| | --- |
| | license: llama2 |
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| | |
| | # Tarsier Model Card |
| | ## Model details |
| | **Model type:** |
| | Tarsier-7b is one of the Tarsier family -- an open-source large-scale video-language models, which is designed to generate high-quality video descriptions, together with good capability of general video understanding (Tarsier-34b gains SOTA results on 6 open benchmarks). Base LLM: [liuhaotian/llava-v1.6-vicuna-7b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b) |
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| | **Model date:** |
| | Tarsier-7b was trained in June 2024. |
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| | **Paper or resources for more information:** |
| | - github repo: https://github.com/bytedance/tarsier |
| | - paper link: https://arxiv.org/abs/2407.00634 |
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|
| | ## License |
| | lmsys/vicuna-7b-v1.5 license. |
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| | **Where to send questions or comments about the model:** |
| | https://github.com/bytedance/tarsier/issues |
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|
| | ## Intended use |
| | **Primary intended uses:** |
| | The primary use of Tarsier is research on large multimodal models, especially video description. |
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| | **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. |
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| | ## Training dataset |
| | Tarsier tasks a two-stage training strategy. |
| | - Stage-1: Multi-task Pre-training on 13M data |
| | - Stage-2: Multi-grained Instruction Tuning on 500K data |
| | |
| | In both stages, we freeze ViT and train all the parameters of projection layer and LLM. |
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| | ## Evaluation dataset |
| | - A challenging video desription dataset: [DREAM-1K](https://huggingface.co/datasets/omni-research/DREAM-1K) |
| | - Multi-choice VQA: [MVBench](https://huggingface.co/datasets/OpenGVLab/MVBench), [NeXT-QA](https://github.com/doc-doc/NExT-QA) and [Egoschema](https://drive.google.com/drive/folders/1SS0VVz8rML1e5gWq7D7VtP1oxE2UtmhQ) |
| | - Open-ended VQA: [MSVD-QA](https://opendatalab.com/OpenDataLab/MSVD), [MSR-VTT-QA](https://opendatalab.com/OpenDataLab/MSR-VTT), [ActivityNet-QA](https://github.com/MILVLG/activitynet-qa) and [TGIF-QA](https://opendatalab.com/OpenDataLab/TGIF-QA) |
| | - Video Caption: [MSVD-Caption](https://opendatalab.com/OpenDataLab/MSVD), [MSRVTT-Caption](https://opendatalab.com/OpenDataLab/MSR-VTT), [VATEX](https://eric-xw.github.io/vatex-website/about.html) |
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| | ## How to Use |
| | see https://github.com/bytedance/tarsier?tab=readme-ov-file#usage |
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