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
```

physicsrob commited on 23 days ago

Commit

b9a8681

verified ·

1 Parent(s): 9a00822

Publish E1M1 38-layer seed-0 bundle

Browse files

This view is limited to 50 files because it contains too many changes. See raw diff

Files changed (50) hide show

.gitattributes +1 -0
README.md +32 -0
config.json +41 -0
doom_bundle_manifest.json +271 -0
doom_palette.json +1285 -0
doom_tables.json +297 -0
doom_vocab.json +3 -0
examples/e1m1_prompt.txt +1 -0
generation_config.json +6 -0
infer.py +252 -0
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.gitattributes CHANGED Viewed

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 *.zip filter=lfs diff=lfs merge=lfs -text
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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+doom_vocab.json filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,32 @@

+---
+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 ordinary Transformers auto classes 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.
+**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 weights load in full fp32 (total size = the
+sum of the shards; the flagship 320×200 bundle is ~98 GB, needing an
+H200/B200-class GPU or multi-GPU `device_map`). The flagship render measured
+~42 minutes of greedy decode on one B200 for its 53,747-token rollout from a
+3,614-token prompt, scoring ~99.99% within-option color against the
+reference renderer. Canonical numbers and their provenance: `FACTS.md` in
+the source repo (github.com/physicsrob/torchwright_doom).

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+  "max_position_embeddings": 65536,
+  "model_type": "phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 38,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 65536,
+  "pad_token_id": 133029,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "partial_rotary_factor": 0.5,
+    "rope_theta": 500000.0,
+    "rope_type": "default"
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+  "tie_word_embeddings": true,
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+  "use_cache": true,
+  "vocab_size": 133098
+}

doom_bundle_manifest.json ADDED Viewed

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+  "artifact_kind": "hf_phi3_bundle",
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+  "bundle_identity": "torchwright_doom.phi3.v1:bfd028379c63520b4e2aa3919fbdeb76c1b598580d425e65059f07dfcd4a33f7",
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+  },
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+  "source_revisions": {
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+    "torchwright_doom": "49feb52d455f1223d402fa585249ff24b6055c74"
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+      -1152.0,
+      1152.0
+    ],
+    "y": [
+      -1152.0,
+      1152.0
+    ]
+  },
+  "origin": [
+    1119.351851851852,
+    -3262.5
+  ],
+  "screen": {
+    "column_count": 160,
+    "height": 200,
+    "pixel_width": 2,
+    "width": 320
+  },
+  "sentinel_markers": [
+    "back.ceil",
+    "back.floor"
+  ],
+  "value_steps": 65535,
+  "wall_names": [
+    "BROWN1",
+    "BROWN144",
+    "COMPUTE2",
+    "DOOR3",
+    "DOORSTOP",
+    "LITE3",
+    "STARTAN3",
+    "STEP6",
+    "SUPPORT2"
+  ],
+  "x_coord_markers": [
+    "bbox0.left",
+    "bbox0.right",
+    "bbox1.left",
+    "bbox1.right",
+    "bx1",
+    "bx2",
+    "v1.x",
+    "v2.x",
+    "viewx",
+    "x"
+  ],
+  "y_coord_markers": [
+    "bbox0.bottom",
+    "bbox0.top",
+    "bbox1.bottom",
+    "bbox1.top",
+    "by1",
+    "by2",
+    "v1.y",
+    "v2.y",
+    "viewy",
+    "y"
+  ]
+}

doom_vocab.json ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:236b913f5ccdcc9cfac21b9ba063b25a155cb11f1cf571378e7ab978cfa27b77
+size 43892140

