Tempo 6B Stage2
This repository contains the Stage 2 intermediate checkpoint for Tempo-6B.
Source du modèle
- Type
- Text model
- Taille du fichier
- 12,2 GB
- Modèle de base
- Qwen/Qwen3-4B
- Version
- vpytorch_model
- Mis à jour
- 24 oct. 2026
- Téléchargements
- 9
- Source
- Hugging Face
Description de la source
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.
🚀 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.
📊 Ablation Performance
To demonstrate its capabilities, here is the performance of this Stage 2 checkpoint compared to our final Tempo-6B model:
| Model Setting | LongVideoBench | MLVU | Video-MME (Overall) | Video-MME (Long) | LVBench |
|---|---|---|---|---|---|
| Tempo-6B-Stage2 (w/o ATA) | 61.4 | 67.2 | 66.1 | 56.3 | 47.3 |
| Tempo-6B-Final (w/o ATA) | 62.8 | 73.5 | 67.0 | 56.2 | 51.1 |
| Tempo-6B Final (w/ ATA) | 65.1 | 75.2 | 67.7 | 57.0 | 52.3 |
Note: For a detailed analysis, please check our paper's Ablation Study A (Progressive Training Curriculum).
🔗 Links
For the final long video understanding performance, please use our final weights:
- Final Model: Vision-CAIR/Tempo-6B
- GitHub Code: FeiElysia/Tempo
- Paper: Small Vision-Language Models are Smart Compressors for Long Video Understanding
Sources
1 source
Sources
1 sourceVérifié 24 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- language: - en license: apache-2.0 pipeline_tag: video-text-to-text base_model: - Qwen/Qwen3-VL-2B-Instruct - Qwen/Qwen3-4B tags: - vision-language-model - long-video-understanding - multimodal - pytorch - custom_code --- # 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. ### 🚀 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. ### 📊 Ablation Performance To demonstrate its capabilities, here is the performance of this Stage 2 checkpoint compared to our final Tempo-6B model: | Model Setting | LongVideoBench | MLVU | Video-MME (Overall) | Video-MME (Long) | LVBench | | :--- | :---: | :---: | :---: | :---: | :---: | | **Tempo-6B-Stage2** (w/o ATA) | 61.4 | 67.2 | 66.1 | 56.3 | 47.3 | | **Tempo-6B-Final** (w/o ATA) | 62.8 | 73.5 | 67.0 | 56.2 | 51.1 | | **Tempo-6B Final** (w/ ATA) | **65.1** | **75.2** | **67.7** | **57.0** | **52.3** | *Note: For a detailed analysis, please check our [paper's Ablation Study A](https://arxiv.org/pdf/2604.08120) (Progressive Training Curriculum).* ### 🔗 Links For the final long video understanding performance, please use our final weights: - **Final Model:** [Vision-CAIR/Tempo-6B](https://huggingface.co/Vision-CAIR/Tempo-6B) - **GitHub Code:** [FeiElysia/Tempo](https://github.com/FeiElysia/Tempo) - **Paper:** [Small Vision-Language Models are Smart Compressors for Long Video Understanding](https://huggingface.co/papers/2604.08120)
Source context: 9 downloads · 0 likes · Pipeline video-text-to-text · Repo Vision-CAIR/Tempo-6B-Stage2
Source context: 5 downloads · 0 likes · Pipeline video-text-to-text · Repo Vision-CAIR/Tempo-6B-Stage2
