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README.md

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 license: apache-2.0
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+# ASM-FT Model Card
+
+## Model details
+
+**Model type:**
+ASM is a unified vision-language foundation model for open-world panoptic visual recognition and understanding. Aligning with LLMs, it supports versatile generation tasks, demonstrating impressive region comprehension capability.
+
+**Model date:**
+ASM was trained in July 2023.
+
+**Paper or resources for more information:**
+https://github.com/OpenGVLab/all-seeing
+
+## License
+ASM 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 ASM 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 [AS-1B](https://huggingface.co/datasets/Weiyun1025/AS-100M/tree/main) and [Laion-COCO](https://huggingface.co/datasets/laion/laion-coco).
+
+The finetuning phase employs [AS-Core](https://huggingface.co/datasets/Weiyun1025/AS-Core), [RefCOCOg](https://github.com/lichengunc/refer), [VG](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html), [LLaVA-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K), [COCO Caption](https://cocodataset.org/#home), [TextCaps](https://textvqa.org/textcaps/), [VQAv2](https://visualqa.org/), and [GQA](https://cs.stanford.edu/people/dorarad/gqa/).
+
+## Evaluation dataset
+A collection of 4 benchmarks, including 2 image captioning benchmarks, and 2 region captioning benchmarks.