Sync from GitHub via hub-sync

dots-mocr.py

@@ -4,15 +4,21 @@
 #     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm>=0.15.1",
 #     "tqdm",
 #     "toolz",
-#     "torch",
 #     # dots.mocr's remote code (AutoProcessor.register with a string key)
 #     # breaks on transformers v5; vllm pulls transformers unpinned.
+#     # Local `uv run --with vllm==...` only: on Jobs the header image's own
+#     # transformers wins via PYTHONPATH, so this pin has no effect there.
 #     "transformers<5",
 # ]
 #
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -37,6 +43,16 @@ SVG variant: rednote-hilab/dots.mocr-svg
 vLLM: Officially integrated since v0.11.0
 GitHub: https://github.com/rednote-hilab/dots.mocr
 Paper: https://arxiv.org/abs/2603.13032
+
+Run on HF Jobs (the [tool.hf-jobs] header sets image, flavor and secrets;
+needs the hf CLI 1.32+):
+
+    hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
+        input-dataset output-dataset
+
+On your own GPU, vLLM is not in the dependencies (the Jobs image provides it):
+
+    uv run --with vllm==0.29.0 dots-mocr.py input-dataset output-dataset
 """
 
 import argparse
@@ -565,24 +581,24 @@ if __name__ == "__main__":
         print("  general        - Free-form (use with --custom-prompt)")
         print("\nExample usage:")
         print("\n1. Basic OCR:")
-        print("   uv run dots-mocr.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.29.0 dots-mocr.py input-dataset output-dataset")
         print("\n2. SVG generation:")
         print(
-            "   uv run dots-mocr.py charts svg-output --prompt-mode svg --model rednote-hilab/dots.mocr-svg"
+            "   uv run --with vllm==0.29.0 dots-mocr.py charts svg-output --prompt-mode svg --model rednote-hilab/dots.mocr-svg"
         )
         print("\n3. Web screen parsing:")
-        print("   uv run dots-mocr.py screenshots parsed --prompt-mode web-parsing")
+        print("   uv run --with vllm==0.29.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing")
         print("\n4. Layout analysis with structure:")
-        print("   uv run dots-mocr.py papers analyzed --prompt-mode layout-all")
+        print("   uv run --with vllm==0.29.0 dots-mocr.py papers analyzed --prompt-mode layout-all")
         print("\n5. Running on HF Jobs:")
-        print("   hf jobs uv run --flavor l4x1 \\")
-        print("     -s HF_TOKEN \\")
+        print("   # [tool.hf-jobs] header sets image/flavor/secrets (hf CLI 1.32+)")
+        print("   hf jobs uv run \\")
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \\"
         )
         print("       input-dataset output-dataset")
         print("\n" + "=" * 80)
-        print("\nFor full help, run: uv run dots-mocr.py --help")
+        print("\nFor full help, run: uv run --with vllm==0.29.0 dots-mocr.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -606,19 +622,19 @@ SVG Code Generation:
 
 Examples:
   # Basic text OCR (default)
-  uv run dots-mocr.py my-docs analyzed-docs
+  uv run --with vllm==0.29.0 dots-mocr.py my-docs analyzed-docs
 
   # SVG generation with optimized variant
-  uv run dots-mocr.py charts svg-out --prompt-mode svg --model rednote-hilab/dots.mocr-svg
+  uv run --with vllm==0.29.0 dots-mocr.py charts svg-out --prompt-mode svg --model rednote-hilab/dots.mocr-svg
 
   # Web screen parsing
-  uv run dots-mocr.py screenshots parsed --prompt-mode web-parsing
+  uv run --with vllm==0.29.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing
 
   # Full layout analysis with structure
-  uv run dots-mocr.py papers structured --prompt-mode layout-all
+  uv run --with vllm==0.29.0 dots-mocr.py papers structured --prompt-mode layout-all
 
   # Random sampling for testing
-  uv run dots-mocr.py large-dataset test --max-samples 50 --shuffle
+  uv run --with vllm==0.29.0 dots-mocr.py large-dataset test --max-samples 50 --shuffle
         """,
     )
 

dots-ocr.py

@@ -4,16 +4,22 @@
 #     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm>=0.15.1",
 #     "tqdm",
 #     "toolz",
-#     "torch",
 #     # dots.ocr's remote code (AutoProcessor.register with a string key)
 #     # breaks on transformers v5; vllm pulls transformers unpinned.
 #     # Same fix as dots-mocr (#76).
+#     # Local `uv run --with vllm==...` only: on Jobs the header image's own
+#     # transformers wins via PYTHONPATH, so this pin has no effect there.
 #     "transformers<5",
 # ]
 #
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -31,6 +37,16 @@ Features:
 
 Model: rednote-hilab/dots.ocr
 vLLM: Officially tested with 0.9.1+ (native support via PR #24645)
+
+Run on HF Jobs (the [tool.hf-jobs] header sets image, flavor and secrets;
+needs `hf` CLI 1.32+):
+
+    hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \\
+        input-dataset output-dataset
+
+Run on your own GPU (vLLM and torch come from the image on Jobs, so add them):
+
+    uv run --with vllm==0.29.0 dots-ocr.py input-dataset output-dataset
 """
 
 import argparse
@@ -478,28 +494,25 @@ if __name__ == "__main__":
         print("- 📝 Layout-aware text extraction")
         print("\nExample usage:")
         print("\n1. Basic OCR:")
-        print("   uv run dots-ocr.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.29.0 dots-ocr.py input-dataset output-dataset")
         print("\n2. With custom settings:")
         print(
-            "   uv run dots-ocr.py docs analyzed-docs --batch-size 20 --max-samples 100"
+            "   uv run --with vllm==0.29.0 dots-ocr.py docs analyzed-docs --batch-size 20 --max-samples 100"
         )
         print("\n3. Layout analysis with structure:")
         print(
-            "   uv run dots-ocr.py papers analyzed-structure --prompt-mode layout-all"
+            "   uv run --with vllm==0.29.0 dots-ocr.py papers analyzed-structure --prompt-mode layout-all"
         )
         print("\n4. Layout detection only (no text):")
-        print("   uv run dots-ocr.py docs layout-info --prompt-mode layout-only")
-        print("\n5. Running on HF Jobs:")
-        print("   hf jobs uv run --flavor l4x1 \\")
-        print(
-            '     -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \\'
-        )
+        print("   uv run --with vllm==0.29.0 dots-ocr.py docs layout-info --prompt-mode layout-only")
+        print("\n5. Running on HF Jobs (header sets image/flavor/secrets; hf CLI 1.32+):")
+        print("   hf jobs uv run \\")
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \\"
         )
         print("       input-dataset output-dataset")
         print("\n" + "=" * 80)
-        print("\nFor full help, run: uv run dots-ocr.py --help")
+        print("\nFor full help, run: uv run --with vllm==0.29.0 dots-ocr.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -513,13 +526,13 @@ Prompt Modes (official DoTS.ocr prompts):
 
 Examples:
   # Basic text OCR (default)
-  uv run dots-ocr.py my-docs analyzed-docs
+  uv run --with vllm==0.29.0 dots-ocr.py my-docs analyzed-docs
 
   # Full layout analysis with structure
-  uv run dots-ocr.py papers structured --prompt-mode layout-all
+  uv run --with vllm==0.29.0 dots-ocr.py papers structured --prompt-mode layout-all
 
   # Random sampling for testing
-  uv run dots-ocr.py large-dataset test --max-samples 50 --shuffle
+  uv run --with vllm==0.29.0 dots-ocr.py large-dataset test --max-samples 50 --shuffle
         """,
     )
 

glm-ocr-bucket.py

@@ -13,13 +13,16 @@
 # [tool.uv]
 # prerelease = "allow"
 # override-dependencies = ["transformers>=5.1.0"]
+#
+# [tool.hf-jobs]
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
 OCR images and PDFs from a directory using GLM-OCR, writing markdown files.
 
