Vision-CAIR/Tempo-6B-Stage2

Model

0

stars

5

commits

1

linked in READMEs

Apr 15, 2026

updated

custom_code
long-video-understanding
multimodal
pytorch
tempo_qwen
video-text-to-text
vision-language-model

README

Tempo-6B-Stage2 (Pre-Long-Context SFT)

This repository contains the Stage 2 intermediate checkpoint for Tempo-6B.

Unlike earlier training stages, this checkpoint is fully capable of direct inference. It has completed short-video and image instruction tuning, making it a strong baseline for general multimodal understanding. However, it has not undergone our final Stage 3 Long-Context SFT.

πŸš€ When to use this checkpoint?

  • Custom Fine-Tuning: An ideal starting point if you want to apply your own long-context SFT curriculum or adapt the model to specialized domains.

πŸ“Š Ablation Performance

To demonstrate its capabilities, here is the performance of this Stage 2 checkpoint compared to our final Tempo-6B model:

Model SettingLongVideoBenchMLVUVideo-MME (Overall)Video-MME (Long)LVBench
Tempo-6B-Stage2 (w/o ATA)61.467.266.156.347.3
Tempo-6B-Final (w/o ATA)62.873.567.056.251.1
Tempo-6B Final (w/ ATA)65.175.267.757.052.3

Note: For a detailed analysis, please check our paper's Ablation Study A (Progressive Training Curriculum).

For the final long video understanding performance, please use our final weights:

Contributors

Vision-CAIR/Tempo-6B-Stage2

Model

0

stars

5

commits

1

linked in READMEs

Apr 15, 2026

updated

custom_code
long-video-understanding
multimodal
pytorch
tempo_qwen
video-text-to-text
vision-language-model

README

Tempo-6B-Stage2 (Pre-Long-Context SFT)

This repository contains the Stage 2 intermediate checkpoint for Tempo-6B.

Unlike earlier training stages, this checkpoint is fully capable of direct inference. It has completed short-video and image instruction tuning, making it a strong baseline for general multimodal understanding. However, it has not undergone our final Stage 3 Long-Context SFT.

πŸš€ When to use this checkpoint?

  • Custom Fine-Tuning: An ideal starting point if you want to apply your own long-context SFT curriculum or adapt the model to specialized domains.

πŸ“Š Ablation Performance

To demonstrate its capabilities, here is the performance of this Stage 2 checkpoint compared to our final Tempo-6B model:

Model SettingLongVideoBenchMLVUVideo-MME (Overall)Video-MME (Long)LVBench
Tempo-6B-Stage2 (w/o ATA)61.467.266.156.347.3
Tempo-6B-Final (w/o ATA)62.873.567.056.251.1
Tempo-6B Final (w/ ATA)65.175.267.757.052.3

Note: For a detailed analysis, please check our paper's Ablation Study A (Progressive Training Curriculum).

For the final long video understanding performance, please use our final weights:

Contributors