---
title: ASMv2
canonical_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/ASMv2"
md_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/ASMv2.md"
repository: Shanghai_AI_Laboratory/ASMv2
last_updated: 2024-12-30
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llava
architectures:
  - LlavaLlamaForCausalLM
library_name:
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task vlm"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
downloads: 1125
stars: 0
---

# ASMv2

> ASMv2 - Shanghai_AI_Laboratory 在 ModelScope 开源的模型。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…

Shanghai_AI_Laboratory/ASMv2 是 ModelScope 魔搭社区上的text-generation模型，采用 apache-2.0 许可，可用 deploy_task vlm、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: Shanghai_AI_Laboratory/ASMv2
- **License**: apache-2.0
- **Tasks**: text-generation
- **Inference backends**: deploy_task vlm, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2
- **Downloads**: 1125
- **Stars**: 0
- **Last updated**: 2024-12-30

Source: https://www.modelscope.cn/models/Shanghai_AI_Laboratory/ASMv2

---

# 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 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 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 benchmarks, 2 region captioning benchmarks, 1 referring question answering benchmark, 1 scene graph generation benchmark, and 1 relation comprehension benchmark.