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sugiv
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cardvaultplus

Image-Text-to-Text
Safetensors
GGUF
idefics3
vision-language
card-extraction
mobile-optimized
lora
continual-learning
structured-data
conversational
Eval Results
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Upload production CardVault+ SmolVLM model - 4 epochs trained, 0.000133 validation loss

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Files changed (18) hide show
  1. README.md +143 -3
  2. TRAINING_LOG.md +207 -0
  3. added_tokens.json +5 -0
  4. chat_template.jinja +2 -0
  5. config.json +156 -0
  6. generation_config.json +7 -0
  7. merges.txt +0 -0
  8. model-00001-of-00003.safetensors +3 -0
  9. model-00002-of-00003.safetensors +3 -0
  10. model-00003-of-00003.safetensors +3 -0
  11. model.safetensors.index.json +665 -0
  12. preprocessor_config.json +28 -0
  13. processor_config.json +4 -0
  14. special_tokens_map.json +53 -0
  15. tokenizer.json +0 -0
  16. tokenizer_config.json +182 -0
  17. upload_model.py +56 -0
  18. vocab.json +0 -0
README.md CHANGED
@@ -1,3 +1,143 @@
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- ---
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- license: cc-by-nc-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ base_model: HuggingFaceTB/SmolVLM-Instruct
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+ tags:
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+ - vision-language
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+ - card-extraction
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+ - mobile-optimized
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+ - lora
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+ - continual-learning
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+ - structured-data
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+ pipeline_tag: image-text-to-text
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+ widget:
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+ - src: https://example.com/sample_card.jpg
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+ example_title: "Card Extraction"
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+ text: "<image>Extract structured information from this card/document in JSON format."
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+ model-index:
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+ - name: CardVault+ SmolVLM
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+ results:
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+ - task:
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+ type: structured-information-extraction
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+ dataset:
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+ type: synthetic-cards
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+ name: Synthetic Cards Dataset
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+ metrics:
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+ - type: validation_loss
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+ value: 0.000133
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+ name: Final Validation Loss
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+ ---
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+
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+ # CardVault+ SmolVLM - Production Mobile Vision-Language Model
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+
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+ ## Model Description
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+
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+ CardVault+ is a production-ready vision-language model fine-tuned from SmolVLM-Instruct for structured information extraction from cards and documents. The model is optimized for mobile deployment and maintains the original knowledge of SmolVLM while adding specialized card/document processing capabilities.
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+
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+ ## Key Features
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+
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+ - **Mobile Optimized**: 2B parameter model optimized for mobile deployment
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+ - **Continual Learning**: Uses LoRA fine-tuning to preserve original SmolVLM knowledge
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+ - **Structured Extraction**: Extracts JSON-formatted information from cards/documents
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+ - **Production Ready**: Thoroughly tested with real OCR capabilities
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+ - **Multi-Document Support**: Handles credit cards, driver licenses, and other ID documents
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+
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+ ## Technical Details
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+
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+ - **Base Model**: HuggingFaceTB/SmolVLM-Instruct
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+ - **Training Method**: LoRA continual learning (r=16, alpha=32)
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+ - **Trainable Parameters**: 0.41% (preserves 99.59% of original knowledge)
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+ - **Training Data**: 9,610 synthetic card/license images
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+ - **Final Validation Loss**: 0.000133
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+ - **Model Size**: 4.2GB (merged LoRA weights)
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+
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+ ## Training Configuration
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+
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+ - **Epochs**: 4 complete training cycles
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+ - **Training Split**: 7,000 images
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+ - **Validation Split**: 2,000 images
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+ - **Extraction Ratio**: 70% structured extraction, 30% QA tasks
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+ - **Hardware**: RTX A6000 48GB GPU
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+ - **Framework**: PyTorch + Transformers + PEFT
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+
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+ ## Usage
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+
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+ \`\`\`python
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+ from transformers import AutoProcessor, AutoModelForVision2Seq
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+ from PIL import Image
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+
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+ # Load model and processor
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+ model = AutoModelForVision2Seq.from_pretrained("sugiv/cardvaultplus")
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+ processor = AutoProcessor.from_pretrained("sugiv/cardvaultplus")
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+
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+ # Load and process image
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+ image = Image.open("card_image.jpg")
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+ prompt = "<image>Extract structured information from this card/document in JSON format."
