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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use physicsrob/torchwright-doom-e1m1 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"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use physicsrob/torchwright-doom-e1m1 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

torchwright-doom-e1m1 / README.md

physicsrob

Publish E1M1 38-layer seed-0 bundle

ed03dd6 verified 7 days ago

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	---
	library_name: transformers
	pipeline_tag: text-generation
	---

	# 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:

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

	repo = "physicsrob/torchwright-doom-e1m1"
	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](https://huggingface.co/physicsrob/torchwright-doom-e1m1)
	and [80×50](https://huggingface.co/physicsrob/torchwright-doom-e1m1-80x50).
	The compiler-facing source is
	[torchwright_doom](https://github.com/physicsrob/torchwright_doom).

	This bundle: screen 320×200, map
	E1M1, dense fp32 sharded safetensors, eager attention (the validated
	implementation), greedy decode, generation bound
	61440 new tokens.

	What running it takes: the fp32 weight shards total
	79.97 GiB (85.87 GB), needing a B200-class GPU
	or multi-GPU `device_map`. The flagship pipeline render peaked at 151.00 GiB
	reserved; greedy decode took 39.7 minutes on one B200 for its 53,747-token rollout
	from a 3,614-token prompt, scoring 99.9% within-option color against the
	reference renderer.

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