update

README.md

@@ -7,11 +7,25 @@ pipeline_tag: image-to-text
 
 MMAlaya2 fine-tunes 20 LoRA modules based on the InternVL-Chat-V1-5 model. These fine-tuned LoRA modules are then merged with the InternVL-Chat-V1-5 model using the PEFT model merging method, TIES.
 
-You can find the inference code [here](https://github.com/open-compass/VLMEvalKit/pull/399/files) (PR still in preparation).
+You can find the inference code [here](https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/vlm/mmalaya.py).
 
 The [MMBench](https://mmbench.opencompass.org.cn/) benchmark contains 20 categories in the `mmbench_dev_cn_20231003.tsv` dataset. For each category, we first use CoT (Chain of Thought) consistency with the InternVL-Chat-V1-5 model to prepare the training dataset. For specific categories like nature_relation, image_emotion, image_scene, action_recognition, and image_style, we analyze the bad cases made by the InternVL-Chat-V1-5 model. We then prepare images and QA text from online sources to address these issues.
 
-After fine-tuning the 20 LoRAs, they are merged with the InternVL-Chat-V1-5 model using the TIES method. The average score on the mmbench_test_cn_20231003.tsv benchmark reached 82.1, which is higher than the 80.7 score of the InternVL-Chat-V1-5 model. This places it in the top 4, matching the performance of GPT-4. We found this result noteworthy. This score was obtained from the uploaded inference file `submission.xlsx`. The online leaderboard average score is still being prepared. As a result, we are sharing this model publicly.
+After fine-tuning the 20 LoRAs, they are merged with the InternVL-Chat-V1-5 model using the TIES method. 
+
+Thank you to the OpenCompass MMBench team for updating the [leaderboard](https://mmbench.opencompass.org.cn/leaderboard) on August 29, 2024. I've collected the ranks and scores from the leaderboard for reference. For example, a ranking of "7/82.1" indicates a 7th place finish with a score of 82.1 in that category. I chose GPT-4o (0513, detail-high) because it is the best-performing GPT-4o model in the MMBench Test (CN).
+
+| Model      | MMBench Test (CN) |MMBench v1.1 Test (CN) |CCBench dev |MMBench Test |MMBench v1.1 Test |
+| ----------- | ----------- | ----------- | ----------- | ----------- |----------- |
+| GPT-4o (0513, detail-high)   | 4/82.1        |    5/81.5   |   7/71.2    |   4/83.4    |   5/83    |
+| MMAlaya2      | 7/82.1       |    8/79.7   |   8/70    |    9/82.5   |    9/80.6   |
+| InternVL-Chat-V1.5   | 14/80.7        |    15/79.1   |   9/69.8    |   11/82.3    |   10/80.3    |
+
+
+The average score on the MMBench Test (CN) reached 82.1, surpassing the InternVL-Chat-V1-5 model's score of 80.7 by 1.4 points. This achievement places it in the top 4, on par with the performance of GPT-4o. Additionally, scores on the other four benchmarks—MMBench v1.1 Test (CN), CCBench dev, MMBench Test, and MMBench v1.1 Test—have also improved by 0.2 to 0.6 points, bringing them closer to GPT-4o's performance.
+
+We found this result noteworthy. As a result, we are sharing this model publicly.
+
 
 # License