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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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EdgeMarket.AI 📣

@edgemarketai · Post #7991 · 02/20/2026, 10:35 AM

High-profile matches expose more than skill — they reveal system dynamics. For Nottingham Forest vs Liverpool, EdgeMarket analyzes scenario formation: • Momentum vs control • Tactical flexibility • Fatigue and recovery cycles • Pressure response under crowd intensity Rather than framing outcomes as binary, we focus on how probabilities evolve before and during the match. Sport is one of the clearest real-world laboratories for decision intelligence. #DecisionIntelligence#SportsAnalytics#PremierLeague#EdgeMarket#SystemsThinking#OutcomeAnalysis