| ## How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| ```python | |
| import requests | |
| from PIL import Image | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| model = AutoModelForCausalLM.from_pretrained("F16/florence2-large-ft-gufeng_v3", trust_remote_code=True) | |
| processor = AutoProcessor.from_pretrained("F16/florence2-large-ft-gufeng_v3", trust_remote_code=True) | |
| prompt = "<MORE_DETAILED_CAPTION>" | |
| url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| inputs = processor(text=prompt, images=image, return_tensors="pt") | |
| generated_ids = model.generate( | |
| input_ids=inputs["input_ids"], | |
| pixel_values=inputs["pixel_values"], | |
| max_new_tokens=1024, | |
| do_sample=False, | |
| num_beams=3 | |
| ) | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] | |
| parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height)) | |
| print(parsed_answer) | |
| ``` | |
| --- | |
| license: mit | |
| --- | |