Create README.md

README.md

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+---
+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))