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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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djangoproject

@djangoproject · Post #195 · 11/08/2016, 03:18 AM

http://stackoverflow.com/questions/29269370/how-to-properly-create-and-run-concurrent-tasks-using-pythons-asyncio-module In the case of trying to concurrently run two looping Tasks, I've noticed that unless the Task has an internal await expression, it will get stuck in the while loop, effectively blocking other tasks from running (much like a normal while loop). However, as soon the Tasks have to wait--even for just a fraction of a second--they seem to run concurrently without an issue. Thus, the await statements seem to provide the event loop with a foothold for switching back and forth between the tasks, giving the effect of #concurrency. Example output with internal await: running async test ...#boo 0 ...#baa 0 ...boo 1 ...baa 1 ...boo 2 ...baa 2

cosplayupload

@cosplayuploadtest2 · Post #102722 · 03/23/2025, 03:37 AM

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