Update README.md

README.md

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 # Introduction
 
-The Infinity-VL-2B model is a vision-language model (VLM) trained based on the [LLava-one-vision] (https://llava-vl.github.io/blog/2024-08-05-llava-onevision/) framework. The [Qwen2.5-1.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model is chose as the LLM, while [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) is utilized as the vision tower.
+The Infinity-VL-2B-llava-qwen model is a vision-language model (VLM) trained based on the [LLava-one-vision] (https://llava-vl.github.io/blog/2024-08-05-llava-onevision/) framework. The [Qwen2.5-1.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model is chose as the LLM, while [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) is utilized as the vision tower.
 
 The model was trained on our self-built Infinity-MM dataset, which contains approximately 40 million image-text pairs. This dataset is a combination of open-source data collected from the internet and synthetic instruction data generated using open-source VLM models.
 
@@ -10,7 +10,7 @@ We plan to open-source the Infinity-MM dataset, training scripts, and related re
 
 We evaluated the model using the [VLMEvalKit] (GitHub - open-compass/VLMEvalKit: Open-source evaluation toolkit of large vision-language models (LV) tool. Whenever possible, we prioritized using the GPT-4 API for test sets that support API-based evaluation.
 
-| Test sets       |   MiniCPM-V-2 |   InternVL2-2B |   XinYuan-VL-2B |   Qwen2-VL-2B-Instruct |   Infinity-VL-2B |
+| Test sets       |   MiniCPM-V-2 |   InternVL2-2B |   XinYuan-VL-2B |   Qwen2-VL-2B-Instruct |   Infinity-VL-2B-llava-qwen |
 |:----------------:|:--------------:|:---------------:|:----------------:|:-----------------------:|:-----------------:|
 | MMMU\_DEV\_VAL    |         39.56 |          34.89 |           43.56 |                  41.67 |            **45.89** |
 | MMStar          |         41.6  |          50.2  |           51.87 |                  47.8  |            **54.4**  |