-Designed to work with HF Buckets mounted as volumes via `hf jobs uv run -v ...`
-(requires huggingface_hub with PR #3936 volume mounting support).
+Designed to work with HF Buckets mounted as volumes via `hf jobs uv run -v ...`.
 
 The script reads images/PDFs from INPUT_DIR, runs GLM-OCR via vLLM, and writes
 one .md file per image (or per PDF page) to OUTPUT_DIR, preserving directory structure.
@@ -36,11 +39,11 @@ Examples:
   # Local test
   uv run glm-ocr-bucket.py ./test-images ./test-output
 
-  # HF Jobs with bucket volumes (PR #3936)
-  hf jobs uv run --flavor l4x1 \\
-      -s HF_TOKEN \\
-      -v bucket/user/ocr-input:/input:ro \\
-      -v bucket/user/ocr-output:/output \\
+  # HF Jobs with bucket volumes (flavor + secrets come from the
+  # [tool.hf-jobs] header; needs hf CLI 1.32+)
+  hf jobs uv run \\
+      -v hf://buckets/user/ocr-input:/input:ro \\
+      -v hf://buckets/user/ocr-output:/output \\
       glm-ocr-bucket.py /input /output
 
 Model: zai-org/GLM-OCR (0.9B, 94.62% OmniDocBench V1.5, MIT licensed)
@@ -102,15 +105,26 @@ def make_ocr_message(image: Image.Image, task: str = "ocr") -> list[dict]:
     ]
 
 
-def discover_files(input_dir: Path) -> list[Path]:
-    """Walk input_dir recursively, returning sorted list of image and PDF files."""
+def discover_files(input_dir: Path, limit: int | None = None) -> list[Path]:
+    """Discover image and PDF files under input_dir.
+
+    Without `limit`, returns the full sorted list (deterministic order).
+    With `limit`, stops scanning once `limit` matching files are found
+    and returns them in filesystem order (much faster on huge mounted
+    buckets, but ordering is not deterministic).
+    """
     files = []
-    for path in sorted(input_dir.rglob("*")):
+    iterator = (
+        input_dir.rglob("*") if limit is not None else sorted(input_dir.rglob("*"))
+    )
+    for path in iterator:
         if not path.is_file():
             continue
         ext = path.suffix.lower()
         if ext in IMAGE_EXTENSIONS or ext == ".pdf":
             files.append(path)
+            if limit is not None and len(files) >= limit:
+                break
     return files
 
 
@@ -175,10 +189,10 @@ Examples:
   uv run glm-ocr-bucket.py ./images ./output
   uv run glm-ocr-bucket.py /input /output --task table --pdf-dpi 200
 
-HF Jobs with bucket volumes (requires huggingface_hub PR #3936):
-  hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
-      -v bucket/user/input-bucket:/input:ro \\
-      -v bucket/user/output-bucket:/output \\
+HF Jobs with bucket volumes (flavor + secrets from the [tool.hf-jobs] header; hf CLI 1.32+):
+  hf jobs uv run \\
+      -v hf://buckets/user/input-bucket:/input:ro \\
+      -v hf://buckets/user/output-bucket:/output \\
       glm-ocr-bucket.py /input /output
         """,
     )
@@ -232,6 +246,14 @@ HF Jobs with bucket volumes (requires huggingface_hub PR #3936):
         default=1.1,
         help="Repetition penalty (default: 1.1)",
     )
+    parser.add_argument(
+        "--max-samples",
+        type=int,
+        default=None,
+        help="Limit the number of input files to process (files, not pages; "
+        "a PDF still yields all its pages). Stops scanning early once the "
+        "limit is reached; file order is then filesystem-dependent.",
+    )
     parser.add_argument(
         "--verbose",
         action="store_true",
@@ -239,6 +261,8 @@ HF Jobs with bucket volumes (requires huggingface_hub PR #3936):
     )
 
     args = parser.parse_args()
+    if args.max_samples is not None and args.max_samples < 1:
+        parser.error("--max-samples must be 1 or more")
 
     check_cuda_availability()
 
@@ -254,8 +278,14 @@ HF Jobs with bucket volumes (requires huggingface_hub PR #3936):
     # Discover and prepare
     start_time = time.time()
 
-    logger.info(f"Scanning {input_dir} for images and PDFs...")
-    files = discover_files(input_dir)
+    if args.max_samples is not None:
+        logger.info(
+            f"Scanning {input_dir} for up to {args.max_samples} images/PDFs "
+            f"(early termination, --max-samples)..."
+        )
+    else:
+        logger.info(f"Scanning {input_dir} for images and PDFs...")
+    files = discover_files(input_dir, limit=args.max_samples)
     if not files:
         logger.error(f"No image or PDF files found in {input_dir}")
         sys.exit(1)
@@ -348,7 +378,7 @@ if __name__ == "__main__":
         print("GLM-OCR Bucket Script")
         print("=" * 60)
         print("\nOCR images/PDFs from a directory → markdown files.")
-        print("Designed for HF Buckets mounted as volumes (PR #3936).")
+        print("Designed for HF Buckets mounted as volumes.")
         print()
         print("Usage:")
         print("  uv run glm-ocr-bucket.py INPUT_DIR OUTPUT_DIR")
@@ -357,10 +387,11 @@ if __name__ == "__main__":
         print("  uv run glm-ocr-bucket.py ./images ./output")
         print("  uv run glm-ocr-bucket.py /input /output --task table")
         print()
-        print("HF Jobs with bucket volumes:")
-        print("  hf jobs uv run --flavor l4x1 -s HF_TOKEN \\")
-        print("      -v bucket/user/ocr-input:/input:ro \\")
-        print("      -v bucket/user/ocr-output:/output \\")
+        print("HF Jobs with bucket volumes (flavor + secrets from the")
+        print("[tool.hf-jobs] header; needs hf CLI 1.32+):")
+        print("  hf jobs uv run \\")
+        print("      -v hf://buckets/user/ocr-input:/input:ro \\")
+        print("      -v hf://buckets/user/ocr-output:/output \\")
         print("      glm-ocr-bucket.py /input /output")
         print()
         print("For full help: uv run glm-ocr-bucket.py --help")

lfm2-extract.py

@@ -3,12 +3,17 @@
 # dependencies = [
 #     "datasets>=4.0.0",
 #     "huggingface-hub",
-#     "vllm",
 #     "transformers",
 #     "tqdm",
 #     "toolz",
-#     "torch",
 # ]
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 """
 Extract structured data (JSON / XML / YAML) from text using LiquidAI's LFM2-1.2B-Extract.
@@ -28,18 +33,20 @@ Pass `--schema` as inline text/JSON, a URL, or a file path describing the struct
 Model:  https://huggingface.co/LiquidAI/LFM2-1.2B-Extract
 Docs:   https://docs.liquid.ai/deployment/gpu-inference/vllm
 
-HF Jobs note: run on the vLLM image so the CUDA toolkit + prebuilt FlashInfer kernels are
-present and startup is fast (it reuses the image's CUDA-matched vLLM build):
+HF Jobs: the `[tool.hf-jobs]` header above pins the vLLM image (vLLM + torch come from the
+image, not the deps), the GPU flavor and the HF_TOKEN secret, so no flags are needed
+(requires `hf` CLI 1.32+):
 
-    hf jobs uv run --flavor l4x1 --secrets HF_TOKEN \
-        --image vllm/vllm-openai --python /usr/bin/python3 \
-        -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \
+    hf jobs uv run \
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lfm2-extract.py \
         INPUT OUTPUT --text-column text --schema '{"field": "description"}'
 
-It also runs on the default uv image, just with a slower first-time vLLM build. Deps are left
-unpinned so uv resolves a recent vLLM; FlashInfer sampling is disabled (see below) so the engine
-never JIT-compiles a kernel that needs nvcc — absent from the default image.
+On your own GPU, supply vLLM yourself:
+
+    uv run --with vllm==0.29.0 lfm2-extract.py INPUT OUTPUT --schema '{"field": "description"}'
+
+FlashInfer sampling is disabled (see below) so the engine never JIT-compiles a kernel that
+needs nvcc.
 """
 
 import argparse
@@ -72,7 +79,7 @@ FORMATS = {"json": "JSON", "xml": "XML", "yaml": "YAML"}
 def check_cuda_availability() -> None:
     if not torch.cuda.is_available():
         logger.error("CUDA is not available. This script requires a GPU.")
-        logger.error("Run on Hugging Face Jobs with: hf jobs uv run --flavor l4x1 ...")
+        logger.error("Run on Hugging Face Jobs with: hf jobs uv run lfm2-extract.py ...")
         sys.exit(1)
     logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name()}")
 
