Local Ai
Doom running on an LLM -- Hugging Face checkpoint included
There's no training anywhere in this. I ported Doom's actual rendering algorithm into transformer weights using a compiler I wrote (torchwright) -- every weight computed, none learned. The prompt carr
There's no training anywhere in this. I ported Doom's actual rendering algorithm into transformer weights using a compiler I wrote (torchwright) -- every weight computed, none learned. The prompt carries the level geometry, player position, and view direction; generation emits drawing commands; a 43-line host program turns them into pixels. Stock Phi3ForCausalLM architecture, loads in vanilla transformers with trust_remote_code=False. Two checkpoints: - 320x200 (the one in the write-up): 21B params, 85.87 GB. One frame is a 3,614-token prompt plus 53,747 generated tokens -- just under 40 minutes on a B200. - 80x50: same prompt format, same textures, 34 GB download. This is the one to actually try. One honest disclaimer: I have not run this locally -- I've been using cloud GPUs (B200 and A100-80). My compiler currently requires fp32 precision in the weights, and I haven't yet explored quantization. For the 80x50 model I'd recommend 80 GB of GPU memory; 64 GB should work in theory but I haven't tried it. Write-up: https://ood.dev/posts/doom/ Weights (80x50): https://huggingface.co/physicsrob/torchwright-doom-e1m1-80x50 Weights (320x200): https://huggingface.co/physicsrob/torchwright-doom-e1m1 Source: https://github.com/physicsrob/torchwright_doom submitted by /u/notforrob [link] [comments]
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Source: r/LocalLLaMA | 2026-08-13