#jupyter_notebook
Unsloth is a tool that makes it much faster and easier to fine-tune large language models like Llama, Mistral, and Gemma, even on regular computers or single GPUs. It uses smart tricks to speed up training by 2 to 5 times and cuts memory use by up to 70%, so you can train models quickly without needing expensive hardware[1][3][4]. The benefit is that anyone—developers, researchers, or AI fans—can create custom AI models for different tasks, from chatting to vision, in less time and with less hassle, using ready-made notebooks and guides for popular models[3][5].
https://github.com/unslothai/notebooks
https://github.com/aio-libs/aiohttp-mako
#mako template renderer for #aiohttp.web based on aiohttp_jinja2. Library has almost same api and support python 3.5 (PEP492) syntax. It is used in aiohttp_debugtoolbar.
#Mako is a #template library written in Python. It provides a familiar, non-XML syntax which compiles into Python modules for maximum performance. Mako's syntax and #API borrows from the best ideas of many others, including #Django and #Jinja2 templates, #Cheetah, #Myghty, and #Genshi. Conceptually, Mako is an embedded Python (i.e. Python Server Page) language, which refines the familiar ideas of componentized layout and inheritance to produce one of the most straightforward and flexible models available, while also maintaining close ties to Python calling and scoping semantics.
http://www.makotemplates.org/