Category: Uncategorized
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Talking to openai about llms
Me: so an LLM is essentially a prediction engine that uses statistics and probability to predict the extension of input information by chopping the inputted information into tokens which becomes embeddings in a geometric semantic landscape and those input embeddings as multi DIMENSIONAL vectors find a mathematical match to to the vectors already frozen into…
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a short story
At the edge of the glass desert lived a lizard named Ash, who believed the world above him was made of dragons. Their green bodies twisted across the sky in every direction, thorned and ancient, hanging roots like beards and tails. The round old cacti at ground level were the silent elders, keeping watch over…
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How a Token Becomes Meaning Inside an LLM – A Frank-said / GPT-said dialogue in plain physical cause-and-effect language
Frank said:Let me see if I’ve finally got the picture. The word cat goes into the system, gets turned into a token, and then somehow the artificial neural network turns that into meaning. But I want this explained like a physical process. Not abstract handwaving. I want to know: what actually causes what? GPT said:Good.…
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Inside an LLM: From Prompt to Prediction
Course Description This course explains what happens inside a large language model after a user enters a prompt. Students move step by step through the inference pipeline: from raw text to tokens, embeddings, positional structure, self-attention, multilayer perceptrons, residual accumulation, final hidden states, logits, probabilities, and next-token generation. The course treats the LLM not as…
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INSIDE AN LLM (EXPANDED)
Block 1: USER PROMPT This is the human-facing beginning of the entire inference event. A user types something in ordinary language, such as “The cat sat on the mat,” and to the human mind that already feels like meaning. But to the model, at this instant, it is not yet meaning in any computational sense.…
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inside the llm
Here are explanations for each labeled processing block: Block 1: USER PROMPT This block marks the input stage of the inference process. The entire pipeline begins when natural language text, such as “The cat sat on the mat,” is provided. This raw data cannot be understood directly by a neural network. At this point, no…