@@ -264,13 +271,15 @@ if __name__ == "__main__":
     if len(sys.argv) == 1:
         print("LFM2-1.2B-Extract — structured extraction (JSON/XML/YAML) from text")
         print("\nUsage:")
-        print("  uv run lfm2-extract.py INPUT OUTPUT --schema SCHEMA [--text-column text] [--format json]")
+        print("  uv run --with vllm==0.29.0 lfm2-extract.py INPUT OUTPUT --schema SCHEMA [--text-column text] [--format json]")
         print("\nExample:")
-        print('  uv run lfm2-extract.py my-docs my-fields \\')
+        print('  uv run --with vllm==0.29.0 lfm2-extract.py my-docs my-fields \\')
         print('    --text-column markdown \\')
         print('    --schema \'{"title": "the title", "date": "any date", "summary": "one sentence"}\'')
         print("\n  --schema accepts inline text/JSON, a URL, or a file path.")
-        print("\nFor full help: uv run lfm2-extract.py --help")
+        print("\nOn HF Jobs (hf CLI 1.32+; image/flavor/secrets come from the script header):")
+        print("  hf jobs uv run lfm2-extract.py INPUT OUTPUT --schema SCHEMA")
+        print("\nFor full help: uv run --with vllm==0.29.0 lfm2-extract.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(

lift-extract.py

@@ -8,6 +8,10 @@
 #     "toolz",
 #     "tqdm",
 # ]
+#
+# [tool.hf-jobs]
+# flavor = "a100-large"
+# secrets = ["HF_TOKEN"]
 # ///
 """
 Extract structured JSON from document images OR multi-page PDFs using Datalab's
@@ -46,15 +50,19 @@ OpenRAIL-M license — free for research, personal use, and startups under $5M
 funding/revenue, but restricted from competitive use against Datalab's API.
 Confirm you are within those terms before using it. https://huggingface.co/datalab-to/lift
 
-HF Jobs — HF backend (default image is fine; 9B needs a roomy GPU):
+HF Jobs — HF backend. The [tool.hf-jobs] header above sets the flavor
+(a100-large; 9B needs a roomy GPU) and the HF_TOKEN secret, so no flags are
+needed (requires `hf` CLI 1.32+):
 
-    hf jobs uv run --flavor a100-large -s HF_TOKEN \\
+    hf jobs uv run \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lift-extract.py \\
         INPUT_DATASET OUTPUT_DATASET \\
         --schema '{"type":"object","properties":{"title":{"type":"string"}}}' \\
         --max-samples 5 --shuffle --seed 42
 
-HF Jobs — vLLM offline backend (use the vllm image so vLLM is present):
+HF Jobs — vLLM offline backend (use the vllm image so vLLM is present). The
+header does not cover this path: pass the image flags explicitly. This path is
+not tested with the header; treat it as unsupported for now:
 
     hf jobs uv run --flavor a100-large -s HF_TOKEN \\
         --image vllm/vllm-openai --python /usr/bin/python3 \\
@@ -105,7 +113,8 @@ def check_cuda_availability() -> None:
     if not torch.cuda.is_available():
         logger.error("CUDA is not available. This script requires a GPU.")
         logger.error(
-            "Run on Hugging Face Jobs with: hf jobs uv run --flavor a100-large ..."
+            "Run on Hugging Face Jobs with: hf jobs uv run lift-extract.py ... "
+            "(the script header sets flavor a100-large; hf CLI 1.32+)"
         )
         sys.exit(1)
     logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name(0)}")

lighton-ocr2-saturate.py

@@ -125,7 +125,8 @@ def main():
     ap.add_argument("--split", default="train")
     ap.add_argument("--id-column", default=None,
                     help="Column to use as row id (default: split-index ids)")
-    ap.add_argument("--limit", type=int, default=None)
+    ap.add_argument("--max-samples", "--limit", dest="limit", type=int, default=None,
+                    help="Process at most N rows (same name as the other OCR recipes; --limit still works)")
     ap.add_argument("--max-tokens", type=int, default=SERVING["max_tokens"])
     ap.add_argument("--temperature", type=float, default=SERVING["temperature"])
     ap.add_argument("--target-size", type=int, default=SERVING["target_size"])

nuextract3.py

@@ -4,11 +4,16 @@
 #     "datasets>=3.1.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm",
 #     "toolz",
-#     "torch",
 #     "numind",
 # ]
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -36,19 +41,16 @@ Modes are selected via flags:
 schema can be hosted (e.g. on an HF dataset's raw URL) and reused across jobs:
     --template https://huggingface.co/datasets/ORG/REPO/raw/main/card.json
 
-HF Jobs invocation (recommended): use the vllm/vllm-openai:latest image so the
-pre-built CUDA kernels (flashinfer etc.) are reused — the default uv-script
-image lacks nvcc and flashinfer's JIT compile fails at engine warmup.
+HF Jobs invocation: the [tool.hf-jobs] header above sets the vllm/vllm-openai
+image (vLLM + torch come from the image, with pre-built CUDA kernels), the
+flavor and the HF_TOKEN secret. Needs `hf` CLI 1.32+.
 
     hf jobs uv run \\
-        --image vllm/vllm-openai:latest \\
-        --flavor a100-large \\
-        --python /usr/bin/python3 \\
-        -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\
-        -s HF_TOKEN \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \\
         INPUT_DATASET OUTPUT_DATASET --max-samples 5 --shuffle --seed 42
 
+On your own GPU: uv run --with vllm==0.29.0 nuextract3.py INPUT_DATASET OUTPUT_DATASET
+
 Model: numind/NuExtract3
 License: Apache-2.0
 """
@@ -590,28 +592,24 @@ if __name__ == "__main__":
         print("  --template / --schema  - Image -> JSON shaped like the template")
         print("\nExamples:")
         print("\n1. Markdown OCR:")
-        print("   uv run nuextract3.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.29.0 nuextract3.py input-dataset output-dataset")
         print("\n2. Structured extraction with an inline template:")
-        print("   uv run nuextract3.py input output \\")
+        print("   uv run --with vllm==0.29.0 nuextract3.py input output \\")
         print('     --template \'{"title": "verbatim-string", "date": "date"}\'')
         print("\n3. Structured extraction from a JSON Schema (e.g. Pydantic):")
-        print("   uv run nuextract3.py input output --schema schema.json")
+        print("   uv run --with vllm==0.29.0 nuextract3.py input output --schema schema.json")
         print("\n   (--template / --schema also accept a URL or a local file path)")
         print("\n4. Reasoning mode for harder documents:")
-        print("   uv run nuextract3.py input output --enable-thinking")
+        print("   uv run --with vllm==0.29.0 nuextract3.py input output --enable-thinking")
         print("\n5. Test with 10 samples:")
-        print("   uv run nuextract3.py large-ds test --max-samples 10 --shuffle")
-        print("\n6. Running on HF Jobs (use vllm/vllm-openai image for built kernels):")
-        print("   hf jobs uv run --flavor a100-large \\")
-        print("     --image vllm/vllm-openai:latest \\")
-        print("     --python /usr/bin/python3 \\")
-        print("     -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\")
-        print("     -s HF_TOKEN \\")
+        print("   uv run --with vllm==0.29.0 nuextract3.py large-ds test --max-samples 10 --shuffle")
+        print("\n6. Running on HF Jobs (image/flavor/secrets from the script header, hf 1.32+):")
+        print("   hf jobs uv run \\")
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nuextract3.py \\"
         )
         print("       input-dataset output-dataset --batch-size 16")
-        print("\nFor full help: uv run nuextract3.py --help")
+        print("\nFor full help: uv run --with vllm==0.29.0 nuextract3.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -629,11 +627,11 @@ Modes:
                 (e.g. Pydantic Model.model_json_schema())
 
