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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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Architectural Shovel / Любовь Дмитриева

@arch_shovel · Post #663 · 09/07/2022, 08:31 PM

C U R V E S / Under Magnitude by Marc Fornes / THEVERYMANY The strength of #undermagnitude is achieved by 'Intensive Curvature,' which is the maximization of double #curvature across the project while constraining maximum radii. The result is a #structure that has much tighter curvature with constant change of direction, and results in more structurally performance. 'Intensive Curvature' leads to the curly, tubular branching characteristics consistent across the studio's body of work. In order to achieve structural #stability , each stripe assumes high degrees of curvature individually and high degrees of double curvature in accumulation -- amounting to extreme structural #rigidity throughout the project. #arch_shovel#archdaily