This is a stock Hugging Face Phi3ForCausalLM that renders DOOM through ordinary autoregressive inference. The model and the data-only fast tokenizer load through the ordinary Transformers text-generation pipeline without remote code.

The bundled examples/e1m1_prompt.txt is the executable prompt. Run infer.py (at the bundle root) to produce canonical emitted row ids and raw tokenizer text. tools/pretty_text.py formats that text for reading, while tools/txt_to_png.py independently decodes its cursor/pixel protocol into a PNG — every cursor move and pixel in that protocol is a model-emitted token. The protocol is specified in PROTOCOL.md in the source repo. Neither post-processing tool participates in inference or performs geometry, visibility, lighting, texture selection, or sorting.

Ordinary Transformers pipeline inference works directly, with no custom or remote model code:

The saved generation defaults are greedy and cover the complete frame. Use the shipped infer.py when canonical integer row IDs, progress reporting, and the exact terminal-token-preserving raw text are required.

Published checkpoints: 320×200 and 80×50. The compiler-facing source is torchwright_doom.

This bundle: screen 80×50, map E1M1, dense fp32 sharded safetensors, eager attention (the validated implementation), greedy decode, generation bound 8000 new tokens.

What running it takes: the fp32 weight shards total 31.75 GiB (34.09 GB). A complete 7,007-token pipeline render peaked at 43.48 GiB reserved on one A100-80GB. At the configured generation cap, the stock KV cache is bounded at 12.41 GiB; one full layer's cache-growth copy adds 0.18 GiB. This targets 64 GiB of total accelerator memory: one 64-GiB-class device, or two 32-GiB consumer GPUs with device_map="auto". The shipped infer.py records the allocator's measured peak in output.ids.json.

Canonical numbers and their provenance: FACTS.md in the source repo (github.com/physicsrob/torchwright_doom).