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Source channel @githubtrending · Post #14815 · Jun 10

#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

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Google Facts™ [ ️@googlefactss🌎]

@googlefactss · Post #40776 · 03/11/2026, 11:01 PM

During World War II, engineers studied planes that returned from missions. They first thought the areas with the most bullet holes needed armor. Statistician Abraham Wald realized this was survivorship bias. Survivorship bias happens when you focus only on survivors and ignore failures.The undamaged areas on returning planes were actually the critical spots. Planes hit there did not survive. He recommended reinforcing those undamaged areas. ✈️📊🛡️ [Read more] @googlefactss #SurvivorshipBias#WWII#AbrahamWald#Planes#Statistics#History