reasoning about user preferences. RecAI aims to bridge these two domains by creating architectures and training methods that allow LLMs to function as intelligent...
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  • 8
    KIS Open API

    KIS Open API

    Korea Investment & Securities Open API Github

    The open-trading-api repository from Korea Investment & Securities provides sample code and developer resources for interacting with the KIS Developers Open Trading API, which enables programmatic access to financial market data and automated trading functionality. The project is designed primarily for Python developers and AI automation environments that want to build investment applications, algorithmic trading systems, or financial analytics tools using the brokerage’s infrastructure. It...
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  • 9
    Hephaestus

    Hephaestus

    Semi-Structured Agentic Framework. Workflows build themselves

    Hephaestus is an open-source semi-structured agentic framework designed to orchestrate multiple AI agents working together on complex tasks. Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression,...
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  • 10
    BrowserGym

    BrowserGym

    A Gym environment for web task automation

    BrowserGym is an open framework for web task automation research that exposes browser interaction as a Gym-style environment for training and evaluating agents. It is intended for researchers building web agents rather than for end users looking for a consumer automation product. The project provides a common environment where agents can interact with websites, execute tasks, and be evaluated against standardized benchmarks. One of its main strengths is that it bundles several important...
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  • 11
    Parallax

    Parallax

    Parallax is a distributed model serving framework

    Parallax is a decentralized inference framework designed to run large language models across distributed computing resources. Instead of relying on centralized GPU clusters in data centers, the system allows multiple heterogeneous machines to collaborate in serving AI inference workloads. Parallax divides model layers across different nodes and dynamically coordinates them to form a complete inference pipeline. A two-stage scheduling architecture determines how model layers are allocated to...
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  • 12
    MiniOneRec

    MiniOneRec

    Minimal reproduction of OneRec

    MiniOneRec is an open-source framework designed to explore generative approaches to recommendation systems using large language model architectures. Traditional recommender systems typically rely on large embedding tables and ranking models, but MiniOneRec adopts a generative paradigm in which items are represented as sequences of semantic identifiers generated by autoregressive models. The framework provides an end-to-end pipeline for building generative recommender systems, including...
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  • 13
    R-KV

    R-KV

    Redundancy-aware KV Cache Compression for Reasoning Models

    R-KV is an open-source research project that focuses on improving the efficiency of large language model inference through key-value cache compression techniques. Modern transformer models rely heavily on KV caches during autoregressive decoding, which store intermediate attention states to accelerate generation. However, these caches can consume large amounts of memory, especially in reasoning-oriented models with long context windows. R-KV introduces a method for compressing the KV cache...
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  • 14
    MetaScreener

    MetaScreener

    AI-powered tool for efficient abstract and PDF screening

    MetaScreener is an open-source AI-assisted tool designed to streamline the screening process in systematic literature reviews and academic research workflows. The system helps researchers analyze large collections of academic abstracts and research papers to determine which studies are relevant for inclusion in evidence synthesis projects. Instead of manually reviewing hundreds or thousands of documents, researchers can use MetaScreener to apply machine learning techniques that assist with...
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  • 15
    DATAGEN

    DATAGEN

    AI-driven multi-agent research assistant automating hypothesis

    DATAGEN is an AI-driven multi-agent research and data analysis platform designed to automate complex analytical workflows. The system coordinates multiple specialized AI agents that collaborate to perform tasks such as hypothesis generation, data collection, analysis, visualization, and report creation. Instead of requiring users to manually orchestrate each stage of a research process, the platform allows these agents to coordinate automatically and handle the workflow end-to-end. The...
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  • 16
    Bespoke Curator

    Bespoke Curator

    Synthetic data curation for post-training and data extraction

    Curator is an open-source Python library designed to build synthetic data pipelines for training and evaluating machine learning models, particularly large language models. The system helps developers generate, transform, and curate high-quality datasets by combining automated generation with structured validation and filtering. It supports workflows where models are used to produce synthetic examples that can later be refined into reliable training datasets for reasoning, question...
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  • 17
    MarkPDFDown