 Examples:
-  uv run nuextract3.py my-docs analyzed-docs
-  uv run nuextract3.py receipts extracted \\
+  uv run --with vllm==0.29.0 nuextract3.py my-docs analyzed-docs
+  uv run --with vllm==0.29.0 nuextract3.py receipts extracted \\
       --template '{"store": "verbatim-string", "total": "number"}'
-  uv run nuextract3.py contracts extracted --schema contract_schema.json
-  uv run nuextract3.py hard-docs out --enable-thinking
+  uv run --with vllm==0.29.0 nuextract3.py contracts extracted --schema contract_schema.json
+  uv run --with vllm==0.29.0 nuextract3.py hard-docs out --enable-thinking
         """,
     )
 

olmocr2-vllm.py

@@ -4,13 +4,17 @@
 #     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm",
 #     "tqdm",
 #     "toolz",
-#     "torch",
 #     "pyyaml",  # For parsing YAML front matter
 # ]
 #
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -30,6 +34,13 @@ Features:
 
 Model: allenai/olmOCR-2-7B-1025-FP8
 Based on: Qwen2.5-VL-7B-Instruct fine-tuned on olmOCR-mix
+
+Run on HF Jobs (the [tool.hf-jobs] header sets image, flavor and secrets;
+needs `hf` CLI 1.32+):
+    hf jobs uv run olmocr2-vllm.py input-dataset output-dataset
+
+Run on your own GPU (vLLM and torch come from the Jobs image, so add vLLM):
+    uv run --with vllm==0.29.0 olmocr2-vllm.py input-dataset output-dataset
 """
 
 import argparse
@@ -265,15 +276,14 @@ Each row contains:
 ## Reproduction
 
 ```bash
-# Using HF Jobs (recommended)
-hf jobs uv run --flavor l4x1 \\
-  -s HF_TOKEN \\
+# Using HF Jobs (recommended; the script header sets image/flavor/secrets, hf CLI 1.32+)
+hf jobs uv run \\
   https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \\
   {source_dataset} \\
   your-username/output-dataset
 
 # Local with GPU
-uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \\
+uv run --with vllm==0.29.0 https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \\
   {source_dataset} \\
   your-username/output-dataset
 ```
@@ -551,32 +561,30 @@ if __name__ == "__main__":
 Examples:
 
 1. Basic OCR on a dataset:
-   uv run olmocr2-vllm.py input-dataset output-dataset
+   uv run --with vllm==0.29.0 olmocr2-vllm.py input-dataset output-dataset
 
 2. Test with first 10 samples:
-   uv run olmocr2-vllm.py input-dataset output-dataset --max-samples 10
+   uv run --with vllm==0.29.0 olmocr2-vllm.py input-dataset output-dataset --max-samples 10
 
 3. Process with custom batch size:
-   uv run olmocr2-vllm.py input-dataset output-dataset --batch-size 8
+   uv run --with vllm==0.29.0 olmocr2-vllm.py input-dataset output-dataset --batch-size 8
 
 4. Custom image column:
-   uv run olmocr2-vllm.py input-dataset output-dataset --image-column page_image
+   uv run --with vllm==0.29.0 olmocr2-vllm.py input-dataset output-dataset --image-column page_image
 
 5. Private output dataset:
-   uv run olmocr2-vllm.py input-dataset output-dataset --private
+   uv run --with vllm==0.29.0 olmocr2-vllm.py input-dataset output-dataset --private
 
 6. Random sampling:
-   uv run olmocr2-vllm.py input-dataset output-dataset --max-samples 100 --shuffle
+   uv run --with vllm==0.29.0 olmocr2-vllm.py input-dataset output-dataset --max-samples 100 --shuffle
 
-7. Running on HuggingFace Jobs:
-   hf jobs uv run --flavor l4x1 \\
-     -s HF_TOKEN \\
+7. Running on HuggingFace Jobs (header sets image/flavor/secrets; hf CLI 1.32+):
+   hf jobs uv run \\
      https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \\
      input-dataset output-dataset
 
 8. Real example with historical documents:
-   hf jobs uv run --flavor l4x1 \\
-     -s HF_TOKEN \\
+   hf jobs uv run --timeout 2h \\
      https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \\
      NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \\
      your-username/handbooks-olmocr \\

ovis-ocr2-saturate.py

@@ -6,7 +6,7 @@
 # ]
 #
 # [tool.hf-jobs]
-# image = "vllm/vllm-openai:latest"
+# image = "vllm/vllm-openai:v0.22.1"
 # flavor = "a10g-small"
 # secrets = ["HF_TOKEN"]
 # ///
@@ -35,6 +35,10 @@ secret (`hf` CLI 1.32+). Pass --timeout for a long run; flags override the heade
       https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2-saturate.py \\
       <input-dataset> <output-dataset>
 
+Smoke test first with --max-samples 3. On your own GPU (vllm on PATH):
+
+  uv run --with vllm==0.22.1 ovis-ocr2-saturate.py <input-dataset> <output-dataset>
+
 Output layout (differs from the -server recipe, which pushes input+markdown):
 the output repo holds `data/part-*.parquet` with rows
 `{id, markdown, model, prompt_tokens, completion_tokens, error}` keyed by the
@@ -78,7 +82,7 @@ import sys
 # Throughput receipt (a10g-small, 20 pages): 4,057 tok/s, window ramped to 32.
 SERVING = {
     "model": "ATH-MaaS/OvisOCR2",
-    "image": "vllm/vllm-openai:latest",
+    "image": "vllm/vllm-openai:v0.22.1",
     "max_model_len": 32768,
     "serve_args": [
         "--limit-mm-per-prompt", '{"image": 1}',
@@ -183,7 +187,8 @@ def main():
     ap.add_argument("--split", default="train")
     ap.add_argument("--id-column", default=None,
                     help="Column to use as row id (default: split-index ids)")
-    ap.add_argument("--limit", type=int, default=None)
+    ap.add_argument("--max-samples", "--limit", dest="limit", type=int, default=None,
+                    help="Process only the first N rows (smoke test)")
     ap.add_argument("--max-tokens", type=int, default=SERVING["max_tokens"])
     ap.add_argument("--keep-image-tags", action="store_true",
                     help="Keep the bbox <img> placeholder blocks in the output")

ovis-ocr2.py

@@ -4,10 +4,15 @@
 #     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm>=0.22.1",
 #     "toolz",
-#     "torch",
 # ]
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -24,6 +29,16 @@ Model: ATH-MaaS/OvisOCR2 (Apache-2.0)
 vLLM: stock Qwen3_5ForConditionalGeneration arch, in stable vLLM >= 0.22.1
       (the version the model card installs); no trust_remote_code needed.
 
