Update README.md

README.md

@@ -1,3 +1,69 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+Model Usage:
+
+```
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_path, max_pixels=262144)
+
+
+reason_prompt = r"You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. During this reasoning process, prioritize analyzing the local regions of the image by leveraging the bounding box coordinates in the format [x_min, y_min, x_max, y_max]. The final answer MUST BE put in \boxed{}. An example is like: <think> reasoning process 1 with [x_min1, y_min1, x_max1, y_max1]; reasoning process 2 with [x_min2, y_min2, x_max2, y_max2] </think>. The answer is: \boxed{answer}."
+
+def get_label(images, content1):
+    content_list = []
+    for image_url in images:
+        content_list.append({
+            "type": "image",
+            "image": image_url,
+        })
+    if mode == 'think':
+        content_list.append({"type": "text",
+                             "text": content1 + '\n' + reason_prompt + '\n'})
+    else:
+        content_list.append({"type": "text",
+                             "text": content1})
+    messages = [
+        {
+            "role": "user",
+            "content": content_list
+        }
+    ]
+
+    # Preparation for inference
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    # print(text)
+    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("cuda")
+
+    # Inference: Generation of the output
+    generated_ids = model.generate(**inputs, max_new_tokens=4096, do_sample=True, temperature=0.6)
+    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)
+    # print(output_text[0])
+    return output_text[0]
+
+```