Instructions to use DaMax96/Stick_OCR_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaMax96/Stick_OCR_v4 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="DaMax96/Stick_OCR_v4")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("DaMax96/Stick_OCR_v4") model = AutoModelForMultimodalLM.from_pretrained("DaMax96/Stick_OCR_v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from typing import Dict, List, Any | |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
| class PreTrainedPipeline(): | |
| def __init__(self, path=""): | |
| self.processor = TrOCRProcessor.from_pretrained(path) | |
| self.model = VisionEncoderDecoderModel.from_pretrained(path) | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| image = data.pop("inputs", data) | |
| # process image | |
| pixel_values = self.processor(images=image, return_tensors="pt").pixel_values | |
| # run prediction | |
| generated_ids = self.model.generate(pixel_values) | |
| # decode output | |
| prediction = generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True) | |
| return {"text":prediction[0]} |