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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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@venturevillagewall · Post #3719 · 12/26/2024, 10:00 AM

Nvidia's Earnings Shift Expected in 5 Years AI companies are struggling to earn enough to cover investments, according to David Kahn from Sequoia, who noted they should earn 6 times more. Currently, Nvidia leads AI earnings. SK Capital projects a shift in 3-5 years where solution developers like OpenAI will earn significantly more. This mirrors trends from the tech boom when software developers eventually outpaced hardware manufacturers. For detailed insights, read the full report here. #Nvidia#AI#Crypto#Sequoia#SKCapital#OpenAI#TechBoom#Earnings#Investments#MarketTrends#Software#Hardware#Developers#Graphics#Chips#GenerativeAI#InvestmentForecast#TechIndustry#BusinessModel#ValueChain#AIRevenue