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raincandy_U

raincandy-u
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reacted to theirpost with 🔥 7 days ago
20K parameters can tell a story. 🚀 🤗 We trained a ~20k-parameter Transformer that can actually write stories! https://huggingface.co/raincandy-u/MacroStories → ~50× smaller than the 1M-parameter TinyStories model → ~3,000× smaller than AlexNet → 81 KB in FP32 yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights. Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome. She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️
reacted to theirpost with 🔥 7 days ago
20K parameters can tell a story. 🚀 🤗 We trained a ~20k-parameter Transformer that can actually write stories! https://huggingface.co/raincandy-u/MacroStories → ~50× smaller than the 1M-parameter TinyStories model → ~3,000× smaller than AlexNet → 81 KB in FP32 yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights. Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome. She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️
posted an update 7 days ago
20K parameters can tell a story. 🚀 🤗 We trained a ~20k-parameter Transformer that can actually write stories! https://huggingface.co/raincandy-u/MacroStories → ~50× smaller than the 1M-parameter TinyStories model → ~3,000× smaller than AlexNet → 81 KB in FP32 yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights. Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome. She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️
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