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For extracting text from an image into JSON locally, with a model size of 10B or less, here's a breakdown of considerations for LLaMa and Qwen: * **LLaMa:** While LLaMa itself isn't inherently multimodal, you can fine-tune a LLaMa-based model with visual capabilities. Several projects focus on adding vision encoders to LLaMa. However, achieving robust OCR-to-JSON extraction with a 10B or smaller LLaMa variant might require significant fine-tuning data and effort. The base LLaMa models lack the vision component needed for direct image processing. * **Qwen:** Qwen-VL and Qwen-VL-Chat are specifically designed as multimodal models. The base Qwen models are strong, and the VL versions are built to handle image and text inputs. Qwen-VL comes in different sizes, and you should be able to find a version that fits within your 10B parameter limit. Qwen-VL is likely the better choice out-of-the-box for your task. **Recommendation:** **Qwen-VL** is the more practical option for your use case. It's designed for multimodal tasks, including image understanding and text extraction. You'll likely get better results with less fine-tuning compared to trying to adapt a LLaMa model. **Considerations:** * **Fine-tuning:** Regardless of whether you choose LLaMa or Qwen, fine-tuning on a dataset of images with corresponding JSON outputs will significantly improve performance. This dataset should be representative of the types of images and JSON structures you expect to encounter. * **Hardware:** Running even a 10B model locally requires a decent GPU. Ensure your hardware meets the minimum requirements for the model you choose. * **Libraries/Frameworks:** Use libraries like Transformers (Hugging Face) to load and run the models. You'll also need libraries for image processing (e.g., OpenCV, Pillow). * **OCR Engine:** Consider integrating a dedicated OCR engine (like Tesseract) as a pre-processing step to improve the accuracy of text detection before feeding the text into the multimodal model. This can be especially helpful for images with low resolution or complex layouts. * **JSON Schema:** Define a clear JSON schema for the output. This will help the model learn to structure the extracted information correctly. In summary, while LLaMa is a powerful language model, Qwen-VL's built-in multimodal capabilities make it the more suitable choice for extracting text from images into JSON, especially within the 10B parameter constraint. Remember to fine-tune the model on your specific data for optimal results.

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