examples/e1m1_prompt.txt ADDED Viewed

	@@ -0,0 +1 @@

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texture.lower(none) lightStatic(light=20) emptyLine(no) closedDoor(no) pegging(no,no) rowoffset value(v=0.187488) seg(i=66,is_first_of_ss=no) v1.x value(v=-0.360555) v1.y value(v=0.137591) v2.x value(v=-0.360555) v2.y value(v=0.276478) oneSided normalAngle angleValue(angle=-4096) floor value(v=1.5259e-05) ceil value(v=0.281239) back.floor value(v=-0.971008) back.ceil value(v=-0.971008) texture.mid(STARTAN3) texture.upper(none) texture.lower(none) lightStatic(light=20) emptyLine(no) closedDoor(no) pegging(no,no) rowoffset value(v=1.5259e-05) ss(s=24) seg(i=67,is_first_of_ss=yes) v1.x value(v=-0.166094) v1.y value(v=0.165362) v2.x value(v=-0.193896) v2.y value(v=0.165362) oneSided normalAngle angleValue(angle=-2048) floor value(v=1.5259e-05) ceil value(v=0.281239) back.floor value(v=-0.971008) back.ceil value(v=-0.971008) texture.mid(BROWN1) texture.upper(none) texture.lower(none) lightStatic(light=28) emptyLine(no) closedDoor(no) pegging(no,no) rowoffset value(v=0.187488) ss(s=25) 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generation_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "bos_token_id": 133096,
+  "eos_token_id": 133029,
+  "pad_token_id": 133029,
+  "transformers_version": "5.13.1"
+}

infer.py ADDED Viewed

	@@ -0,0 +1,252 @@

+"""The sole Doom inference program: portable stock-Hugging-Face generation.
+This file is copied byte-identical to the root of every published Doom
+bundle (``<bundle>/infer.py``) and executed there as a subprocess — by
+anyone who downloads a bundle and by production render orchestration
+(``run.py``) alike.  It is executed, never imported.  It intentionally
+imports no TorchWright or ``torchwright_doom`` code: text enters through
+the saved tokenizer, a stock ``Phi3ForCausalLM`` produces rows (a "row" is
+one tokenizer id — one row of the tied embedding matrix, one sequence
+position), and the only outputs are canonical integer ids plus their raw
+tokenizer text.  No pixels are produced here: the bundle's standalone
+tools (``tools/txt_to_png.py``; token protocol in ``PROTOCOL.md`` in the
+source repo) decode those ids into a frame afterward and do no inference.
+The bundle manifest's schema, field meanings, and completeness gate live
+in ``torchwright_doom/bundle/manifest.py`` in the source repo.
+"""
+from __future__ import annotations
+import argparse
+import hashlib
+import json
+import os
+import time
+from pathlib import Path
+# Multi-shard checkpoints otherwise load serially.  Keep these defaults in the
+# portable program so downloaded bundles and production use the same loader.
+os.environ.setdefault("HF_ENABLE_PARALLEL_LOADING", "true")
+os.environ.setdefault("HF_PARALLEL_LOADING_WORKERS", "8")
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+_PROGRESS_INTERVAL_SECONDS = 15.0
+def _canonical_json(value) -> bytes:
+    return json.dumps(value, ensure_ascii=False, separators=(",", ":")).encode("utf-8")
+def _sha(data: bytes) -> str:
+    return hashlib.sha256(data).hexdigest()
+class _ProgressStreamer:
+    """Report generation throughput without changing or collecting tokens."""
+    def __init__(self, prompt_rows: int, max_new_tokens: int) -> None:
+        self.prompt_rows = prompt_rows
+        self.max_new_tokens = max_new_tokens
+        self.started = time.monotonic()
+        self.last_progress = self.started
+        self.prefill_seconds: float | None = None
+        self.finished: float | None = None
+        self.generated_rows = 0
+        self._saw_prompt = False
+    def put(self, value: torch.Tensor) -> None:
+        # GenerationMixin streams the complete prompt once before any emitted
+        # row.  It is already accounted for separately in ``prompt_rows``.
