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README.md

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 ---
 license: apache-2.0
+pipeline_tag: image-to-text
 ---
+
+# MMAlaya2
+
+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/blob/main/vlmeval/vlm/mmalaya.py#L8).
+
+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 82.2, which we found noteworthy. As a result, we are sharing this model publicly.
+
+# License
+
+This project is released under the MIT license, in alignment with the InternVL-Chat-V1-5 model's license. InternLM2, however, is licensed under the Apache-2.0 license.
+
+# Citation
+
+If you find this project useful in your research, please consider citing:
+
+```bibtex
+@misc{datacanvas2024mmalaya2,
+    author = {DataCanvas Ltd.},
+    title = {MMAlaya2},
+    year = {2024},
+    howpublished = {\url{https://huggingface.co/DataCanvas/MMAlaya2}},
+}
+```