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README.md

@@ -24,7 +24,7 @@ hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.p
 
 The Job adds a `markdown` column to all seven rows and saves them in `your-username/ocr-demo-results`. Dependency installation and model loading can take a few minutes before OCR starts. The [dataset card](https://huggingface.co/datasets/uv-scripts/ocr-demo) gives the source and licence.
 
-> **Note:** the command needs no flags because the script's [`[tool.hf-jobs]` header](https://huggingface.co/docs/hub/jobs-configuration#define-the-launch-config-in-the-script) sets the GPU, the Docker image and the `HF_TOKEN` secret. The `hf` CLI reads the header from version 1.32. `hf jobs uv run --dry-run <script>` shows the resolved settings. Flags override the header, for example `--timeout 1h` for a larger dataset or `--flavor` for other hardware. **Older CLIs ignore the header without a warning**, and the Job then fails on a CPU without your token. Check with `hf version` and upgrade. If you cannot upgrade, copy the header values from the top of the script (or from `jobs` in [`models.json`](models.json)) as flags. For `glm-ocr.py` that is `--flavor a10g-small --secrets HF_TOKEN --image vllm/vllm-openai:v0.29.0 --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages`.
+> **Note:** the command needs no flags because the script's [`[tool.hf-jobs]` header](https://huggingface.co/docs/hub/jobs-configuration#define-the-launch-config-in-the-script) sets the GPU, the Docker image and the `HF_TOKEN` secret. The `hf` CLI reads the header from version 1.32. `hf jobs uv run --dry-run <script>` shows the resolved settings. Flags override the header, for example `--timeout 1h` for a larger dataset or `--flavor` for other hardware. **Older CLIs ignore the header without a warning**, and the Job then fails on a CPU without your token. Check with `hf version` and upgrade. If you cannot upgrade, copy the header values from the top of the script (or from `jobs` in [`models.json`](models.json)) as flags. For `glm-ocr.py` that is `--flavor a10g-small --secrets HF_TOKEN --image vllm/vllm-openai:v0.30.0 --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages`.
 
 ### Try the same pages as a PDF
 
@@ -259,7 +259,7 @@ job = run_uv_job(
     "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py",
     script_args=["input-dataset", "output-dataset", "--max-samples", "10"],
     flavor="a10g-small",
-    image="vllm/vllm-openai:v0.29.0",
+    image="vllm/vllm-openai:v0.30.0",
     python="/usr/bin/python3",
     env={"PYTHONPATH": "/usr/local/lib/python3.12/dist-packages"},
     secrets={"HF_TOKEN": get_token()},
@@ -280,7 +280,7 @@ nvrtc: error: failed to open libnvrtc-builtins.so...
 The Job ran on the default image, which has no CUDA toolkit. This happens with an older CLI, with a legacy recipe, or when you override `--image`. Run it on the `vllm/vllm-openai` image:
 
 ```bash
---image vllm/vllm-openai:v0.29.0 --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages
+--image vllm/vllm-openai:v0.30.0 --python /usr/bin/python3 -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages
 ```
 
 Use the tag from the script's header. The Surya recipes use `/usr/local/bin/python3` and `site-packages` instead; copy their header exactly. `unlimited-ocr-vllm.py` needs Baidu's `vllm/vllm-openai:unlimited-ocr` image (`:unlimited-ocr-cu129` on H100 or H200).

dots-mocr.py

@@ -6,15 +6,12 @@
 #     "pillow",
 #     "tqdm",
 #     "toolz",
-#     # 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"
+# # >=v0.30.0: ships huggingface_hub>=1.31, which datasets>=5.1 imports (httpx). On v0.29.0,
+# # PYTHONPATH puts the image's older hub first and `import datasets` crashes.
+# image = "vllm/vllm-openai:v0.30.0"
 # python = "/usr/bin/python3"
 # env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
 # flavor = "a10g-small"
@@ -52,7 +49,7 @@ needs the hf CLI 1.32+):
 
