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343 points sillysaurusx | 1 comments | | HN request time: 0.323s | source
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v64 ◴[] No.35028738[source]
If anyone is interested in running this at home, please follow the llama-int8 project [1]. LLM.int8() is a recent development allowing LLMs to run in half the memory without loss of performance [2]. Note that at the end of [2]'s abstract, the authors state "This result makes such models much more accessible, for example making it possible to use OPT-175B/BLOOM on a single server with consumer GPUs. We open-source our software." I'm very thankful we have researchers like this further democratizing access to this data and prying it out of the hands of the gatekeepers who wish to monetize it.

[1] https://github.com/tloen/llama-int8

[2] https://arxiv.org/abs/2208.07339

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swyx ◴[] No.35029601[source]
why is it that these models tend to be released as float16 and converting to int8 is left to the reader? is there something special about training that defaults you to float16?
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1. charcircuit ◴[] No.35033050[source]
Quantization and other optimizations are more for productionizing models. You start with something accurate and then you start making tradeoffs to get the inference time to fit into your compute, memory, and time budgets.