Update README.md

README.md

@@ -9,10 +9,80 @@ tags:
 library_name: transformers
 ---
 
+
+## Introduction
+
+`Cylingo/Xinyuan-VL-2B` is a high-performance multimodal large model for the end-side from the Cylingo Group, which is fine-tuned with `Qwen/Qwen2-VL-2B-Instruct`, and uses more than 5M of multimodal data as well as a small amount of plain text data. 
+
+It performs well on several authoritative Benchmarks.
+
+## How to use
+
+In order to rely on the thriving ecology of the open source community, we chose to fine-tune [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) to form our `Cylingo/Xinyuan-VL- 2B`. 
+
+Thus, using `Cylingo/Xinyuan-VL-2B` is consistent with using `Qwen/Qwen2-VL-2B-Instruct`:
+
+```Python
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Cylingo/Xinyuan-VL-2B", torch_dtype="auto", device_map="auto"
+)
+
+# default processer
+processor = AutoProcessor.from_pretrained("Cylingo/Xinyuan-VL-2B")
+
+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("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+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)
+
+```
+
+
+
+## Evaluation
 We evaluated **[XinYuan-VL-2B](https://huggingface.co/thomas-yanxin/XinYuan-VL-2B)** using the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) toolkit across the following benchmarks and found that **XinYuan-VL-2B** **outperformed** [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) released by Alibaba Cloud, as well as other models of comparable parameter scale that have significant influence in the open-source community.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6299c90ef1f2a097fcaa1293/7ThTCYfd_lDzsvaFLlUv2.png)
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6299c90ef1f2a097fcaa1293/7ThTCYfd_lDzsvaFLlUv2.png">
+</p>
 
+You can see the results in [opencompass/open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard):
 
 | Benchamrk | MiniCPM-2B | InternVL-2B | Qwen2-VL-2B | **XinYuan-VL-2B** |
 | :---: | :---: | :---: | :---: | :---: |