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bitnet    
n. 国际学术网路 ; 网络名,(BECAUSE IT'S TIME
NETWORK的缩写。一个以欧美各大学为主的学术网络结构,通过邮件网关和INTERNET可以互相发送邮件)

国际学术网路; 网路名,(BECAUSE IT'S TIME NETWORK的缩写。一个以欧美各大学为主的学术网络结构,通过邮件网关和INTERNET可以互相发送邮件)

/bit'net/ (Because It's Time NETwork) An academic
and research computer network connecting approximately 2500
computers. BITNET provides interactive, {electronic mail} and
file transfer services, using a {store and forward}
{protocol}, based on {IBM} {Network Job Entry} protocols.

Bitnet-II encapsulates the Bitnet protocol within {IP}
{packets} and depends on the {Internet} to route them. BITNET
traffic and Internet traffic are exchanged via several
{gateway} hosts.

BITNET is now operated by {CREN}.

BITNET is everybody's least favourite piece of the network.
The BITNET hosts are a collection of {IBM} {dinosaurs},
{VAXen} (with lobotomised communications hardware), and {Prime
Computer} supermini computers. They communicate using
80-character {EBCDIC} card images (see {eighty-column mind});
thus, they tend to mangle the {headers} and text of
third-party traffic from the rest of the {ASCII}/{RFC 822}
world with annoying regularity. BITNET is also notorious as
the apparent home of {BIFF}.

[{Jargon File}]

(2002-09-02)


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英文字典中文字典相关资料:


  • GitHub - microsoft BitNet: Official inference framework for 1-bit LLMs
    bitnet cpp is the official inference framework for 1-bit LLMs (e g , BitNet b1 58) It offers a suite of optimized kernels, that support fast and lossless inference of 1 58-bit models on CPU and GPU (NPU support will coming next)
  • BitNet - Official Inference Framework for 1-bit LLMs
    BitNet is a revolutionary framework for running 1-bit Large Language Models, providing efficient inference with reduced memory footprint and improved performance Developed by Microsoft, BitNet enables state-of-the-art language model capabilities with unprecedented efficiency
  • BitNet: Microsofts 1-Bit LLMs That Run on Your CPU
    BitNet is a neural network architecture where every weight is constrained to just three values: {-1, 0, +1} Technically, this is "ternary" or 1 58-bit quantization (since log₂ (3) ≈ 1 58) The key insight is that you can't just quantize an existing model to ternary weights and expect it to work
  • microsoft bitnet-b1. 58-2B-4T · Hugging Face
    This repository contains the weights for BitNet b1 58 2B4T, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale, developed by Microsoft Research
  • BITNET - Wikipedia
    BITNET, with Remote Spooling Communications Subsystem (RSCS) and the Network Job Entry (NJE) network protocol, was used for the huge IBM internal network known as VNET
  • BitNet: Scaling 1-bit Transformers for Large Language Models
    In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models Specifically, we introduce BitLinear as a drop-in replacement of the nn Linear layer in order to train 1-bit weights from scratch
  • BitNet - Microsoft Foundry Labs
    Developed by Microsoft Research, BitNet b1 58 2B4T is the first open-source, native 1-bit large language model (LLM) in which every parameter is ternary (i e , -1, 0, 1), at a 2-billion parameter scale
  • BitNet: Scaling 1-bit Transformers for Large Language Models
    In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models Specifically, we introduce BitLinear as a drop-in replacement of the this http URL layer in order to train 1-bit weights from scratch
  • Run Tiny AI Models Locally Using BitNet A Beginner Guide
    Instead of shrinking a large pretrained model, BitNet is designed from the beginning to run efficiently at very low precision This reduces memory usage and compute requirements while still keeping strong performance
  • BitNet: Microsoft’s 1-bit LLM Inference Framework | PyShine
    Explore BitNet, Microsoft’s official inference framework for 1-bit Large Language Models Learn how to run 100B parameter models on CPU at human reading speed with lossless inference





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