 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
+    uv run --with vllm==0.30.0 dots-mocr.py input-dataset output-dataset
 """
 
 import argparse
@@ -581,15 +578,15 @@ if __name__ == "__main__":
         print("  general        - Free-form (use with --custom-prompt)")
         print("\nExample usage:")
         print("\n1. Basic OCR:")
-        print("   uv run --with vllm==0.29.0 dots-mocr.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.30.0 dots-mocr.py input-dataset output-dataset")
         print("\n2. SVG generation:")
         print(
-            "   uv run --with vllm==0.29.0 dots-mocr.py charts svg-output --prompt-mode svg --model rednote-hilab/dots.mocr-svg"
+            "   uv run --with vllm==0.30.0 dots-mocr.py charts svg-output --prompt-mode svg --model rednote-hilab/dots.mocr-svg"
         )
         print("\n3. Web screen parsing:")
-        print("   uv run --with vllm==0.29.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing")
+        print("   uv run --with vllm==0.30.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing")
         print("\n4. Layout analysis with structure:")
-        print("   uv run --with vllm==0.29.0 dots-mocr.py papers analyzed --prompt-mode layout-all")
+        print("   uv run --with vllm==0.30.0 dots-mocr.py papers analyzed --prompt-mode layout-all")
         print("\n5. Running on HF Jobs:")
         print("   # [tool.hf-jobs] header sets image/flavor/secrets (hf CLI 1.32+)")
         print("   hf jobs uv run \\")
@@ -598,7 +595,7 @@ if __name__ == "__main__":
         )
         print("       input-dataset output-dataset")
         print("\n" + "=" * 80)
-        print("\nFor full help, run: uv run --with vllm==0.29.0 dots-mocr.py --help")
+        print("\nFor full help, run: uv run --with vllm==0.30.0 dots-mocr.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -622,19 +619,19 @@ SVG Code Generation:
 
 Examples:
   # Basic text OCR (default)
-  uv run --with vllm==0.29.0 dots-mocr.py my-docs analyzed-docs
+  uv run --with vllm==0.30.0 dots-mocr.py my-docs analyzed-docs
 
   # SVG generation with optimized variant
-  uv run --with vllm==0.29.0 dots-mocr.py charts svg-out --prompt-mode svg --model rednote-hilab/dots.mocr-svg
+  uv run --with vllm==0.30.0 dots-mocr.py charts svg-out --prompt-mode svg --model rednote-hilab/dots.mocr-svg
 
   # Web screen parsing
-  uv run --with vllm==0.29.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing
+  uv run --with vllm==0.30.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing
 
   # Full layout analysis with structure
-  uv run --with vllm==0.29.0 dots-mocr.py papers structured --prompt-mode layout-all
+  uv run --with vllm==0.30.0 dots-mocr.py papers structured --prompt-mode layout-all
 
   # Random sampling for testing
-  uv run --with vllm==0.29.0 dots-mocr.py large-dataset test --max-samples 50 --shuffle
+  uv run --with vllm==0.30.0 dots-mocr.py large-dataset test --max-samples 50 --shuffle
         """,
     )
 

dots-ocr.py

@@ -6,16 +6,12 @@
 #     "pillow",
 #     "tqdm",
 #     "toolz",
-#     # 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"
+# # >=v0.30.0: ships huggingface_hub>=1.31, which datasets>=5.1 imports (httpx). On v0.29.0,
+# # PYTHONPATH puts the image's older hub first and `import datasets` crashes.
+# image = "vllm/vllm-openai:v0.30.0"
 # python = "/usr/bin/python3"
 # env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
 # flavor = "a10g-small"
@@ -46,7 +42,7 @@ needs `hf` CLI 1.32+):
 
 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
+    uv run --with vllm==0.30.0 dots-ocr.py input-dataset output-dataset
 """
 
 import argparse
@@ -494,17 +490,17 @@ if __name__ == "__main__":
         print("- 📝 Layout-aware text extraction")
         print("\nExample usage:")
         print("\n1. Basic OCR:")
-        print("   uv run --with vllm==0.29.0 dots-ocr.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.30.0 dots-ocr.py input-dataset output-dataset")
         print("\n2. With custom settings:")
         print(
-            "   uv run --with vllm==0.29.0 dots-ocr.py docs analyzed-docs --batch-size 20 --max-samples 100"
+            "   uv run --with vllm==0.30.0 dots-ocr.py docs analyzed-docs --batch-size 20 --max-samples 100"
         )
         print("\n3. Layout analysis with structure:")
         print(
-            "   uv run --with vllm==0.29.0 dots-ocr.py papers analyzed-structure --prompt-mode layout-all"
+            "   uv run --with vllm==0.30.0 dots-ocr.py papers analyzed-structure --prompt-mode layout-all"
         )
         print("\n4. Layout detection only (no text):")
-        print("   uv run --with vllm==0.29.0 dots-ocr.py docs layout-info --prompt-mode layout-only")
+        print("   uv run --with vllm==0.30.0 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(
@@ -512,7 +508,7 @@ if __name__ == "__main__":
         )
         print("       input-dataset output-dataset")
         print("\n" + "=" * 80)
-        print("\nFor full help, run: uv run --with vllm==0.29.0 dots-ocr.py --help")
+        print("\nFor full help, run: uv run --with vllm==0.30.0 dots-ocr.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -526,13 +522,13 @@ Prompt Modes (official DoTS.ocr prompts):
 
