Instructions to use physicsrob/torchwright-doom-e1m1-80x50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use physicsrob/torchwright-doom-e1m1-80x50 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="physicsrob/torchwright-doom-e1m1-80x50")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("physicsrob/torchwright-doom-e1m1-80x50")
model = AutoModelForCausalLM.from_pretrained("physicsrob/torchwright-doom-e1m1-80x50", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use physicsrob/torchwright-doom-e1m1-80x50 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "physicsrob/torchwright-doom-e1m1-80x50"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "physicsrob/torchwright-doom-e1m1-80x50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/physicsrob/torchwright-doom-e1m1-80x50

SGLang

How to use physicsrob/torchwright-doom-e1m1-80x50 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "physicsrob/torchwright-doom-e1m1-80x50" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "physicsrob/torchwright-doom-e1m1-80x50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "physicsrob/torchwright-doom-e1m1-80x50" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "physicsrob/torchwright-doom-e1m1-80x50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use physicsrob/torchwright-doom-e1m1-80x50 with Docker Model Runner:
```
docker model run hf.co/physicsrob/torchwright-doom-e1m1-80x50
```

TorchWright Doom — E1M1

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:

from pathlib import Path
from huggingface_hub import hf_hub_download
from transformers import pipeline

repo = "physicsrob/torchwright-doom-e1m1-80x50"
prompt = Path(hf_hub_download(repo, "examples/e1m1_prompt.txt")).read_text()
generate = pipeline("text-generation", model=repo, device_map="auto")
generated_text = generate(prompt, return_full_text=False)[0]["generated_text"]

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).

Downloads last month: 215

Safetensors

Model size

9B params

Tensor type

F32