omni-research/Tarsier-34b · Hugging Face (original) (raw)

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