Update README.md

README.md

@@ -1,6 +1,63 @@
 ---
 license: apache-2.0
 ---
+## Inference 
+Our models are established on top of the Qwen2.5-VL family. So we include a simple use case here, and refer the readers to [the standard inference procedure of Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL).
+
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "Reallm-Labs/Infi-MMR-3B", torch_dtype="auto", device_map="auto"
+)
+min_pixels = 256*28*28
+max_pixels = 1280*28*28
+processor = AutoProcessor.from_pretrained("Reallm-Labs/Infi-MMR-3B", min_pixels=min_pixels, max_pixels=max_pixels)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+# 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(model.device)
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=4096)
+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)
+
+```
+
+
+
 ## Citation Information
 If you find this work useful, we would be grateful if you consider citing the following papers:
 ```bibtex