Showing 4 open source projects for "server performance"

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  • 1
    Google Workspace MCP Server

    Google Workspace MCP Server

    Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms

    Google Workspace MCP is an open-source server that connects AI assistants to Google Workspace services through the Model Context Protocol (MCP), allowing large language models to interact directly with productivity tools. The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically. By acting as a bridge between AI clients and the Google...
    Downloads: 0 This Week
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  • 2
    CogVLM2

    CogVLM2

    GPT4V-level open-source multi-modal model based on Llama3-8B

    CogVLM2 is the second generation of the CogVLM vision-language model series, developed by ZhipuAI and released in 2024. Built on Meta-Llama-3-8B-Instruct, CogVLM2 significantly improves over its predecessor by providing stronger performance across multimodal benchmarks such as TextVQA, DocVQA, and ChartQA, while introducing extended context length support of up to 8K tokens and high-resolution image input up to 1344×1344. The series includes models for both image understanding and video...
    Downloads: 0 This Week
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  • 3
    Langcorn

    Langcorn

    Serving LangChain LLM apps automagically with FastApi

    LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience.
    Downloads: 0 This Week
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  • 4
    GLM-130B

    GLM-130B

    GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)

    ...It is designed for large-scale inference and supports both left-to-right generation and blank filling, making it versatile across NLP tasks. Trained on over 400 billion tokens (200B English, 200B Chinese), it achieves performance surpassing GPT-3 175B, OPT-175B, and BLOOM-176B on multiple benchmarks, while also showing significant improvements on Chinese datasets compared to other large models. The model supports efficient inference via INT8 and INT4 quantization, reducing hardware requirements from 8× A100 GPUs to as little as a single server with 4× RTX 3090s. ...
    Downloads: 0 This Week
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