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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 #550 · 01/15/2018, 07:05 AM

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http://www.wikipython.com/other-concepts/anatomy-of-a-class/ It seems obvious, but note that you must define a class before you use it. When you create a #class, it establishes its own namespace and all its own local variables (except global definitions) exist only inside that #namespace. They do not interact with other variables of the same name outside it. This leads us to one very important “feature” of classes that you need to know. If you use the same word to designate some specific value both inside and outside the class blueprint, the instance value will take precedence when you try to use that value. #learn

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