+Run on HF Jobs (needs `hf` CLI 1.32+; the [tool.hf-jobs] header above sets the
+vllm/vllm-openai:v0.29.0 image, a10g-small flavor and HF_TOKEN secret):
+
+    hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py \\
+        input-dataset output-dataset --batch-size 16
+
+Run on your own GPU (vLLM and torch come from the image on Jobs, so add them here):
+
+    uv run --with vllm==0.29.0 ovis-ocr2.py input-dataset output-dataset
+
 Features:
 - 0.9B parameters (ultra-compact, runs on l4x1)
 - Markdown output with LaTeX formulas + HTML tables
@@ -525,20 +540,19 @@ if __name__ == "__main__":
         print("  - Visual-region <img> tags filtered by default")
         print("    (--keep-image-tags to retain them)")
         print("\nExamples:")
-        print("\n1. Basic OCR:")
-        print("   uv run ovis-ocr2.py input-dataset output-dataset")
+        print("\n1. Basic OCR (own GPU):")
+        print("   uv run --with vllm==0.29.0 ovis-ocr2.py input-dataset output-dataset")
         print("\n2. Keep visual-region image tags:")
-        print("   uv run ovis-ocr2.py docs results --keep-image-tags")
+        print("   uv run --with vllm==0.29.0 ovis-ocr2.py docs results --keep-image-tags")
         print("\n3. Test with small sample:")
-        print("   uv run ovis-ocr2.py large-dataset test --max-samples 10 --shuffle")
-        print("\n4. Running on HF Jobs:")
-        print("   hf jobs uv run --flavor l4x1 \\")
-        print("     -s HF_TOKEN \\")
+        print("   uv run --with vllm==0.29.0 ovis-ocr2.py large-dataset test --max-samples 10 --shuffle")
+        print("\n4. Running on HF Jobs (hf CLI 1.32+; image/flavor/secrets from the script header):")
+        print("   hf jobs uv run \\")
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/ovis-ocr2.py \\"
         )
         print("       input-dataset output-dataset --batch-size 16")
-        print("\nFor full help: uv run ovis-ocr2.py --help")
+        print("\nFor full help: uv run --with vllm==0.29.0 ovis-ocr2.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -546,9 +560,9 @@ if __name__ == "__main__":
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 Examples:
-  uv run ovis-ocr2.py my-docs analyzed-docs
-  uv run ovis-ocr2.py docs results --keep-image-tags
-  uv run ovis-ocr2.py large-dataset test --max-samples 50 --shuffle
+  uv run --with vllm==0.29.0 ovis-ocr2.py my-docs analyzed-docs
+  uv run --with vllm==0.29.0 ovis-ocr2.py docs results --keep-image-tags
+  uv run --with vllm==0.29.0 ovis-ocr2.py large-dataset test --max-samples 50 --shuffle
         """,
     )
 

paddleocr-vl-1.6.py

@@ -4,13 +4,17 @@
 #     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm>=0.15.1",
 #     "tqdm",
 #     "toolz",
-#     "torch",
 #     "pyarrow",
-#     "transformers",
 # ]
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -34,14 +38,12 @@ Features:
 Model: PaddlePaddle/PaddleOCR-VL-1.6
 Backend: vLLM offline (batch inference)
 
-HF Jobs note: PaddleOCR-VL-1.6 is supported by stable vLLM, but on HF Jobs you must run
-with the pre-built vLLM image so flashinfer's CUDA kernels are reused. The default
-uv-script image has the CUDA runtime but no `nvcc`, so vLLM's flashinfer sampler crashes
-at warmup with "Could not find nvcc". Use image-mode (see the example at the bottom):
-    --image vllm/vllm-openai:latest --flavor a100-large
-    --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages
-This is the same image-mode pattern as nuextract3.py. Verified end-to-end on a100-large
-(2026-06-01): 5/5 clean markdown on davanstrien/ufo-ColPali, ~194 tok/s, 0 errors.
+HF Jobs: the [tool.hf-jobs] header above pins the image (vllm/vllm-openai:v0.29.0),
+python, PYTHONPATH, flavor (a10g-small) and HF_TOKEN secret, so no launch flags are
+needed (requires `hf` CLI 1.32+):
+    hf jobs uv run paddleocr-vl-1.6.py input-dataset output-dataset
+In image mode the image's vLLM/torch win over the venv copies via PYTHONPATH.
+On your own GPU: uv run --with vllm==0.29.0 paddleocr-vl-1.6.py input-dataset output-dataset
 """
 
 import argparse
@@ -338,13 +340,10 @@ for info in inference_info:
 ## Reproduction
 
 This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) PaddleOCR-VL-1.6 script.
-On HF Jobs, run with the pre-built vLLM image (image-mode) so flashinfer kernels are reused:
+On HF Jobs (`hf` CLI 1.32+; the script's [tool.hf-jobs] header sets image, flavor and secrets):
 
 ```bash
 hf jobs uv run \\
-    --image vllm/vllm-openai:latest --flavor a100-large \\
-    --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\
-    -s HF_TOKEN \\
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.6.py \\
     {source_dataset} \\
     <output-dataset> \\
@@ -459,12 +458,8 @@ def main(
     except Exception as e:
         logger.error(f"Failed to initialize PaddleOCR-VL-1.6 with vLLM: {e}")
         logger.error(
-            "On HF Jobs, run with the pre-built vLLM image so flashinfer kernels are "
-            "reused (the default uv-script image has no nvcc):"
-        )
-        logger.error(
-            "  --image vllm/vllm-openai:latest --flavor a100-large "
-            "--python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages"
+            "On HF Jobs, use `hf` CLI 1.32+ so the script's [tool.hf-jobs] header "
+            "(vllm/vllm-openai image) applies; the default uv-script image has no nvcc."
         )
         sys.exit(1)
 
@@ -649,32 +644,26 @@ if __name__ == "__main__":
             print(f"  {mode:8} - {description}")
         print("\nExample usage:")
         print("\n1. Basic OCR (default mode):")
-        print("   uv run paddleocr-vl-1.6.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.29.0 paddleocr-vl-1.6.py input-dataset output-dataset")
         print("\n2. Table extraction:")
-        print("   uv run paddleocr-vl-1.6.py docs tables-extracted --task-mode table")
+        print("   uv run --with vllm==0.29.0 paddleocr-vl-1.6.py docs tables-extracted --task-mode table")
         print("\n3. Formula recognition:")
         print(
-            "   uv run paddleocr-vl-1.6.py papers formulas --task-mode formula --batch-size 32"
+            "   uv run --with vllm==0.29.0 paddleocr-vl-1.6.py papers formulas --task-mode formula --batch-size 32"
         )
         print("\n4. Chart analysis:")
-        print("   uv run paddleocr-vl-1.6.py diagrams charts-analyzed --task-mode chart")
+        print("   uv run --with vllm==0.29.0 paddleocr-vl-1.6.py diagrams charts-analyzed --task-mode chart")
         print("\n5. Test with small sample:")
-        print("   uv run paddleocr-vl-1.6.py dataset test --max-samples 10 --shuffle")
-        print("\n6. Running on HF Jobs (image-mode required — see note below):")
+        print("   uv run --with vllm==0.29.0 paddleocr-vl-1.6.py dataset test --max-samples 10 --shuffle")
+        print("\n6. Running on HF Jobs (hf CLI 1.32+; header sets image/flavor/secrets):")
         print("   hf jobs uv run \\")
-        print("     --image vllm/vllm-openai:latest --flavor a100-large \\")
-        print(
-            "     --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\"
-        )
-        print("     -s HF_TOKEN \\")
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.6.py \\"
         )
         print("       input-dataset output-dataset --task-mode ocr")
-        print("\n   NOTE: the default uv-script image has no nvcc, so vLLM's flashinfer")
-        print("   sampler crashes at warmup. The vllm/vllm-openai image ships the kernels.")
+        print("\n   NOTE: the [tool.hf-jobs] header pins vllm/vllm-openai:v0.29.0 on a10g-small.")
         print("\n" + "=" * 80)
-        print("\nFor full help, run: uv run paddleocr-vl-1.6.py --help")
+        print("\nFor full help, run: uv run --with vllm==0.29.0 paddleocr-vl-1.6.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -691,22 +680,22 @@ Task Modes:
 
 Examples:
   # Basic text OCR
-  uv run paddleocr-vl-1.6.py my-docs analyzed-docs
+  uv run --with vllm==0.29.0 paddleocr-vl-1.6.py my-docs analyzed-docs
 
   # Extract tables from documents
-  uv run paddleocr-vl-1.6.py papers tables --task-mode table
+  uv run --with vllm==0.29.0 paddleocr-vl-1.6.py papers tables --task-mode table
 
   # Recognize mathematical formulas
-  uv run paddleocr-vl-1.6.py textbooks formulas --task-mode formula
+  uv run --with vllm==0.29.0 paddleocr-vl-1.6.py textbooks formulas --task-mode formula
 
   # Analyze charts and diagrams
-  uv run paddleocr-vl-1.6.py reports charts --task-mode chart
+  uv run --with vllm==0.29.0 paddleocr-vl-1.6.py reports charts --task-mode chart
 
   # Test with random sampling
-  uv run paddleocr-vl-1.6.py large-dataset test --max-samples 50 --shuffle --task-mode ocr
+  uv run --with vllm==0.29.0 paddleocr-vl-1.6.py large-dataset test --max-samples 50 --shuffle --task-mode ocr
 
   # Disable smart resize for original resolution
-  uv run paddleocr-vl-1.6.py images output --no-smart-resize
+  uv run --with vllm==0.29.0 paddleocr-vl-1.6.py images output --no-smart-resize
         """,
     )
 

pp-doclayout.py

@@ -28,6 +28,10 @@
 #
 # [tool.uv.sources]
 # paddlepaddle-gpu = { index = "paddle" }
+#
+# [tool.hf-jobs]
+# flavor = "t4-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -51,22 +55,22 @@ Output schema (column `layout` is a JSON string):
 
 Coordinates are in the original input-image pixel space.
 
-Example commands:
+Example commands (the [tool.hf-jobs] header sets flavor + secrets; needs `hf` CLI 1.32+):
 
-  # Dataset -> dataset (smoke on L4)
-  hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\
+  # Dataset -> dataset (smoke test)
+  hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\
       davanstrien/ufo-ColPali pp-doclayout-smoke \\
       --max-samples 3 --shuffle --seed 42 --private
 
   # Dataset -> bucket (incremental shards, resumable)
   hf buckets create davanstrien/pp-doclayout-scratch --exist-ok
-  hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\
+  hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\
       davanstrien/ufo-ColPali \\
       hf://buckets/davanstrien/pp-doclayout-scratch/run1 \\
       --max-samples 20 --shard-size 5
 
   # Bucket of images -> dataset
-  hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\
+  hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\
       hf://buckets/davanstrien/pp-doclayout-images \\
       pp-doclayout-from-bucket --private
 """
@@ -840,7 +844,7 @@ for det in detections:
 ## Reproduction
 
