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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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Crypto M - Crypto News

@CryptoM · Post #64625 · 04/09/2026, 11:54 AM

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🚀 Independent Miner Achieves Rare Bitcoin Block Discovery An independent Bitcoin miner achieved a rare feat by successfully mining a Bitcoin block with a very low probability. According to BlockBeats, the miner used CKpool's solo mining software to mine block number 944,306, earning a total of 3.128 Bitcoin. Data from block explorer Mempool indicates that the miner received a block subsidy of 3.125 Bitcoin and transaction fees amounting to 0.003 Bitcoin. CKpool developer Con Kolivas noted on the X platform that the miner accomplished this with just 70TH of computing power. The probability of a miner of this scale discovering a block daily is approximately one in 100,000, with an average occurrence of once every 300 years. #Bitcoin#mining#CKpool#blockchain#rarefeat#blockdiscovery#cryptocurrency#soloMining#Mempool#transactionfees#ConKolivas#BTC

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