Tarsier Model Card

Model details

Model type: Tarsier-34b is 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 (SOTA results on 6 open benchmarks).

Model date: Tarsier-34b was trained in June 2024.

Paper or resources for more information:

github repo: https://github.com/bytedance/tarsier
paper link: https://arxiv.org/abs/2407.00634

License

NousResearch/Nous-Hermes-2-Yi-34B license.

Where to send questions or comments about the model: https://github.com/bytedance/tarsier/issues

Intended use

Primary intended uses: The primary use of Tarsier is research on large multimodal models, especially video description.

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

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.

Evaluation dataset

A challenging video desription dataset: DREAM-1K
Multi-choice VQA: MVBench, NeXT-QA and Egoschema
Open-ended VQA: MSVD-QA, MSR-VTT-QA, ActivityNet-QA and TGIF-QA
Video Caption: MSVD-Caption, MSRVTT-Caption, VATEX

How to Use

see https://github.com/bytedance/tarsier?tab=readme-ov-file#usage