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Source channel @githubtrending · Post #14845 · Jun 20
#jupyter_notebook#ai#artificial_intelligence#chatgpt#deep_learning#from_scratch#gpt#language_model#large_language_models#llm#machine_learning#python#pytorch#transformer You can learn how to build your own large language model (LLM) like GPT from scratch with clear, step-by-step guidance, including coding, training, and fine-tuning, all explained with examples and diagrams. This approach mirrors how big models like ChatGPT are made but is designed to run on a regular laptop without special hardware. You also get access to code for loading pretrained models and fine-tuning them for tasks like text classification or instruction following. This helps you deeply understand how LLMs work inside and lets you create your own functional AI assistant, gaining practical skills in AI development[1][2][3][4]. https://github.com/rasbt/LLMs-from-scratch
Search: #interpreter
@djangoproject · Post #156 · 09/06/2016, 01:43 AM
https://wiki.python.org/moin/GlobalInterpreterLock In #CPython, the #global#interpreter lock, or #GIL, is a mutex that prevents multiple native #threads from executing Python bytecodes at once. This lock is necessary mainly because CPython's memory management is not thread-safe. (However, since the GIL exists, other features have grown to depend on the guarantees that it enforces.)
@djangoproject · Post #159 · 09/12/2016, 05:37 PM
https://docs.python.org/3/library/atexit.html The #atexit module defines #functions to #register and #unregister cleanup functions. Functions thus registered are automatically executed upon normal interpreter termination. atexit runs these functions in the reverse order in which they were registered; if you register A, B, and C, at #interpreter#termination time they will be run in the order C, B, A.