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New LLM Launch: 217,000-Parameter Offline Model
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🚨 TRAIN BILLION-PARAMETER LLMS FROM SCRATCH ON A SINGLE GPU This repo proves you don’t need a datacenter or millions of dollars to train real LLMs. Ships with: • Full GPT-style model training from scratch • Tokenization, architecture & training loop • Advanced techniques that run on consumer hardware • Distributed training tricks optimized for single GPU Everything is open source and explained step-by-step. 👉 https://github.com/FareedKhan-dev/train-llm-from-scratch Who’s training their own bil
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Siempre que le preguntas a Claude si tu idea es buena, te dice que sí. Aunque sea mala. El LLM Council lo arregla: lanza 5 asesores con perspectivas opuestas, se revisan entre sí, y un juez te da el veredicto y el paso a seguir. El skill y la guía están en mi comunidad de WhatsApp. Link en mi perfil. #ClaudeCode #IA #Claude #Anthropic
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Siempre que le preguntas a Claude si tu idea es buena, te dice que sí. Aunque sea mala. El LLM Council lo arregla: lanza 5 asesores con perspectivas opuestas, se revisan entre sí, y un juez te da el veredicto y el paso a seguir. El skill y la guía están en mi comunidad de WhatsApp. Link en mi perfil. #ClaudeCode #IA #Claude #Anthropic
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Another life taken during the arsenal parade where a 20 year old was stabbed to death near north London Holloway this happened over 2 rival gang saw each other when will this stop???#government #LLM#arsenalparade #fyp
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New Chinese GLM 5.2 model straight from China blows away the competition, ranking as the best
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Kineski AI prijeti divovima iz Silicijske doline! 😬 Kineski AI model GLM-2 je gotovo na razini Claudea i ChatGPT-ja, ali uz otprilike šest puta manje troškove. #minideepseekmoment #llm
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Andrej Karpathy's explaination of Bigram Language Model explained Bigram LMs, though simple, it provides powerful insight into the inner mechanics of how tokens are processed in language models. This is a pre-amble for what's next: GPT, which is the 2nd part of the series The Bigram model here incorporates: Tokenization, Vocabulary, Negative Loss Function, Cross Entropy, Logits, SoftMax, Optimizer, and AdamW These are essential ingredients to understand in order to build our knowledge on how LLM
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