 Examples:
   # Basic text OCR (default)
-  uv run --with vllm==0.29.0 dots-ocr.py my-docs analyzed-docs
+  uv run --with vllm==0.30.0 dots-ocr.py my-docs analyzed-docs
 
   # Full layout analysis with structure
-  uv run --with vllm==0.29.0 dots-ocr.py papers structured --prompt-mode layout-all
+  uv run --with vllm==0.30.0 dots-ocr.py papers structured --prompt-mode layout-all
 
   # Random sampling for testing
-  uv run --with vllm==0.29.0 dots-ocr.py large-dataset test --max-samples 50 --shuffle
+  uv run --with vllm==0.30.0 dots-ocr.py large-dataset test --max-samples 50 --shuffle
         """,
     )
 

glm-ocr.py

@@ -1,14 +1,16 @@
 # /// script
 # requires-python = ">=3.11"
 # dependencies = [
-#     "datasets>=3.1.0",
+#     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
 #     "toolz",
 # ]
 #
 # [tool.hf-jobs]
-# image = "vllm/vllm-openai:v0.29.0"
+# # >=v0.30.0: ships huggingface_hub>=1.31, which datasets>=5.1 imports (httpx). On v0.29.0,
+# # PYTHONPATH puts the image's older hub first and `import datasets` crashes.
+# image = "vllm/vllm-openai:v0.30.0"
 # python = "/usr/bin/python3"
 # env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
 # flavor = "a10g-small"
@@ -22,10 +24,10 @@ GLM-OCR is a compact 0.9B parameter OCR model achieving 94.62% on OmniDocBench V
 Uses CogViT visual encoder with GLM-0.5B language decoder and Multi-Token Prediction
 (MTP) loss for fast, accurate document parsing.
 
-NOTE: On Jobs, vLLM, torch and transformers come from the vllm/vllm-openai:v0.29.0
+NOTE: On Jobs, vLLM, torch and transformers come from the vllm/vllm-openai:v0.30.0
 image pinned in the [tool.hf-jobs] header (`hf` CLI 1.32+). GLM-OCR needs vLLM
 >=0.16.0 (PR #33005) and transformers>=5.1.0; the image has both. To run on your
-own GPU: `uv run --with vllm==0.29.0 glm-ocr.py ...`.
+own GPU: `uv run --with vllm==0.30.0 glm-ocr.py ...`.
 
 Features:
 - 0.9B parameters (ultra-compact)
@@ -37,7 +39,7 @@ Features:
 - MIT licensed
 
 Model: zai-org/GLM-OCR
-vLLM: vllm/vllm-openai:v0.29.0 image (see the header)
+vLLM: vllm/vllm-openai:v0.30.0 image (see the header)
 Performance: 94.62% on OmniDocBench V1.5
 """
 
@@ -587,20 +589,20 @@ if __name__ == "__main__":
         print("  table    - Table extraction")
         print("\nExamples:")
         print("\n1. Basic OCR:")
-        print("   uv run --with vllm==0.29.0 glm-ocr.py input-dataset output-dataset")
+        print("   uv run --with vllm==0.30.0 glm-ocr.py input-dataset output-dataset")
         print("\n2. Formula recognition:")
-        print("   uv run --with vllm==0.29.0 glm-ocr.py docs results --task formula")
+        print("   uv run --with vllm==0.30.0 glm-ocr.py docs results --task formula")
         print("\n3. Table extraction:")
-        print("   uv run --with vllm==0.29.0 glm-ocr.py docs results --task table")
+        print("   uv run --with vllm==0.30.0 glm-ocr.py docs results --task table")
         print("\n4. Test with small sample:")
-        print("   uv run --with vllm==0.29.0 glm-ocr.py large-dataset test --max-samples 10 --shuffle")
+        print("   uv run --with vllm==0.30.0 glm-ocr.py large-dataset test --max-samples 10 --shuffle")
         print("\n5. Running on HF Jobs (hardware and HF_TOKEN come from the")
         print("   script's [tool.hf-jobs] header; --flavor/--timeout override it):")
         print(
             "   hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \\"
         )
         print("       input-dataset output-dataset --batch-size 16")
-        print("\nFor full help: uv run --with vllm==0.29.0 glm-ocr.py --help")
+        print("\nFor full help: uv run --with vllm==0.30.0 glm-ocr.py --help")
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
@@ -613,9 +615,9 @@ Task modes:
   table    Table extraction
 