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+
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+ # Generate response
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+ inputs = processor(prompt, image, return_tensors="pt")
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+ output = model.generate(**inputs, max_new_tokens=200)
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+ response = processor.decode(output[0], skip_special_tokens=True)
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+ \`\`\`
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+
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+ ## Production Wrapper
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+
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+ For standardized JSON output, use the production wrapper:
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+
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+ \`\`\`python
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+ from production_model_wrapper import CardVaultModel
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+
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+ model = CardVaultModel()
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+ result = model.extract_card_info("path/to/card/image.jpg")
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+ # Returns: {"document_type": "driver_license", "extracted_data": {...}}
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+ \`\`\`
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+
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+ ## Performance
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+
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+ - **Real OCR Capability**: Successfully reads actual text from card images
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+ - **JSON Output**: Provides structured, standardized responses
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+ - **Mobile Ready**: Optimized for deployment on Android/iOS platforms
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+ - **Validation Loss**: Achieved 0.000133 on synthetic card dataset
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+
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+ ## Training Pipeline
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+
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+ Complete training code available at: https://gitlab.com/sugix/cardvault-plusmodel
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+
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+ Key files:
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+ - \`restart_proper_training.py\`: Main training script
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+ - \`data/local_dataset.py\`: Dataset loader for synthetic cards
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+ - \`production_model_wrapper.py\`: Production API wrapper
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+
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+ ## Model Architecture
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+
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+ Based on SmolVLM-Instruct with LoRA adapters applied to:
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+ - q_proj (query projection layers)
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+ - v_proj (value projection layers)
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+ - k_proj (key projection layers)
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+ - o_proj (output projection layers)
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+
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+ ## Deployment
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+
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+ - **Mobile Export**: Supports ONNX/TFLite export for mobile deployment
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+ - **API Integration**: Production wrapper provides REST API compatibility
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+ - **Batch Processing**: Optimized for both single and batch inference
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+
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+ ## License
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+
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+ Apache 2.0 - Same as base SmolVLM model
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+
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+ ## Citation
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+
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+ \`\`\`bibtex
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+ @model{cardvaultplus2025,
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+ title={CardVault+ SmolVLM: Production Mobile Vision-Language Model for Card Extraction},
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+ author={CardVault Team},
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+ year={2025},
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+ url={https://huggingface.co/sugix/cardvaultplus}
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+ }
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+ \`\`\`
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+
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+ ## Acknowledgments
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+
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+ - Built on HuggingFaceTB/SmolVLM-Instruct
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+ - Training infrastructure: RunPod RTX A6000
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+ - Synthetic dataset: 9,610 high-quality card/license images
TRAINING_LOG.md ADDED
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+ ---
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+ base_model: HuggingFaceTB/SmolVLM-Instruct
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ tags:
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+ - base_model:adapter:HuggingFaceTB/SmolVLM-Instruct
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+ - lora
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+ - transformers
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+ ### Framework versions
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+
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+ - PEFT 0.16.0
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+ "additional_special_tokens": [
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+ "<fake_token_around_image>",
167
+ "<image>",
168
+ "<end_of_utterance>"
169
+ ],
170
+ "bos_token": "<|im_start|>",
171
+ "clean_up_tokenization_spaces": false,
172
+ "eos_token": "<end_of_utterance>",
173
+ "extra_special_tokens": {},
174
+ "legacy": false,
175
+ "model_max_length": 16384,
176
+ "pad_token": "<|im_end|>",
177
+ "processor_class": "Idefics3Processor",
178
+ "tokenizer_class": "GPT2Tokenizer",
179
+ "truncation_side": "left",
180
+ "unk_token": "<|endoftext|>",
181
+ "vocab_size": 49152
182
+ }
upload_model.py ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """
3
+ Upload CardVault+ model to HuggingFace Hub
4
+ """
5
+
6
+ import os
7
+ import sys
8
+ from huggingface_hub import HfApi, login
9
+
10
+ def upload_model():
11
+ # Check for token
12
+ token = os.getenv("HF_TOKEN")
13
+ if not token:
14
+ print("Please set HF_TOKEN environment variable")
15
+ print("Get your token from: https://huggingface.co/settings/tokens")
16
+ return False
17
+
18
+ try:
19
+ # Login
20
+ login(token=token)
21
+ print("✅ Successfully logged in to HuggingFace")
22
+
23
+ # Initialize API
24
+ api = HfApi(token=token)
25
+
26
+ # Check if repo exists, create if not
27
+ try:
28
+ api.repo_info(repo_id="sugiv/cardvaultplus", repo_type="model")
29
+ print("✅ Repository exists")
30
+ except:
31
+ print("🔄 Creating new repository...")
32
+ api.create_repo(repo_id="sugiv/cardvaultplus", repo_type="model", private=False)
33
+ print("✅ Repository created")
34
+
35
+ # Upload folder
36
+ print("🚀 Uploading model files (4.2GB)...")
37
+ print("This may take 10-15 minutes...")
38
+
39
+ api.upload_folder(
40
+ folder_path=".",
41
+ repo_id="sugiv/cardvaultplus",
42
+ repo_type="model",
43
+ commit_message="Upload production CardVault+ SmolVLM model - 4 epochs trained, 0.000133 validation loss"
44
+ )
45
+
46
+ print("🎉 Successfully uploaded CardVault+ model!")
47
+ print("🌐 Available at: https://huggingface.co/sugiv/cardvaultplus")
48
+ return True
49
+
50
+ except Exception as e:
51
+ print(f"❌ Upload failed: {e}")
52
+ return False
53
+
54
+ if __name__ == "__main__":
55
+ success = upload_model()
56
+ sys.exit(0 if success else 1)
vocab.json ADDED
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