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README.md

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+# ASMv2 Model Card
+
+## Model details
+
+**Model type:**
+ASMv2 is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on multimodal instruction-following data.
+It integrates the Scene Graph Conversation (SGC) ability while maintaining powerful general capabilities.
+This model is also endowed with grounding and referring capabilities, exhibiting state-of-the-art performance on region-level tasks, and can be naturally adapted to the Scene Graph Generation task in an open-ended manner.
+
+**Model date:**
+ASMv2 was trained in January 2024.
+
+**Paper or resources for more information:**
+https://github.com/OpenGVLab/all-seeing
+
+## License
+ASMv2 is open-sourced under the Apache License 2.0, 
+
+**Where to send questions or comments about the model:**
+https://github.com/OpenGVLab/all-seeing/issues
+
+## Intended use
+**Primary intended uses:**
+The primary use of ASMv2 is research on large multimodal models and chatbots.
+
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+
+## Training dataset
+The pretrain phase employs [5M filtered samples](https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_filtered.json) from CC12M, [10M filtered samples](https://huggingface.co/datasets/Weiyun1025/AS-V2/blob/main/as_pretrain_10m.json) from AS-1B, and 15M filtered samples from [GRiT](https://huggingface.co/datasets/zzliang/GRIT).
+
+The instruction-tuning phase employs [4M samples](https://huggingface.co/datasets/Weiyun1025/AS-V2/blob/main/as_mix_4m.json) collected from a variety of sources, including image-level datasets
+
+See [here](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing-v2#training) for more details.
+
+## Evaluation dataset
+A collection of 20 benchmarks, including 5 academic VQA benchmarks, 7 multimodal benchmarks specifically proposed for instruction-following LMMs, 3 referring expression comprehension benchmark, 2 region captioning benchmark, 1 referring question answering benchmark, 1 scene graph generation benchmark, and 1 relation comprehension benchmark.