    MarkPDFDown

    A high-quality PDF to Markdown tool based on large language model

    MarkPDFdown is an open-source document processing tool designed to convert PDF files into structured Markdown output that can be easily used for documentation, content pipelines, and AI processing workflows. The project focuses on extracting text, formatting, and structural information from complex PDF documents and transforming that information into clean Markdown that preserves the original hierarchy of headings, paragraphs, tables, and lists. By producing Markdown rather than raw text,...
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  • 18
    OneFileLLM

    OneFileLLM

    Specify a github or local repo, github pull request

    OneFileLLM is an open-source project designed to simplify the distribution and execution of large language model applications by packaging them into a single portable file. The concept behind the project is to eliminate the complexity normally associated with deploying AI systems, which often require multiple dependencies, frameworks, and configuration steps. Instead, the entire runtime environment, model interface, and application logic are bundled together into a single executable...
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  • 19
    TokenCost

    TokenCost

    Easy token price estimates for 400+ LLMs. TokenOps

    TokenCost is an open-source developer utility designed to estimate the cost of using large language model APIs by calculating token usage and translating it into real monetary values. The tool focuses on helping developers understand how much their prompts and generated completions cost when interacting with commercial AI models. It works by counting tokens in prompts and responses before or after sending requests and then applying pricing information associated with different models. This...
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  • 20
    LLMs-Zero-to-Hero

    LLMs-Zero-to-Hero

    From nobody to big model (LLM) hero

    LLMs-Zero-to-Hero is an open-source educational project designed to guide learners through the complete process of understanding and building large language models from the ground up. The repository presents a structured learning pathway that begins with fundamental concepts in machine learning and progresses toward advanced topics such as model pre-training, fine-tuning, and deployment. Rather than relying entirely on existing frameworks, the project encourages readers to implement...
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  • 21
    MoBA

    MoBA

    MoBA: Mixture of Block Attention for Long-Context LLMs

    MoBA, short for Mixture of Block Attention, is an open-source research implementation of a novel attention mechanism designed to improve the efficiency of large language models processing extremely long contexts. The architecture adapts ideas from Mixture-of-Experts networks and applies them directly to the attention mechanism of transformer models. Instead of forcing each token to attend to every other token in the sequence, MoBA divides the context into blocks and dynamically routes...
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  • 22
    AI Engineering Academy

    AI Engineering Academy

    Mastering Applied AI, One Concept at a Time

    AI-Engineering.academy is a community-driven educational repository that organizes practical knowledge and learning paths for applied AI engineering. The project aims to make complex AI concepts accessible by structuring them into progressive learning modules covering topics such as prompt engineering, retrieval-augmented generation, LLM deployment, and AI agents. Rather than focusing purely on theoretical explanations, the repository emphasizes hands-on understanding of how modern AI...
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  • 23
    how-to-optim-algorithm-in-cuda

    how-to-optim-algorithm-in-cuda

    How to optimize some algorithm in cuda

    how-to-optim-algorithm-in-cuda is an open educational repository focused on teaching developers how to optimize algorithms for high-performance execution on GPUs using CUDA. The project combines technical notes, code examples, and practical experiments that demonstrate how common computational kernels can be optimized to improve speed and memory efficiency. Instead of presenting only theoretical explanations, the repository includes hand-written CUDA implementations of fundamental operations...
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  • 24
    SageAttention

    SageAttention

    NeurIPS2025 Spotlight] Quantized Attention

    SageAttention is an open-source optimization library designed to accelerate the attention mechanism used in transformer-based neural networks. Since attention operations are often the most computationally expensive component of modern AI models, SageAttention introduces quantization techniques that significantly reduce computational overhead while preserving model accuracy. The system achieves this by using low-precision numerical formats such as INT4, FP8, or INT8 to represent key matrices...
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  • 25
    VLMEvalKit

    VLMEvalKit

    Open-source evaluation toolkit of large multi-modality models (LMMs)

    VLMEvalKit is an open-source evaluation toolkit designed for benchmarking large vision-language models that combine visual understanding with natural language reasoning. The toolkit provides a unified framework that allows researchers and developers to evaluate multimodal models across a wide range of datasets and standardized benchmarks with minimal setup. Instead of requiring complex data preparation pipelines or multiple repositories for each benchmark, the system enables evaluation...
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