Showing 4 open source projects for "parallel compression"

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  • 1
    Koog

    Koog

    Koog is the official Kotlin framework for building AI agents

    ...It features pure Kotlin implementation, seamless Model Control Protocol (MCP) integration for enhanced model management, vector embeddings for semantic search, and a flexible system for creating and extending tools that access external systems and APIs. Ready‑to‑use components address common AI engineering challenges, while intelligent history compression optimizes token usage and preserves context. A powerful streaming API enables real‑time response processing and parallel tool calls. Persistent memory allows agents to retain knowledge across sessions and between agents, and comprehensive tracing facilities provide detailed debugging and monitoring.
    Downloads: 0 This Week
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  • 2
    MSA: Memory Sparse Attention

    MSA: Memory Sparse Attention

    Trainable latent-memory framework for 100M-token contexts

    MSA, or Memory Sparse Attention, is a research framework for scaling language-model memory to extremely long contexts. It replaces full attention over all tokens with sparse selection of compressed latent memory states. Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. Its Memory Parallel engine distributes scoring and transfers...
    Downloads: 0 This Week
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  • 3
    ERNIE

    ERNIE

    The official repository for ERNIE 4.5 and ERNIEKit

    ...The project also emphasizes optimization techniques for large-scale training, including mixed-precision and hybrid-parallel strategies that are commonly needed for multi-node GPU clusters. In addition to training, it includes guidance and example materials intended to help developers adopt ERNIE models for real product scenarios rather than only research demonstrations.
    Downloads: 0 This Week
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  • 4
    ADAMS

    ADAMS

    ADAMS is a workflow engine for building complex knowledge workflows.

    ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA)...
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    Downloads: 9 This Week
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