 ```bash
-hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
+hf jobs uv run \\
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\
     {source} <output> --model-name {model_name}
 ```
@@ -1078,13 +1082,13 @@ def _print_usage_banner() -> None:
     print("  - HF bucket (incremental shards, resumable):")
     print("      hf://buckets/namespace/bucket/run-name")
     print("\nExamples:")
-    print("\n  # Smoke test on L4 (dataset -> dataset)")
-    print("  hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\")
+    print("\n  # Smoke test (dataset -> dataset; header needs hf CLI 1.32+)")
+    print("  hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\")
     print("      davanstrien/ufo-ColPali pp-doclayout-smoke \\")
     print("      --max-samples 3 --shuffle --seed 42 --private")
     print("\n  # Dataset -> bucket (incremental shards)")
     print(
-        "  hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\"
+        "  hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\"
     )
     print("      davanstrien/ufo-ColPali \\")
     print(
@@ -1093,7 +1097,7 @@ def _print_usage_banner() -> None:
     print("      --max-samples 20 --shard-size 5")
     print("\n  # Bucket of images -> dataset")
     print(
-        "  hf jobs uv run --flavor l4x1 -s HF_TOKEN https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\"
+        "  hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-doclayout.py \\"
     )
     print(
         "      hf://buckets/davanstrien/pp-doclayout-images \\"

pp-ocrv6.py

@@ -29,6 +29,10 @@
 #
 # [tool.uv.sources]
 # paddlepaddle-gpu = { index = "paddle" }
+#
+# [tool.hf-jobs]
+# flavor = "t4-small"
+# secrets = ["HF_TOKEN"]
 # ///
 """
 OCR images with PP-OCRv6 — a lightweight detection+recognition pipeline from
@@ -46,16 +50,17 @@ Model tiers (pick with `--model-tier`):
 All tiers are Apache 2.0 licensed. Runs via PaddleOCR's default Paddle engine
 (`paddle_static`) — same proven header pattern as `pp-doclayout.py`.
 
-HF Jobs examples:
+HF Jobs examples (flavor and secrets come from the [tool.hf-jobs] header,
+which needs `hf` CLI 1.32+):
 
     # Tiny on a cheap GPU
-    hf jobs uv run --flavor t4-small -s HF_TOKEN \\
+    hf jobs uv run \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-ocrv6.py \\
         INPUT_DATASET OUTPUT_DATASET \\
         --model-tier tiny --max-samples 5
 
     # Medium on a small GPU (recommended for quality)
-    hf jobs uv run --flavor t4-small -s HF_TOKEN \\
+    hf jobs uv run \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-ocrv6.py \\
         INPUT_DATASET OUTPUT_DATASET \\
         --model-tier medium --max-samples 10
@@ -795,7 +800,7 @@ for block in json.loads(ds[0]["pp_ocr_blocks"]):
 ## Reproduction
 
