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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 #346 · 06/21/2017, 07:24 AM

http://mongoengine.org To get to grips with MongoEngine, there is extensive documentation, API references and a tutorial. You can find help by joining the MongoEngine Users mailing list or by chatting with other users on the #mongoengine IRC channel.

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djangoproject

@djangoproject · Post #421 · 08/21/2017, 10:39 AM

https://alysivji.github.io/flask-part1-generating-html-pages-with-mongoengine-jinja2.html Generating HTML Pages from #MongoDB with #MongoEngine and #Jinja2 (Flask Part 1) Summary Overview of MongoDB Discussion of Object-Relational Mapping (#ORM) Use MongoEngine to get items out of MongoDB Render #HTML pages using Jinja2 Interact with #REST API to send emails with #Requests