|
--- |
|
inference: false |
|
pipeline_tag: image-text-to-text |
|
--- |
|
<br> |
|
<br> |
|
|
|
# AVG-LLaVA Model Card |
|
|
|
## Model details |
|
|
|
**Model type:** |
|
AVG-LLaVA is an open-source LMM that can adaptively select the appropriate visual granularity |
|
based on the input image and instruction. |
|
It is an auto-regressive language model, based on the transformer architecture. |
|
Base LLM: [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) |
|
|
|
**Paper or resources for more information:** |
|
https://arxiv.org/abs/2410.02745 |
|
|
|
## License |
|
Llama 2 is licensed under the LLAMA 2 Community License, |
|
Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
|
|
|
**Where to send questions or comments about the model:** |
|
https://github.com/DeepLearnXMU/AVG-LLaVA/issues |
|
|
|
## Intended use |
|
**Primary intended uses:** |
|
The primary use of LLaVA 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 |
|
- ShareGPT4V Mix665K |
|
- 200K GPT4V-generated instruction data (ALLaVA) |
|
- 200K various VQA data |
|
|
|
## Evaluation dataset |
|
A collection of 11 benchmarks, including general VQA benchmarks, text-oriented VQA benchmarks, and general multimodal benchmarks. |
