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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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Venture Village Wall 🦄

@venturevillagewall · Post #4266 · 02/28/2025, 07:00 AM

AI Predictions: From Vacuums to Real Impact AI-powered prediction platforms are gaining traction, with a focus on forecasting reactions based on content and audience. Initial predictions using tools like ChatGPT yield 17% accuracy, but considering audience interactions can boost accuracy to 83%. This innovative approach helped a startup refine its pitch to enter Y Combinator. Discover more insights on enhancing prediction accuracy in various fields here. #AI#Startup#Prediction#YCombinator#Marketing#Innovation#Tech#Growth#Entrepreneurship#Forecasting#AudienceAnalysis#DataScience#MachineLearning#Success#Business#Trends#Platforms#Metrics#Investment#VC