Instructions to use physicsrob/torchwright-doom-e1m1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 13,702 Bytes
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This file is also copied into published bundles as ``tools/pretty_text.py``.
It therefore must not import TorchWright, torch, Transformers, or
``torchwright_doom``.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import sys
from pathlib import Path
def _sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def _scan(text: str) -> list[tuple[str, list[str] | None]]:
body = "\n".join(line.split("#", 1)[0] for line in text.splitlines())
out: list[tuple[str, list[str] | None]] = []
i, n = 0, len(body)
while i < n:
if body[i].isspace():
i += 1
continue
start = i
while i < n and not body[i].isspace() and body[i] != "(":
i += 1
name = body[start:i]
args = None
if i < n and body[i] == "(":
close = body.find(")", i)
if close < 0:
raise ValueError(f"unclosed token arguments after {name!r}")
inner = body[i + 1 : close].strip()
args = [part.strip() for part in inner.split(",")] if inner else []
i = close + 1
if name:
out.append((name, args))
return out
def _label(name: str, args: list[str] | None, *, compact: bool = False) -> str:
if not args:
return name
separator = "," if compact else ", "
return f"{name}({separator.join(args)})"
def _fmt_decimal(value: float, places: int) -> str:
if places <= 0:
return str(int(round(value)))
text = f"{value:.{places}f}".rstrip("0").rstrip(".")
return text or "0"
def _decode_float(lo: float, hi: float, encoded: float) -> float:
return lo + (float(encoded) + 1.0) * 0.5 * (hi - lo)
def _encode_float(lo: float, hi: float, value: float) -> float:
return (2.0 / (hi - lo)) * float(value) - (hi + lo) / (hi - lo)
def _level(value: float, steps: int) -> int:
return round((float(value) + 1.0) * 0.5 * steps)
class DoomTextFormatter:
def __init__(self, vocab: dict, tables: dict):
self.vocab_blob = vocab
self.tables = tables
self.words = list(vocab["words"])
self.labels = list(vocab["labels"])
if len(self.words) != int(vocab["n_rows"]) or len(self.labels) != len(
self.words
):
raise ValueError("frozen Doom vocabulary arrays have inconsistent widths")
self.word_to_id = {word: row for row, word in enumerate(self.words)}
self.label_to_id = {label: row for row, label in enumerate(self.labels)}
if len(self.word_to_id) != len(self.words) or len(self.label_to_id) != len(
self.labels
):
raise ValueError("frozen Doom vocabulary is not injective")
carrier = tables["carrier"]
self.value_start = int(carrier["value"]["start"])
self.value_size = int(carrier["value"]["size"])
self.angle_start = int(carrier["angle"]["start"])
self.angle_size = int(carrier["angle"]["size"])
self.angle_lo = int(carrier["angle"]["lo"])
self.value_steps = int(tables["value_steps"])
self.angle_bam = int(tables["angle_bam"])
# Sentinel encoding for "no back sector": one-sided walls have no
# back-sector heights, so the prompt carries this reserved value,
# rendered as "none".
self.sentinel_value = float(tables["back_height_sentinel"])
self.marker_range = {
key: (float(value[0]), float(value[1]))
for key, value in tables["marker_range"].items()
}
self.angle_markers = set(tables["angle_markers"])
self.sentinel_markers = set(tables["sentinel_markers"])
self.x_markers = set(tables["x_coord_markers"])
self.y_markers = set(tables["y_coord_markers"])
origin = tables.get("origin", [0.0, 0.0])
self.origin = (float(origin[0]), float(origin[1]))
self.header_levels = {
str(key): int(value)
for key, value in tables.get("header_levels", {}).items()
}
layout = tables.get("layout", {})
self.indent_unit = int(layout.get("indent_unit", 2))
self.field_indent = int(layout.get("field_indent", 4))
@classmethod
def from_bundle(
cls, bundle_dir: str | Path, *, allow_incomplete: bool = False
) -> "DoomTextFormatter":
directory = Path(bundle_dir)
manifest_path = directory / "doom_bundle_manifest.json"
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
if not allow_incomplete and not manifest.get("validation", {}).get("complete"):
raise ValueError("Doom bundle manifest is not complete")
files = manifest.get("files", {})
for name in ("doom_vocab.json", "doom_tables.json"):
path = directory / name
if not path.is_file():
raise FileNotFoundError(f"Doom bundle is missing {name}")
expected = files.get(name, {}).get("sha256")
if expected and _sha256(path) != expected:
raise ValueError(f"Doom bundle hash mismatch for {name}")
vocab = json.loads((directory / "doom_vocab.json").read_text(encoding="utf-8"))
tables = json.loads(
(directory / "doom_tables.json").read_text(encoding="utf-8")
)
if int(vocab["n_rows"]) != int(manifest["vocab_size"]):
raise ValueError("Doom formatter vocabulary width disagrees with manifest")
if vocab.get("fingerprint") != manifest.get("row_vocab_fingerprint"):
raise ValueError("Doom formatter row-vocabulary fingerprint mismatch")
screen = vocab.get("screen", {})
manifest_screen = manifest.get("screen", {})
if (screen.get("width"), screen.get("height")) != (
manifest_screen.get("width"),
manifest_screen.get("height"),
):
raise ValueError("Doom formatter screen identity mismatch")
words_digest = hashlib.sha256(
json.dumps(
vocab["words"], ensure_ascii=False, separators=(",", ":")
).encode("utf-8")
).hexdigest()
if words_digest != manifest.get("tokenizer_vocab_sha256"):
raise ValueError("Doom formatter tokenizer-word identity mismatch")
return cls(vocab, tables)
def rows_from_raw_text(self, raw_text: str) -> list[int]:
rows = []
for word in raw_text.split():
try:
rows.append(self.word_to_id[word])
except KeyError:
raise ValueError(f"unknown canonical Doom word: {word!r}") from None
return rows
def raw_text_from_rows(self, rows: list[int]) -> str:
# Same explicit non-negative contract as tokenizer/codec.py (the
# project-side codec): reject negative rows rather than inheriting
# Python list wraparound. Parity tests pin the two implementations.
out = []
for row in rows:
try:
index = int(row)
except (TypeError, ValueError):
raise ValueError("Doom row outside frozen vocabulary") from None
if not 0 <= index < len(self.words):
raise ValueError("Doom row outside frozen vocabulary")
out.append(self.words[index])
return " ".join(out)
def _carrier_kind(self, row: int) -> str | None:
if self.value_start <= row < self.value_start + self.value_size:
return "value"
if self.angle_start <= row < self.angle_start + self.angle_size:
return "angle"
return None
def _origin_shift(self, marker: str) -> float:
if marker in self.x_markers:
return self.origin[0]
if marker in self.y_markers:
return self.origin[1]
return 0.0
def _shortest_value(
self, lo: float, hi: float, carrier: float, shift: float
) -> str:
target = _level(carrier, self.value_steps)
physical = _decode_float(lo, hi, carrier) + shift
for places in range(10):
candidate = round(physical, places)
if (
_level(_encode_float(lo, hi, candidate - shift), self.value_steps)
== target
):
return _fmt_decimal(candidate, places)
return repr(physical)
def _render_carrier(self, marker: str, row: int) -> str:
if self._carrier_kind(row) == "value":
if marker not in self.marker_range:
raise ValueError(f"value follows non-marker {marker!r}")
lo, hi = self.marker_range[marker]
carrier = -1.0 + (row - self.value_start) / self.value_steps * 2.0
shift = self._origin_shift(marker)
physical = _decode_float(lo, hi, carrier) + shift
if (
marker in self.sentinel_markers
and abs(physical - self.sentinel_value) < 0.5
):
return "none"
return self._shortest_value(lo, hi, carrier, shift)
if marker not in self.angle_markers:
raise ValueError(f"angle carrier follows non-angle marker {marker!r}")
bam = row - self.angle_start + self.angle_lo
physical = bam * 360.0 / self.angle_bam
for places in range(10):
candidate = round(physical, places)
if round(candidate * self.angle_bam / 360.0) == bam:
return _fmt_decimal(candidate, places)
return repr(physical)
def _encode_carrier(self, marker: str, value: str) -> int:
if marker in self.marker_range:
lo, hi = self.marker_range[marker]
if marker in self.sentinel_markers and value == "none":
carrier = _encode_float(lo, hi, self.sentinel_value)
else:
carrier = _encode_float(
lo, hi, float(value) - self._origin_shift(marker)
)
return self.value_start + _level(carrier, self.value_steps)
if marker in self.angle_markers:
bam = round(float(value) * self.angle_bam / 360.0)
return self.angle_start + bam - self.angle_lo
raise ValueError(f"token {marker!r} cannot carry value {value!r}")
def _pretty_flat(self, rows: list[int]) -> str:
units = []
i = 0
while i < len(rows):
row = rows[i]
if self._carrier_kind(row):
raise ValueError(f"carrier at row-stream position {i} has no marker")
label = self.labels[row]
if i + 1 < len(rows) and self._carrier_kind(rows[i + 1]):
name, args = _scan(label)[0]
args = list(args or ())
args.append(self._render_carrier(name, rows[i + 1]))
label = _label(name, args)
i += 1
units.append(label)
i += 1
return " ".join(units)
def _layout(self, flat: str) -> str:
lines: list[str] = []
group: list[str] = []
level = 0
def flush() -> None:
if not group:
return
if group[0].split("(", 1)[0] in self.header_levels:
lines.append(" " * (level * self.indent_unit) + group[0])
if len(group) > 1:
lines.append(
" " * (level * self.indent_unit + self.field_indent)
+ " ".join(group[1:])
)
else:
lines.append(" ".join(group))
group.clear()
for name, args in _scan(flat):
if name in self.header_levels:
flush()
level = self.header_levels[name]
group.append(_label(name, args))
flush()
return "\n".join(lines)
def format_text(self, raw_tokenizer_text: str) -> str:
return self._layout(
self._pretty_flat(self.rows_from_raw_text(raw_tokenizer_text))
)
def parse_pretty_text(self, pretty_text: str) -> str:
rows: list[int] = []
for name, args in _scan(pretty_text):
pretty = _label(name, args)
row = self.label_to_id.get(pretty)
if row is not None:
rows.append(row)
continue
if not args:
raise ValueError(f"unknown pretty Doom token: {pretty!r}")
base = _label(name, args[:-1])
try:
rows.append(self.label_to_id[base])
except KeyError:
raise ValueError(f"unknown pretty Doom token: {pretty!r}") from None
rows.append(self._encode_carrier(name, args[-1]))
return self.raw_text_from_rows(rows)
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Format canonical Doom tokenizer text")
parser.add_argument("--bundle", type=Path)
parser.add_argument("--input", type=Path)
parser.add_argument("--output", type=Path)
args = parser.parse_args(argv)
bundle = args.bundle or Path(__file__).resolve().parent.parent
formatter = DoomTextFormatter.from_bundle(bundle)
raw = args.input.read_text(encoding="utf-8") if args.input else sys.stdin.read()
rendered = formatter.format_text(raw) + "\n"
if args.output:
args.output.write_text(rendered, encoding="utf-8")
else:
sys.stdout.write(rendered)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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