Replace fragile git dep with stable transformers>=5.1.0 for GLM-OCR

GLM-OCR support landed in transformers v5.1.0. The previous unpinned git
dependency could break at any commit. Also updated docstring to note vLLM
nightly was re-verified on 2026-02-12.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

glm-ocr.py

@@ -15,7 +15,7 @@
 #
 # [tool.uv]
 # prerelease = "allow"
-# override-dependencies = ["transformers @ git+https://github.com/huggingface/transformers.git"]
+# override-dependencies = ["transformers>=5.1.0"]
 # ///
 
 """
@@ -25,7 +25,8 @@ 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: Requires vLLM nightly wheels and transformers from git for GLM-OCR support.
+NOTE: Requires vLLM nightly wheels (checked 2026-02-12, still needed) and
+transformers>=5.1.0 (GLM-OCR support landed in stable release).
 First run may take a few minutes to download and install dependencies.
 
 Features:
@@ -38,7 +39,7 @@ Features:
 - MIT licensed
 
 Model: zai-org/GLM-OCR
-vLLM: Requires vLLM nightly build + transformers from git
+vLLM: Requires vLLM nightly build + transformers>=5.1.0
 Performance: 94.62% on OmniDocBench V1.5
 """
 
@@ -140,7 +141,11 @@ def create_dataset_card(
 ) -> str:
     """Create a dataset card documenting the OCR process."""
     model_name = model.split("/")[-1]
-    task_desc = {"ocr": "text recognition", "formula": "formula recognition", "table": "table recognition"}
+    task_desc = {
+        "ocr": "text recognition",
+        "formula": "formula recognition",
+        "table": "table recognition",
+    }
 
     return f"""---
 tags:
@@ -305,10 +310,7 @@ def main(
         )
 
         try:
-            batch_messages = [
-                make_ocr_message(img, task=task)
-                for img in batch_images
-            ]
+            batch_messages = [make_ocr_message(img, task=task) for img in batch_images]
 
             outputs = llm.chat(batch_messages, sampling_params)
 
@@ -347,7 +349,11 @@ def main(
 
         def update_inference_info(example):
             try:
-                existing_info = json.loads(example["inference_info"]) if example["inference_info"] else []
+                existing_info = (
+                    json.loads(example["inference_info"])
+                    if example["inference_info"]
+                    else []
+                )
             except (json.JSONDecodeError, TypeError):
                 existing_info = []
             existing_info.append(inference_entry)
@@ -385,9 +391,13 @@ def main(
     card.push_to_hub(output_dataset, token=HF_TOKEN)
 
     logger.info("Done! GLM-OCR processing complete.")
-    logger.info(f"Dataset available at: https://huggingface.co/datasets/{output_dataset}")
+    logger.info(
+        f"Dataset available at: https://huggingface.co/datasets/{output_dataset}"
+    )
     logger.info(f"Processing time: {processing_time_str}")
-    logger.info(f"Processing speed: {len(dataset) / processing_duration.total_seconds():.2f} images/sec")
+    logger.info(
+        f"Processing speed: {len(dataset) / processing_duration.total_seconds():.2f} images/sec"
+    )
 
 
 if __name__ == "__main__":
@@ -412,7 +422,9 @@ if __name__ == "__main__":
         print("\n5. Running on HF Jobs:")
         print("   hf jobs uv run --flavor l4x1 \\")
         print("     -s HF_TOKEN \\")
-        print("     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \\")
+        print(
+            "     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 glm-ocr.py --help")
         sys.exit(0)