--- license: apache-2.0 --- # ASMv2 Model Card This is a pretrained checkpoint, you can use it to instruct tune your multimodal models. Check out the instructions [here](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing-v2#stage2-finetuning). ## Model details **Model type:** ASMv2 is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on multimodal instruction-following data. It integrates the Relation Conversation (ReC) 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-Pretrain was trained in January 2024. **Paper or resources for more information:** https://github.com/OpenGVLab/all-seeing ## License ASMv2-Pretrain 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). See [here](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing-v2#training) for more details.