 ```bash
-hf jobs uv run --flavor t4-small -s HF_TOKEN \\
+hf jobs uv run \\
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/pp-ocrv6.py \\
     {source} <output> --model-tier {tier}
 ```

qianfan-ocr.py

@@ -4,11 +4,16 @@
 #     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm>=0.15.1",
 #     "tqdm",
 #     "toolz",
-#     "torch",
 # ]
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.29.0"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -28,6 +33,16 @@ Features:
 Model: baidu/Qianfan-OCR
 License: Apache 2.0
 Paper: https://arxiv.org/abs/2603.13398
+
+Run on HF Jobs (the [tool.hf-jobs] header sets image, flavor and secrets;
+needs `hf` CLI 1.32+):
+
+    hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \
+        input-dataset output-dataset --max-samples 10
+
+Run on your own GPU:
+
+    uv run --with vllm==0.29.0 qianfan-ocr.py input-dataset output-dataset
 """
 
 import argparse
@@ -484,25 +499,26 @@ if __name__ == "__main__":
         print("- Key information extraction with custom prompts")
         print("\nExample usage:")
         print("\n1. Basic OCR:")
-        print("   uv run qianfan-ocr.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.29.0 qianfan-ocr.py input-dataset output-dataset")
         print("\n2. With Layout-as-Thought (complex documents):")
-        print("   uv run qianfan-ocr.py docs output --think")
+        print("   uv run --with vllm==0.29.0 qianfan-ocr.py docs output --think")
         print("\n3. Table extraction:")
-        print("   uv run qianfan-ocr.py docs output --prompt-mode table")
+        print("   uv run --with vllm==0.29.0 qianfan-ocr.py docs output --prompt-mode table")
         print("\n4. Formula extraction:")
-        print("   uv run qianfan-ocr.py docs output --prompt-mode formula")
+        print("   uv run --with vllm==0.29.0 qianfan-ocr.py docs output --prompt-mode formula")
         print("\n5. Key information extraction:")
         print(
-            '   uv run qianfan-ocr.py invoices output --prompt-mode kie --custom-prompt "Extract: name, date, total. Output JSON."'
+            '   uv run --with vllm==0.29.0 qianfan-ocr.py invoices output --prompt-mode kie --custom-prompt "Extract: name, date, total. Output JSON."'
         )
-        print("\n6. Running on HF Jobs:")
-        print("   hf jobs uv run --flavor l4x1 \\")
-        print("     -s HF_TOKEN \\")
+        print("\n6. Running on HF Jobs (header sets image/flavor/secrets; hf CLI 1.32+):")
+        print("   hf jobs uv run \\")
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/qianfan-ocr.py \\"
         )
         print("       input-dataset output-dataset --max-samples 10")
-        print("\nFor full help, run: uv run qianfan-ocr.py --help")
+        print("\n7. Running on your own GPU:")
+        print("   uv run --with vllm==0.29.0 qianfan-ocr.py input-dataset output-dataset")
+        print("\nFor full help, run: uv run --with vllm==0.29.0 qianfan-ocr.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -518,10 +534,10 @@ Prompt modes:
   kie       Key information extraction (requires --custom-prompt)
 
 Examples:
-  uv run qianfan-ocr.py my-docs analyzed-docs
-  uv run qianfan-ocr.py docs output --think --max-samples 50
-  uv run qianfan-ocr.py docs output --prompt-mode table
-  uv run qianfan-ocr.py invoices data --prompt-mode kie --custom-prompt "Extract: name, date, total."
+  uv run --with vllm==0.29.0 qianfan-ocr.py my-docs analyzed-docs
+  uv run --with vllm==0.29.0 qianfan-ocr.py docs output --think --max-samples 50
+  uv run --with vllm==0.29.0 qianfan-ocr.py docs output --prompt-mode table
+  uv run --with vllm==0.29.0 qianfan-ocr.py invoices data --prompt-mode kie --custom-prompt "Extract: name, date, total."
         """,
     )
 

surya-ocr-bucket.py

@@ -14,6 +14,13 @@
 # # this recipe injects into); an unpinned/loosened resolve backtracks to an ancient
 # # surya without it. huggingface-hub is left unpinned: at runtime PYTHONPATH puts the
 # # pinned image's hub (with the buckets API) ahead of the venv, so no version tension.
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.20.1"
+# python = "/usr/local/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/site-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 """
 Structured OCR over a **bucket of document files** (images + PDFs) with Datalab's
@@ -55,27 +62,27 @@ OpenRAIL-M license — free for research, personal use, and startups under $5M
 funding/revenue, restricted from competitive use against Datalab's API. Confirm you
 are within those terms. https://huggingface.co/datalab-to/surya-ocr-2
 
-HF Jobs — MUST use the pinned vLLM image + the site-packages python path (the model
-is the recent, version-sensitive `qwen3_5` architecture; v0.20.1 is Surya's
-known-good build, and it puts python/vLLM under /usr/local, NOT /usr/bin):
+HF Jobs — the `[tool.hf-jobs]` header above pins the vLLM image + the site-packages
+python path (the model is the recent, version-sensitive `qwen3_5` architecture;
+v0.20.1 is Surya's known-good build, and it puts python/vLLM under /usr/local, NOT
+/usr/bin), plus flavor and HF_TOKEN. The header needs `hf` CLI 1.32+; CLI flags
+override it:
 
     # copy input -> dataset output
-    hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
-        --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 \\
-        -e PYTHONPATH=/usr/local/lib/python3.12/site-packages \\
+    hf jobs uv run \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr-bucket.py \\
         hf://buckets/<ns>/<bucket> --io-mode copy --glob "*.jp2" \\
         --output-dataset <ns>/<out> --private
 
     # mount input -> per-file bucket output (mirrors dir structure)
-    hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
-        --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 \\
-        -e PYTHONPATH=/usr/local/lib/python3.12/site-packages \\
+    hf jobs uv run \\
         -v hf://buckets/<ns>/<bucket>:/in:ro \\
         -v hf://buckets/<ns>/<out-bucket>:/out \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr-bucket.py \\
         /in --io-mode mount --glob "*.jp2" --output-bucket /out
 
+On your own GPU (no image): `uv run --with vllm==0.20.1 surya-ocr-bucket.py ...`
+
 Model: datalab-to/surya-ocr-2  (package: surya-ocr, https://github.com/datalab-to/surya)
 """
 
@@ -127,8 +134,8 @@ def check_cuda_availability() -> None:
     if not torch.cuda.is_available():
         logger.error("CUDA is not available. This script requires a GPU.")
         logger.error(
-            "Run on Hugging Face Jobs with: hf jobs uv run --flavor l4x1 "
-            "--image vllm/vllm-openai:v0.20.1 ..."
+            "Run on Hugging Face Jobs with: hf jobs uv run surya-ocr-bucket.py ... "
+            "(the script's [tool.hf-jobs] header sets image + GPU; hf CLI 1.32+)"
         )
         sys.exit(1)
     logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name(0)}")
@@ -1211,10 +1218,10 @@ Outputs (at least one required):
                     hf://buckets/... URL); resumable, O(1) memory
   --output-dataset  parquet dataset push (one row per file)
 
-Run on the vllm/vllm-openai:v0.20.1 image (offline vLLM batch; qwen3_5 is
-version-sensitive — the site-packages python path is load-bearing):
-  --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 \\
-    -e PYTHONPATH=/usr/local/lib/python3.12/site-packages
+HF Jobs: the script's [tool.hf-jobs] header pins vllm/vllm-openai:v0.20.1 + the
+site-packages python path (qwen3_5 is version-sensitive), flavor and HF_TOKEN, so
+`hf jobs uv run surya-ocr-bucket.py ...` needs no flags (hf CLI 1.32+).
+Own GPU: uv run --with vllm==0.20.1 surya-ocr-bucket.py ...
 """,
     )
     parser.add_argument(
@@ -1274,7 +1281,9 @@ version-sensitive — the site-packages python path is load-bearing):
         help="DPI for PDF rendering (default: Surya's IMAGE_DPI_HIGHRES)",
     )
     parser.add_argument(
-        "--max-samples", type=int, help="Limit number of files (for testing)"
+        "--max-samples",
+        type=int,
+        help="Limit number of input files, not pages (a PDF counts once)",
     )
     parser.add_argument(
         "--shuffle", action="store_true", help="Shuffle before sampling"
@@ -1359,10 +1368,8 @@ def _print_banner() -> None:
     print("      --glob '*.jp2' --output-dataset me/news-ocr --private")
     print("\n  # mount a bucket -> per-file .md + .json in an output bucket")
     print("  uv run surya-ocr-bucket.py /in --io-mode mount --output-bucket /out")
-    print("\nRun on the vllm/vllm-openai:v0.20.1 image (offline vLLM batch):")
-    print("  hf jobs uv run --flavor l4x1 -s HF_TOKEN \\")
-    print("      --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 \\")
-    print("      -e PYTHONPATH=/usr/local/lib/python3.12/site-packages \\")
+    print("\nHF Jobs (the [tool.hf-jobs] header sets image + GPU; hf CLI 1.32+):")
+    print("  hf jobs uv run \\")
     print("      -v hf://buckets/me/news:/in:ro -v hf://buckets/me/news-ocr:/out \\")
     print(
         "      https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr-bucket.py \\"

surya-ocr.py

@@ -8,6 +8,13 @@
 #     "toolz",
 #     "tqdm",
 # ]
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:v0.20.1"
+# python = "/usr/local/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/site-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 """
 Document intelligence on images OR multi-page PDFs with Datalab's **Surya OCR 2**
@@ -49,15 +56,16 @@ OpenRAIL-M license — free for research, personal use, and startups under $5M
 funding/revenue, but restricted from competitive use against Datalab's API.
 Confirm you are within those terms. https://huggingface.co/datalab-to/surya-ocr-2
 
-HF Jobs (use the pinned vLLM image so vLLM + qwen3_5 support are present):
+HF Jobs (the [tool.hf-jobs] header pins the vLLM image, interpreter, PYTHONPATH,
+flavor and HF_TOKEN secret; needs `hf` CLI 1.32+):
 
-    hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
-        --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 \\
-        -e PYTHONPATH=/usr/local/lib/python3.12/site-packages \\
+    hf jobs uv run \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py \\
         INPUT_DATASET OUTPUT_DATASET \\
         --max-samples 5 --shuffle --seed 42
 
+On your own GPU: uv run --with vllm==0.20.1 surya-ocr.py INPUT_DATASET OUTPUT_DATASET ...
+
 Model: datalab-to/surya-ocr-2  (package: surya-ocr, https://github.com/datalab-to/surya)
 """
 
@@ -97,8 +105,8 @@ def check_cuda_availability() -> None:
     if not torch.cuda.is_available():
         logger.error("CUDA is not available. This script requires a GPU.")
         logger.error(
-            "Run on Hugging Face Jobs with: hf jobs uv run --flavor l4x1 "
-            "--image vllm/vllm-openai:v0.20.1 ..."
+            "Run on Hugging Face Jobs with: hf jobs uv run <script> ... "
+            "(the script's [tool.hf-jobs] header sets the GPU + vLLM image; hf CLI 1.32+)"
         )
         sys.exit(1)
     logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name(0)}")
@@ -118,13 +126,14 @@ def check_vllm_available() -> None:
     if importlib.util.find_spec("vllm") is None:
         logger.error("vLLM is not importable — this recipe cannot run on the bare uv image.")
         logger.error(
-            "Surya-2 needs the pinned vLLM build; re-run with the image + interpreter flags:"
+            "Surya-2 needs the pinned vLLM build. With hf CLI 1.32+ the script's "
+            "[tool.hf-jobs] header applies the image + interpreter automatically:"
         )
+        logger.error("  hf jobs uv run <script_url> INPUT_DATASET OUTPUT_DATASET ...")
         logger.error(
-            "  hf jobs uv run --flavor l4x1 -s HF_TOKEN \\\n"
-            "      --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 \\\n"
-            "      -e PYTHONPATH=/usr/local/lib/python3.12/site-packages \\\n"
-            "      <script_url> INPUT_DATASET OUTPUT_DATASET ..."
+            "Older hf CLIs: add --flavor a10g-small -s HF_TOKEN --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 "
+            "-e PYTHONPATH=/usr/local/lib/python3.12/site-packages. "
+            "On your own GPU: uv run --with vllm==0.20.1 <script> ..."
         )
         sys.exit(1)
 
@@ -775,9 +784,10 @@ Input (one document per row):
   --image-column COL   one image per row   (default: image)
   --pdf-column COL     PDF bytes per row    (multi-page; honors --page-range)
 
-Run on the vllm/vllm-openai:v0.20.1 image:
-  --image vllm/vllm-openai:v0.20.1 --python /usr/local/bin/python3 \\
-    -e PYTHONPATH=/usr/local/lib/python3.12/site-packages
+HF Jobs (hf CLI 1.32+; the [tool.hf-jobs] header sets image/flavor/secrets):
+  hf jobs uv run surya-ocr.py INPUT_DATASET OUTPUT_DATASET --max-samples 5
+Own GPU:
+  uv run --with vllm==0.20.1 surya-ocr.py INPUT_DATASET OUTPUT_DATASET
 """,
     )
     parser.add_argument(

tesseract-ocr.py

@@ -7,6 +7,10 @@
 #     "pillow",
 #     "tqdm",
 # ]
+#
+# [tool.hf-jobs]
+# flavor = "cpu-upgrade"
+# secrets = ["HF_TOKEN"]
 # ///
 """
 Plain-text OCR with **Tesseract** — the classical CPU OCR engine, as a baseline.
@@ -26,9 +30,10 @@ root). For non-English languages it also tries to install the matching
 `tesseract-ocr-<lang>` data pack. If your Jobs image blocks apt, bake Tesseract
 into a custom `--image` instead.
 
-HF Jobs (CPU — no GPU needed):
+HF Jobs (CPU — no GPU needed). The script header sets the flavor
+(`cpu-upgrade`) and the HF_TOKEN secret; this needs `hf` CLI 1.32+:
 
-    hf jobs uv run --flavor cpu-upgrade -s HF_TOKEN \\
+    hf jobs uv run \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/tesseract-ocr.py \\
         input-dataset output-dataset \\
         --max-samples 100 --shuffle
@@ -512,8 +517,8 @@ if __name__ == "__main__":
         print("\n3. Test with a small sample, no push:")
         print("   uv run tesseract-ocr.py large-dataset out --max-samples 5 --dry-run")
         print("\n4. Running on HF Jobs (CPU flavor):")
-        print("   hf jobs uv run --flavor cpu-upgrade \\")
-        print("     -s HF_TOKEN \\")
+        print("   # header sets flavor + HF_TOKEN secret (hf CLI 1.32+)")
+        print("   hf jobs uv run \\")
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/tesseract-ocr.py \\"
         )

unlimited-ocr-vllm.py

@@ -7,6 +7,13 @@
 #     "tqdm",
 #     "toolz",
 # ]
+#
+# [tool.hf-jobs]
+# image = "vllm/vllm-openai:unlimited-ocr"
+# python = "/usr/bin/python3"
+# env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
+# flavor = "a10g-small"
+# secrets = ["HF_TOKEN"]
 # ///
 
 """
@@ -30,15 +37,16 @@ hallucination in our tests, where the model's own SGLang build held up better
 robust multi-page path. (vLLM's upstream PR, vllm-project/vllm#46564, benchmarks single-page only.)
 
 IMPORTANT: Unlimited-OCR's architecture is not in a stable vLLM pip wheel, so this script MUST run on
-Baidu's dedicated vLLM image (vllm and torch come from the image, not the PEP 723 deps):
+Baidu's dedicated vLLM image (vllm and torch come from the image, not the PEP 723 deps). The
+[tool.hf-jobs] header above sets the image, python, PYTHONPATH, flavor and HF_TOKEN secret, so the
+run command needs no flags (the header needs `hf` CLI 1.32+):
 
-    hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
-        --image vllm/vllm-openai:unlimited-ocr --python /usr/bin/python3 \\
-        -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\
+    hf jobs uv run \\
         https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \\
         your-input-dataset your-output-dataset --max-samples 10
 
-Use the vllm/vllm-openai:unlimited-ocr-cu129 tag on Hopper GPUs (h100/h200).
+On Hopper GPUs (h100/h200) add --image vllm/vllm-openai:unlimited-ocr-cu129 (a CLI flag overrides
+the header). On your own GPU, run inside that image: no stable vLLM wheel has this architecture yet.
 
 Model card: https://huggingface.co/baidu/Unlimited-OCR
 vLLM recipe: https://recipes.vllm.ai/baidu/Unlimited-OCR
@@ -197,12 +205,11 @@ print(ds[0]["{output_column}"])
 ## Reproduction
 
 Generated with the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) Unlimited-OCR
-vLLM recipe. Unlimited-OCR needs Baidu's dedicated vLLM image:
+vLLM recipe. Unlimited-OCR needs Baidu's dedicated vLLM image, which the script's `[tool.hf-jobs]` header
+sets (`hf` CLI 1.32+):
 
