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Language model token selection animated with Qwen2.5-0.5B probabilities

By PulseAugur Editorial · [1 sources] ·

A video explains the process by which language models select the next token, using real probability data from the Qwen2.5-0.5B model. The animation demonstrates how different sampling strategies like greedy selection, temperature, top-k, and top-p influence the token choice. It highlights that even with identical settings, variations in token selection can occur due to factors like server-dependent addition order. AI

IMPACT Provides a visual explanation of core LLM mechanics, aiding understanding for developers and researchers.

RANK_REASON The item describes a technical explanation and animation of a language model's internal workings, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Language model token selection animated with Qwen2.5-0.5B probabilities

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3 / 100
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The item describes a technical explanation and animation of a language model's internal workings, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Rajucg ·

    I animated how a language model picks the next token, with real probabilities from Qwen2.5-0.5B: softmax, greedy vs sampling, temperature, top-k, top-p (7 min video) [D]

    <!-- SC_OFF --><div class="md"><p>I made this explainer for my small channel and would like feedback from people learning this, especially if anything is wrong.</p> <p>It follows one real example the whole way. I gave Qwen2.5-0.5B-Instruct &quot;Write one sentence about the moon.…

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