Upload push-model.py with huggingface_hub

push-model.py

@@ -0,0 +1,169 @@
+import os
+from typing import Optional
+
+from huggingface_hub import HfApi, create_repo
+from transformers import AutoConfig, AutoModelForCausalLM, AutoProcessor
+
+
+class SpecVisionModelRegistration:
+    """
+    Handles the registration and pushing of SpecVision model to Hugging Face Hub.
+    """
+    
+    def __init__(self, 
+                 model_path: str,
+                 repo_name: str,
+                 organization: Optional[str] = None,
+                 token: Optional[str] = None):
+        """
+        Initialize the registration handler.
+        
+        Args:
+            model_path: Local path to your model files
+            repo_name: Name for the Hugging Face repository
+            organization: Optional organization name on Hugging Face
+            token: Hugging Face API token
+        """
+        self.model_path = model_path
+        self.repo_name = repo_name
+        self.organization = organization
+        self.token = token or os.getenv("HF_TOKEN")
+        
+        if not self.token:
+            raise ValueError("Please provide a Hugging Face token either directly or via HF_TOKEN environment variable")
+            
+        self.api = HfApi()
+        
+    def register_model_components(self):
+        """
+        Register the SpecVision model architecture components with the transformers library.
+        """
+        # Import your custom model classes
+        from configuration_spec_vision import SpecVisionConfig
+        from modeling_spec_vision import SpecVisionForCausalLM
+        from processing_spec_vision import SpecVisionProcessor
+
+        # Register the model architecture
+        AutoConfig.register("spec_vision", SpecVisionConfig)
+        AutoModelForCausalLM.register(SpecVisionConfig, SpecVisionForCausalLM)
+        AutoProcessor.register(SpecVisionConfig, SpecVisionProcessor)
+        
+        print("✓ Successfully registered SpecVision model architecture")
+        
+    def create_huggingface_repo(self):
+        """
+        Create a new repository on the Hugging Face Hub.
+        """
+        repo_id = f"{self.organization}/{self.repo_name}" if self.organization else self.repo_name
+        
+        try:
+            create_repo(
+                repo_id,
+                token=self.token,
+                private=False,
+                exist_ok=True
+            )
+            print(f"✓ Created/accessed repository: {repo_id}")
+            return repo_id
+        except Exception as e:
+            raise Exception(f"Failed to create repository: {str(e)}")
+            
+    def update_model_card(self):
+        """
+        Create or update the model card (README.md) with necessary information.
+        """
+        model_card = f"""---
+language: en
+tags:
+- spec-vision
+- vision-language-model
+- transformers
+license: apache-2.0
+---
+
+# SpecVision Model
+
+This is the SpecVision model, a vision-language model based on the transformers architecture.
+
+## Model Description
+
+SpecVision is designed for vision-language tasks, combining visual and textual understanding capabilities.
+
+## Usage
+
+```python
+from transformers import AutoConfig, AutoModelForCausalLM, AutoProcessor
+
+# Load the model and processor
+model = AutoModelForCausalLM.from_pretrained("{self.repo_name}")
+processor = AutoProcessor.from_pretrained("{self.repo_name}")
+
+# Process inputs
+inputs = processor(images=image, text=text, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+## Training and Evaluation
+
+[Add your training and evaluation details here]
+
+## Limitations and Biases
+
+[Add any known limitations and biases here]
+"""
+        
+        with open(os.path.join(self.model_path, "README.md"), "w") as f:
+            f.write(model_card)
+            
+        print("✓ Created/updated model card")
+        
+    def push_to_hub(self):
+        """
+        Push the model, configurations, and related files to Hugging Face Hub.
+        """
+        repo_id = self.create_huggingface_repo()
+        
+        # Update the model card first
+        self.update_model_card()
+        
+        # Create a dictionary of files to upload
+        files_to_upload = {}
+        for filename in os.listdir(self.model_path):
+            if filename.endswith(('.json', '.py', '.md', '.txt', '.safetensors')):
+                filepath = os.path.join(self.model_path, filename)
+                files_to_upload[filename] = filepath
+        
+        # Upload all files
+        for filename, filepath in files_to_upload.items():
+            self.api.upload_file(
+                path_or_fileobj=filepath,
+                path_in_repo=filename,
+                repo_id=repo_id,
+                token=self.token
+            )
+            print(f"✓ Uploaded {filename}")
+            
+        print(f"\nModel successfully pushed to https://huggingface.co/{repo_id}")
+
+def main():
+    """
+    Main function to execute the registration and push process.
+    """
+    # You can set your HF_TOKEN as an environment variable or pass it directly
+    TOKEN = os.getenv("HF_TOKEN")  # or "your_token_here"
+    
+    registration = SpecVisionModelRegistration(
+        model_path="./",  # Assuming you're running from the model directory
+        repo_name="Spec-4B-Vision-V1",  # Change this to your desired repo name
+        organization="SVECTOR-CORPORATION",  # Your organization name
+        token=TOKEN
+    )
+    
+    # Register the model architecture
+    registration.register_model_components()
+    
+    # Push everything to the Hub
+    registration.push_to_hub()
+    
+if __name__ == "__main__":
+    main()