 ```bash
-hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
-    --image vllm/vllm-openai:unlimited-ocr --python /usr/bin/python3 \\
-    -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\
+hf jobs uv run \\
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \\
     {source_dataset} <output-dataset>
 ```
@@ -434,13 +441,10 @@ if __name__ == "__main__":
         print("=" * 80)
         print("\nBaidu Unlimited-OCR (3.3B, MIT) — one image per row -> markdown.")
         print("\nMUST run on the dedicated image: vllm/vllm-openai:unlimited-ocr")
-        print("(use the -cu129 tag on Hopper GPUs).")
+        print("(set by the script's [tool.hf-jobs] header, hf CLI 1.32+;")
+        print("on Hopper GPUs add --image vllm/vllm-openai:unlimited-ocr-cu129).")
         print("\nExample:")
-        print("   hf jobs uv run --flavor l4x1 -s HF_TOKEN \\")
-        print(
-            "     --image vllm/vllm-openai:unlimited-ocr --python /usr/bin/python3 \\"
-        )
-        print("     -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\")
+        print("   hf jobs uv run \\")
         print("     unlimited-ocr-vllm.py my-images my-markdown --max-samples 10")
         print(
             "\nMulti-page documents: serve the model instead (see serving-unlimited-ocr.md)."
@@ -459,10 +463,8 @@ Examples:
   # Clean text (strip grounding tags)
   uv run unlimited-ocr-vllm.py my-images ocr-results --strip-grounding
 
-  # On HF Jobs (dedicated image required)
-  hf jobs uv run --flavor l4x1 -s HF_TOKEN \\
-      --image vllm/vllm-openai:unlimited-ocr --python /usr/bin/python3 \\
-      -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\
+  # On HF Jobs (image, flavor and secret come from the [tool.hf-jobs] header; hf CLI 1.32+)
+  hf jobs uv run \\
       https://huggingface.co/datasets/uv-scripts/ocr/raw/main/unlimited-ocr-vllm.py \\
       my-dataset my-output --max-samples 10
         """,