|
--- |
|
inference: false |
|
license: apache-2.0 |
|
pipeline_tag: image-text-to-text |
|
--- |
|
|
|
<br> |
|
<br> |
|
|
|
# LLaVA Model Card |
|
|
|
## Model details |
|
|
|
**Model type:** |
|
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. |
|
It is an auto-regressive language model, based on the transformer architecture. |
|
Base LLM: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) |
|
|
|
**Model date:** |
|
LLaVA-v1.6-Mistral-7B was trained in December 2023. |
|
|
|
**Paper or resources for more information:** |
|
https://llava-vl.github.io/ |
|
|
|
## License |
|
[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) license. |
|
|
|
**Where to send questions or comments about the model:** |
|
https://github.com/haotian-liu/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 |
|
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. |
|
- 158K GPT-generated multimodal instruction-following data. |
|
- 500K academic-task-oriented VQA data mixture. |
|
- 50K GPT-4V data mixture. |
|
- 40K ShareGPT data. |
|
|
|
## Evaluation dataset |
|
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |
