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Objz's LLM isn't actually that large. It only has a 64-dimensional embedding space, a 256-neuron hidden layer, and a tiny vocabulary of 2,048 words, and was trained on 11,118 DailyDialog conversations. Users can talk to it using the game's "/dialog" functionality, and the output stream comes back one word at a time, as with typical chatbots.
Even with that training, however, the author notes that this LLM is only conversational and isn't particularly clever, as it cannot do math and has no broad knowledge.
Even still, the work on display is quite impressive. 445,000 command blocks sounds like a lot until you realize that this kind of data and computational structure would require literally millions of cubes were it not optimized. Indeed, the original version, even with the aforementioned capabilities, rang in at nearly 2 million blocks. LLM weights are normally represented with floating-point values, which would be prohibitively complicated to implement, so Objz opted to use only ternary values for the weights: -1, 0, and +1.
Minecraft's "scoreboard" command supports multiplication and division, but it's integer-based, and using it would require integer-float conversion, thus adding extra operations. The initial quantization to -1/0/+1 kills two zombies with one stone, skipping commands and shrinking the dataset. The equivalent of each multiply-accumulate operation, then, averages 0.67 commands thanks to all the null values.
Colibrì proof-of-concept gains frontier-level 1.5-TB AI model
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Reference reading
- https://www.tomshardware.com/video-games/SPONSORED_LINK_URL
- https://www.tomshardware.com/video-games/minecraft-creator-works-around-in-game-math-limitations-to-implement-an-llm-using-445k-command-blocks-clever-approach-shrank-initial-block-count-from-over-1-million-requires-no-mods-plugins-or-datapacks-to-work#main
- https://www.tomshardware.com/membership
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