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---
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.
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