Upload oculus_unified_model/configuration_oculus.py with huggingface_hub

oculus_unified_model/configuration_oculus.py

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+"""
+Oculus Configuration
+
+HuggingFace-compatible configuration for the unified Oculus model.
+"""
+
+from typing import Optional, Dict, Any, List
+from transformers import PretrainedConfig
+
+
+class OculusConfig(PretrainedConfig):
+    """
+    Configuration class for Oculus vision-language model.
+    
+    Args:
+        vision_config: Configuration for vision encoders
+        projector_config: Configuration for vision-to-language projector
+        text_config: Configuration for language model
+        reasoning_enabled: Whether to enable thinking traces
+        output_mode: Default output mode ("text", "point", "box", "polygon")
+    """
+    
+    model_type = "oculus"
+    
+    def __init__(
+        self,
+        # Vision encoder settings
+        dinov3_model_id: str = "facebook/dinov2-large",
+        siglip_model_id: str = "google/siglip-base-patch16-224",
+        dinov3_hidden_size: int = 1280,  # DINOv3 ViT-H/16+ output dim
+        siglip_hidden_size: int = 768,   # SigLIP2 base output dim
+        
+        # Projector settings
+        projector_hidden_dim: int = 2048,
+        num_vision_tokens: int = 64,
+        
+        # Language model settings
+        text_model_id: str = "Salesforce/blip-image-captioning-base",
+        lm_hidden_size: int = 1536,
+        vocab_size: int = 131072,
+        max_position_embeddings: int = 32768,
+        
+        # Reasoning settings
+        reasoning_enabled: bool = True,
+        thinking_token: str = "<think>",
+        thinking_end_token: str = "</think>",
+        max_thinking_tokens: int = 256,
+        
+        # Output mode settings
+        output_mode: str = "text",  # "text", "point", "box", "polygon"
+        num_detection_classes: int = 80,
+        num_segmentation_classes: int = 150,
+        
+        # Generation settings
+        max_new_tokens: int = 512,
+        temperature: float = 0.7,
+        top_p: float = 0.95,
+        
+        # Tool calling / Focus system
+        enable_focus: bool = True,
+        focus_token: str = "<focus>",
+        focus_end_token: str = "</focus>",
+        
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        
+        # Vision
+        self.dinov3_model_id = dinov3_model_id
+        self.siglip_model_id = siglip_model_id
+        self.dinov3_hidden_size = dinov3_hidden_size
+        self.siglip_hidden_size = siglip_hidden_size
+        self.fused_vision_dim = dinov3_hidden_size + siglip_hidden_size
+        
+        # Projector
+        self.projector_hidden_dim = projector_hidden_dim
+        self.num_vision_tokens = num_vision_tokens
+        
+        # Language model
+        self.text_model_id = text_model_id
+        self.lm_hidden_size = lm_hidden_size
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        
+        # Reasoning
+        self.reasoning_enabled = reasoning_enabled
+        self.thinking_token = thinking_token
+        self.thinking_end_token = thinking_end_token
+        self.max_thinking_tokens = max_thinking_tokens
+        
+        # Output modes
+        self.output_mode = output_mode
+        self.num_detection_classes = num_detection_classes
+        self.num_segmentation_classes = num_segmentation_classes
+        
+        # Generation
+        self.max_new_tokens = max_new_tokens
+        self.temperature = temperature
+        self.top_p = top_p
+        
+        # Focus system
+        self.enable_focus = enable_focus
+        self.focus_token = focus_token
+        self.focus_end_token = focus_end_token
+    
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        """Load config from pretrained path."""
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+        return cls.from_dict(config_dict, **kwargs)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Serialize config to dictionary."""
+        output = super().to_dict()
+        return output
+
+
+# Register for auto-loading
+OculusConfig.register_for_auto_class()