+        if not self._saw_prompt:
+            self._saw_prompt = True
+            return
+        now = time.monotonic()
+        if self.prefill_seconds is None:
+            self.prefill_seconds = now - self.started
+            print(
+                f"[infer] prefill complete; rows={self.prompt_rows} "
+                f"elapsed={self.prefill_seconds:.1f}s",
+                flush=True,
+            )
+        self.generated_rows += value.numel()
+        if now - self.last_progress >= _PROGRESS_INTERVAL_SECONDS:
+            elapsed = now - self.started - self.prefill_seconds
+            last_row = int(value.reshape(-1)[-1])
+            print(
+                f"[infer] decode rows={self.generated_rows}/{self.max_new_tokens} "
+                f"total_position={self.prompt_rows + self.generated_rows} "
+                f"elapsed={elapsed:.1f}s "
+                f"rows/s={self.generated_rows / elapsed:.1f} "
+                f"last_row={last_row}",
+                flush=True,
+            )
+            self.last_progress = now
+    def end(self) -> None:
+        self.finished = time.monotonic()
+    @property
+    def decode_seconds(self) -> float:
+        stopped = self.finished or time.monotonic()
+        prefill = self.prefill_seconds or 0.0
+        return stopped - self.started - prefill
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description="Run stock Phi-3 Doom inference")
+    parser.add_argument("--model", type=Path)
+    parser.add_argument("--prompt", type=Path)
+    parser.add_argument("--output", type=Path, default=Path("out"))
+    parser.add_argument(
+        "--device", default="cuda" if torch.cuda.is_available() else "cpu"
+    )
+    parser.add_argument("--max-new-tokens", type=int)
+    args = parser.parse_args(argv)
+    # This file sits at the bundle root, so its own directory is the bundle.
+    model_dir = (args.model or Path(__file__).resolve().parent).resolve()
+    prompt_path = args.prompt or model_dir / "examples" / "e1m1_prompt.txt"
+    manifest = json.loads((model_dir / "doom_bundle_manifest.json").read_text())
+    if not manifest.get("validation", {}).get("complete"):
+        raise ValueError("Doom bundle manifest is not complete")
+    prompt_bytes = prompt_path.read_bytes()
+    prompt_sha256 = _sha(prompt_bytes)
+    bundled_prompt = prompt_sha256 == manifest["prompt"]["sha256"]
+    load_t0 = time.monotonic()
+    tokenizer = AutoTokenizer.from_pretrained(model_dir)
+    load_kwargs = {}
+    if args.device != "cpu":
+        # Accelerate builds the skeleton on meta and dispatches each shard
+        # directly to the target device.  This avoids a second full-model
+        # ``model.to(cuda)`` pass through CPU-backed mmap pages.
+        load_kwargs["device_map"] = args.device
+    model = AutoModelForCausalLM.from_pretrained(
+        model_dir,
+        attn_implementation="eager",
+        dtype=torch.float32,
+        # Read each shard's bytes eagerly: deferring them to mmap page
+        # faults stalls badly on network filesystems, and eager reads are
+        # harmless on local disks.
+        disable_mmap=True,
+        **load_kwargs,
+    ).eval()
+    attention_implementation = getattr(model.config, "_attn_implementation", None)
+    if attention_implementation != "eager":
+        # Eager is the implementation the published render was validated
+        # under; fused kernels change fp accumulation order, and this check
+        # keeps every run on the validated numerics.
+        raise RuntimeError(
+            "Doom inference requires eager attention, got "
+            f"{attention_implementation!r}"
+        )
+    if (
+        model.config.original_max_position_embeddings
+        != model.config.max_position_embeddings
+    ):
+        raise RuntimeError(
+            "default-RoPE Doom model has inconsistent original/max position "
+            "capacity; GenerationMixin would discard its cache at the boundary"
+        )
+    load_seconds = time.monotonic() - load_t0
+    inputs = tokenizer(
+        prompt_bytes.decode("utf-8"),
+        return_tensors="pt",
+        add_special_tokens=False,
+    )
+    input_device = next(model.parameters()).device
+    inputs = inputs.to(input_device)
+    prompt_ids = [int(row) for row in inputs.input_ids[0].tolist()]
+    prompt_ids_sha256 = _sha(_canonical_json(prompt_ids))
+    # Only the bundled prompt has a manifest row-id expectation; a custom
+    # prompt is permitted, never verified, and recorded in the payload as
+    # matches_bundled_prompt=false.
