#jupyter_notebook#chatglm#chatglm3#gemma_2b_it#glm_4#internlm2#llama3#llm#lora#minicpm#q_wen#qwen#qwen1_5#qwen2
This guide helps beginners set up and use open-source large language models (LLMs) on Linux or cloud platforms like AutoDL, with step-by-step instructions for environment setup, model deployment, and fine-tuning for models such as LLaMA, ChatGLM, and InternLM[2][4][5]. It covers everything from basic installation to advanced techniques like LoRA and distributed fine-tuning, and supports integration with tools like LangChain and online demo deployment. The main benefit is making powerful AI models accessible and easy to use for students, researchers, and anyone interested in experimenting with or customizing LLMs for their own projects[2][4][5].
https://github.com/datawhalechina/self-llm
https://docs.python.org/3/library/marshal.html#module-marshal
marshal — Internal Python #object_serialization
This module contains functions that can read and write Python values in a binary format. The format is specific to Python, but independent of machine architecture issues (e.g., you can write a #Python value to a #file on a PC, transport the file to a Sun, and read it back there). Details of the format are undocumented on purpose; it may change between Python versions (although it rarely does).
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https://f-droid.org/packages/com.ammar.sharing/
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