 Examples:
-  uv run --with vllm==0.29.0 glm-ocr.py my-docs analyzed-docs
-  uv run --with vllm==0.29.0 glm-ocr.py docs results --task formula
-  uv run --with vllm==0.29.0 glm-ocr.py large-dataset test --max-samples 50 --shuffle
+  uv run --with vllm==0.30.0 glm-ocr.py my-docs analyzed-docs
+  uv run --with vllm==0.30.0 glm-ocr.py docs results --task formula
+  uv run --with vllm==0.30.0 glm-ocr.py large-dataset test --max-samples 50 --shuffle
         """,
     )
 

models.json

@@ -1,7 +1,7 @@
 {
   "_meta": {
     "description": "Machine-readable catalog of the OCR recipes in this directory: script -> model, params, backend, support level, the tested Jobs launch config (the script's [tool.hf-jobs] header), and what the model's own card claims about language coverage. Human-readable language notes: LANGUAGES.md. Language evidence levels, strongest first: per-language-benchmark | count-claim | named-list | multilingual-unspecified | english-only | not-stated | not-applicable. Claims come from model cards (compiled 2026-07-15) - treat as claims, not guarantees.",
-    "updated": "2026-10-30",
+    "updated": "2026-11-03",
     "run_pattern": "hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/<script> <input-dataset> <output-dataset>  (hf CLI >= 1.32 reads image/flavor/secrets from the script's [tool.hf-jobs] header; add --timeout for long runs. Scripts without a `jobs` entry need --flavor/-s HF_TOKEN (and any --image in their docstring).)",
     "support_levels": {
       "core": "Maintained. Smoke-tested flagless on HF Jobs on 2026-09-23/24 with the pinned launch config in `jobs`.",
@@ -127,7 +127,7 @@
     },
     "support": "core",
     "jobs": {
-      "image": "vllm/vllm-openai:v0.29.0",
+      "image": "vllm/vllm-openai:v0.30.0",
       "python": "/usr/bin/python3",
       "env": {
         "PYTHONPATH": "/usr/local/lib/python3.12/dist-packages"
@@ -137,7 +137,7 @@
         "HF_TOKEN"
       ]
     },
-    "image": "vllm/vllm-openai:v0.29.0"
+    "image": "vllm/vllm-openai:v0.30.0"
   },
   "paddleocr-vl.py": {
     "model_id": "PaddlePaddle/PaddleOCR-VL",
@@ -506,7 +506,7 @@
     },
     "support": "core",
     "jobs": {
-      "image": "vllm/vllm-openai:v0.29.0",
+      "image": "vllm/vllm-openai:v0.30.0",
       "python": "/usr/bin/python3",
       "env": {
         "PYTHONPATH": "/usr/local/lib/python3.12/dist-packages"
@@ -516,7 +516,7 @@
         "HF_TOKEN"
       ]
     },
-    "image": "vllm/vllm-openai:v0.29.0"
+    "image": "vllm/vllm-openai:v0.30.0"
   },
   "firered-ocr.py": {
     "model_id": "FireRedTeam/FireRed-OCR",
@@ -567,7 +567,7 @@
     },
     "support": "core",
     "jobs": {
-      "image": "vllm/vllm-openai:v0.29.0",
+      "image": "vllm/vllm-openai:v0.30.0",
       "python": "/usr/bin/python3",
       "env": {
         "PYTHONPATH": "/usr/local/lib/python3.12/dist-packages"
@@ -577,7 +577,7 @@
         "HF_TOKEN"
       ]
     },
-    "image": "vllm/vllm-openai:v0.29.0"
+    "image": "vllm/vllm-openai:v0.30.0"
   },
   "nanonets-ocr2.py": {
     "model_id": "nanonets/Nanonets-OCR2-3B",