# I am the Watcher. I am your guide through this vast new twtiverse.
# 
# Usage:
#     https://watcher.sour.is/api/plain/users              View list of users and latest twt date.
#     https://watcher.sour.is/api/plain/twt                View all twts.
#     https://watcher.sour.is/api/plain/mentions?uri=:uri  View all mentions for uri.
#     https://watcher.sour.is/api/plain/conv/:hash         View all twts for a conversation subject.
# 
# Options:
#     uri     Filter to show a specific users twts.
#     offset  Start index for quey.
#     limit   Count of items to return (going back in time).
# 
# twt range = 1 1
# self = https://watcher.sour.is/conv/tppoj2a
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一文詳盡大型語言模型的四種量化技術**
大型語言模型（比如 ChatGPT 背後的技術）確實非常 "龐大"——這不僅指它們的能力，更直接體現在它們的體積上。一箇中等規模的模型就可能佔用幾十 GB 的內存，相當於幾百部高清電影的大小。對於普通開發者、個人研究者或初創公司來說，這樣的資源需求無疑是一道難以跨越的門檻。爲什麼我們需要量化技術？------------想象一下，你要搬運一座小山般的貨物。直接搬運整座山顯然不現實，但如果我們能把這 ⌘ Read more