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Source channel @githubtrending · Post #15432 · Jan 23

#jupyter_notebook#chinese_llm#chinese_nlp#finetune#generative_ai#instruct_gpt#instruction_set#llama#llm#lora#open_models#open_source#open_source_models#qlora AirLLM is a tool that lets you run very large AI models on computers with limited memory by using a smart layer-by-layer loading technique instead of traditional compression methods. You can run a 70-billion-parameter model on just 4GB of GPU memory, or even a 405-billion-parameter model on 8GB, without losing model quality. The benefit is that you can use powerful AI models on affordable hardware without expensive upgrades, and the tool also offers optional compression features that can speed up performance by up to 3 times while maintaining accuracy. https://github.com/lyogavin/airllm

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#jupyter_notebook #chinese_llm #chinese_nlp #finetune #generative_ai #instruct_gpt #instruction_set #llama #llm #lora #open_models #open_source #open_source_models #qlora

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djangoproject

@djangoproject · Post #173 · 09/22/2016, 06:55 PM

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#signal — Set handlers for #asynchronous events This module provides mechanisms to use signal handlers in Python. The signal.signal() function allows to define custom handlers to be executed when a signal is received. A small number of default handlers are installed: #SIGPIPE is ignored (so write errors on pipes and sockets can be reported as ordinary Python exceptions) and #SIGINT is translated into a KeyboardInterrupt exception. #Asyncio https://docs.python.org/3.4/library/signal.html

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#signal #asynchronous #sigpipe #sigint #asyncio