Add ColQwen2.5 model support and improve model selection (#183)

- Add ColQwen2.5 and ColQwen2_5_Processor imports
- Implement smart model type detection for colqwen2, colqwen2.5, and colpali
- Add task name aliases for easier benchmark invocation
- Add safe model name handling for file paths and index naming
- Support custom model paths including LoRA adapters
- Improve model choice validation and error handling

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

Co-authored-by: Claude <noreply@anthropic.com>

apps/multimodal/vision-based-pdf-multi-vector/leann_multi_vector.py

@@ -152,20 +152,65 @@ def _select_device_and_dtype():
 
 def _load_colvision(model_choice: str):
     import torch
-    from colpali_engine.models import ColPali, ColQwen2, ColQwen2Processor
+    from colpali_engine.models import (
+        ColPali,
+        ColQwen2,
+        ColQwen2_5,
+        ColQwen2_5_Processor,
+        ColQwen2Processor,
+    )
     from colpali_engine.models.paligemma.colpali.processing_colpali import ColPaliProcessor
     from transformers.utils.import_utils import is_flash_attn_2_available
 
     device_str, device, dtype = _select_device_and_dtype()
 
+    # Determine model name and type
+    # IMPORTANT: Check colqwen2.5 BEFORE colqwen2 to avoid false matches
+    model_choice_lower = model_choice.lower()
     if model_choice == "colqwen2":
         model_name = "vidore/colqwen2-v1.0"
-        # On CPU/MPS we must avoid flash-attn and stay eager; on CUDA prefer flash-attn if available
-        attn_implementation = (
-            "flash_attention_2"
-            if (device_str == "cuda" and is_flash_attn_2_available())
-            else "eager"
-        )
+        model_type = "colqwen2"
+    elif model_choice == "colqwen2.5" or model_choice == "colqwen25":
+        model_name = "vidore/colqwen2.5-v0.2"
+        model_type = "colqwen2.5"
+    elif model_choice == "colpali":
+        model_name = "vidore/colpali-v1.2"
+        model_type = "colpali"
+    elif (
+        "colqwen2.5" in model_choice_lower
+        or "colqwen25" in model_choice_lower
+        or "colqwen2_5" in model_choice_lower
+    ):
+        # Handle HuggingFace model names like "vidore/colqwen2.5-v0.2"
+        model_name = model_choice
+        model_type = "colqwen2.5"
+    elif "colqwen2" in model_choice_lower and "colqwen2-v1.0" in model_choice_lower:
+        # Handle HuggingFace model names like "vidore/colqwen2-v1.0" (but not colqwen2.5)
+        model_name = model_choice
+        model_type = "colqwen2"
+    elif "colpali" in model_choice_lower:
+        # Handle HuggingFace model names like "vidore/colpali-v1.2"
+        model_name = model_choice
+        model_type = "colpali"
+    else:
+        # Default to colpali for backward compatibility
+        model_name = "vidore/colpali-v1.2"
+        model_type = "colpali"
+
+    # Load model based on type
+    attn_implementation = (
+        "flash_attention_2" if (device_str == "cuda" and is_flash_attn_2_available()) else "eager"
+    )
+
+    if model_type == "colqwen2.5":
+        model = ColQwen2_5.from_pretrained(
+            model_name,
+            torch_dtype=torch.bfloat16,
+            device_map=device,
+            attn_implementation=attn_implementation,
+        ).eval()
+        processor = ColQwen2_5_Processor.from_pretrained(model_name)
+    elif model_type == "colqwen2":
         model = ColQwen2.from_pretrained(
             model_name,
             torch_dtype=torch.bfloat16,
@@ -173,8 +218,7 @@ def _load_colvision(model_choice: str):
             attn_implementation=attn_implementation,
         ).eval()
         processor = ColQwen2Processor.from_pretrained(model_name)
-    else:
-        model_name = "vidore/colpali-v1.2"
+    else:  # colpali
         model = ColPali.from_pretrained(
             model_name,
             torch_dtype=torch.bfloat16,