datasets:
- scta/scta-htr-training-data
base_model:
- Qwen/Qwen2-VL-2B-Instruct
import torch from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info
device = "cuda" if torch.cuda.is_available() else "cpu"
model_dir = "medieval-data/qwen2-vl-2b-scta"
model = Qwen2VLForConditionalGeneration.from_pretrained( model_dir, torch_dtype="auto", device_map="auto" )
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") image_url ="""https://loris2.scta.info/lon/L28v.jpg/full/full/0/default.jpg""" messages = [ { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "Convert this image to text."}, ], } ]
Preparation for inference
text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to(device)
Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=4000) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False )
print(output_text)
Import required libraries if not already imported
from IPython.display import display, Image
Display the output text
print(output_text)
Display the image
display(Image(url=image_url))