Critical quality fixes: temperature 0.1 and image resizing to 1288px

Two critical changes to match official olmOCR implementation:

1. **Temperature 0.1 (was 0.0)**
- Matches olmOCR transformers example
- Their pipeline also starts at 0.1 (not 0.0)
- Temperature 0.0 with sampling can cause quality issues

2. **Image resizing to 1288px on longest edge**
- Official pipeline default: target_longest_image_dim=1288
- Model was trained with images at this resolution
- Ensures consistent token counts and detail level
- Uses LANCZOS resampling for quality

These are the two highest-impact fixes identified by code review comparing
our implementation to official olmOCR pipeline and transformers example.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

olmocr2-vllm.py

@@ -117,8 +117,15 @@ def parse_yaml_frontmatter(text: str) -> tuple[dict, str]:
 def make_ocr_message(
     image: Union[Image.Image, Dict[str, Any], str],
     prompt: str = OLMOCR_PROMPT,
+    target_longest_dim: int = 1288,
 ) -> List[Dict]:
-    """Create chat message for olmOCR processing."""
+    """Create chat message for olmOCR processing.
+
+    Args:
+        image: Input image (PIL Image, dict with bytes, or path)
+        prompt: OCR prompt text
+        target_longest_dim: Target size for longest image dimension (default 1288, matching olmOCR)
+    """
     # Convert to PIL Image if needed
     if isinstance(image, Image.Image):
         pil_img = image
@@ -129,6 +136,16 @@ def make_ocr_message(
     else:
         raise ValueError(f"Unsupported image type: {type(image)}")
 
+    # Resize image to target dimension (matching olmOCR pipeline default of 1288px)
+    width, height = pil_img.size
+    longest_side = max(width, height)
+    if longest_side != target_longest_dim:
+        scale = target_longest_dim / longest_side
+        new_width = int(width * scale)
+        new_height = int(height * scale)
+        pil_img = pil_img.resize((new_width, new_height), Image.Resampling.LANCZOS)
+        logger.debug(f"Resized image from {width}x{height} to {new_width}x{new_height}")
+
     # Convert to base64 data URI
     buf = io.BytesIO()
     pil_img.save(buf, format="PNG")
@@ -265,7 +282,7 @@ def main(
     model: str = "allenai/olmOCR-2-7B-1025-FP8",
     max_model_len: int = 16384,
     max_tokens: int = 8192,
-    temperature: float = 0.0,
+    temperature: float = 0.1,
     gpu_memory_utilization: float = 0.8,
     guided_decoding: bool = False,
     hf_token: str = None,
@@ -287,7 +304,7 @@ def main(
         model: HuggingFace model ID for olmOCR
         max_model_len: Maximum context length
         max_tokens: Maximum tokens to generate per image
-        temperature: Sampling temperature (0.0 for deterministic)
+        temperature: Sampling temperature (0.1 default, matches olmOCR)
         gpu_memory_utilization: Fraction of GPU memory to use
         guided_decoding: Enable guided decoding with regex for YAML front matter
         hf_token: HuggingFace token for authentication
@@ -340,7 +357,7 @@ def main(
         limit_mm_per_prompt={"image": 1},
     )
 
-    # Sampling parameters - olmOCR uses temperature 0.0 for deterministic output
+    # Sampling parameters - olmOCR uses temperature 0.1 (transformers example)
     sampling_params_kwargs = {
         "temperature": temperature,
         "max_tokens": max_tokens,
@@ -544,8 +561,8 @@ Examples:
     parser.add_argument(
         "--temperature",
         type=float,
-        default=0.0,
-        help="Sampling temperature (default: 0.0 for deterministic output)",
+        default=0.1,
+        help="Sampling temperature (default: 0.1, matches olmOCR transformers example)",
     )
     parser.add_argument(
         "--gpu-memory-utilization",