+    if bundled_prompt and prompt_ids_sha256 != manifest["prompt"]["row_ids_sha256"]:
+        raise ValueError("bundled prompt text does not reproduce its manifest rows")
+    default_new = int(manifest["generation"]["max_new_tokens"])
+    max_new = default_new if args.max_new_tokens is None else int(args.max_new_tokens)
+    if max_new < 1:
+        raise ValueError("max-new-tokens must be >= 1")
+    if len(prompt_ids) + max_new > model.config.max_position_embeddings:
+        raise ValueError("requested generation exceeds model position capacity")
+    print(
+        f"[infer] model ready in {load_seconds:.1f}s; prompt={len(prompt_ids)} "
+        f"max_new_tokens={max_new} device={input_device}",
+        flush=True,
+    )
+    generate_t0 = time.monotonic()
+    progress = _ProgressStreamer(len(prompt_ids), max_new)
+    print(f"[infer] generation started; max_new_tokens={max_new}", flush=True)
+    with torch.inference_mode():
+        sequences = model.generate(
+            **inputs,
+            do_sample=False,
+            use_cache=True,
+            max_new_tokens=max_new,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            streamer=progress,
+        )
+    generate_seconds = time.monotonic() - generate_t0
+    generated = [int(row) for row in sequences[0, inputs.input_ids.shape[1] :].tolist()]
+    prefill_seconds = progress.prefill_seconds or generate_seconds
+    decode_seconds = progress.decode_seconds
+    raw_text = tokenizer.decode(
+        generated, skip_special_tokens=False, clean_up_tokenization_spaces=False
+    )
+    if not isinstance(raw_text, str):
+        raise TypeError("tokenizer returned a batched decode for one row list")
+    if tokenizer(raw_text, add_special_tokens=False)["input_ids"] != generated:
+        raise ValueError("decoded output text does not round-trip to generated rows")
+    args.output.mkdir(parents=True, exist_ok=True)
+    emitted_ids_sha256 = _sha(_canonical_json(generated))
+    stopped = bool(generated and generated[-1] == tokenizer.eos_token_id)
+    payload = {
+        "format": "torchwright_doom.output_ids.v1",
+        "bundle": manifest.get("bundle_identity"),
+        "compile_payload_sha256": manifest.get("compile_payload_sha256"),
+        "row_vocab_fingerprint": manifest.get("row_vocab_fingerprint"),
+        "prompt": {
+            "sha256": prompt_sha256,
+            "matches_bundled_prompt": bundled_prompt,
+            "row_ids": prompt_ids,
+            "row_ids_sha256": prompt_ids_sha256,
+        },
+        "emitted_row_ids": generated,
+        "emitted_row_ids_sha256": emitted_ids_sha256,
+        "generation": {
+            "mode": "transformers_generate",
+            "max_new_tokens": max_new,
+            "termination_reason": "terminal" if stopped else "cap",
+        },
+        "timing_seconds": {
+            "load": load_seconds,
+            "prefill": prefill_seconds,
+            "decode": decode_seconds,
+            "generate": generate_seconds,
+        },
+        "attention_implementation": attention_implementation,
+        "transformers_version": transformers.__version__,
+    }
+    (args.output / "output.ids.json").write_text(
+        json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8"
+    )
+    (args.output / "output.txt").write_text(raw_text + "\n", encoding="utf-8")
+    print(
+        f"[infer] wrote {len(generated)} rows in {generate_seconds:.1f}s; "
+        f"stopped={payload['generation']['termination_reason']}",
+        flush=True,
+    )
+    return 0
+if __name__ == "__main__":
+    raise SystemExit(main())

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