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About
Apollo 3B - An Exploration of Video Understanding in Large Multimodal Models

Example Output
Prompt:
"Describe this video in detail"
Output
The video features a lone astronaut in a white spacesuit, equipped with a helmet and gloves, standing on the moon's surface. The backdrop is dominated by a large, detailed image of the moon, set against a starry space. The astronaut begins to run across the lunar terrain, leaving footprints behind. As he runs, the camera angle shifts to reveal more of the moon's rugged landscape. The astronaut continues his run until he reaches the edge of the frame, where he leaps into the vast expanse of space, floating away from the moon. Throughout the sequence, the moon remains a constant and prominent feature in the background, emphasizing the astronaut's journey into the cosmos. The video captures the astronaut's solitary trek across the moon's surface and his subsequent leap into the unknown, symbolizing humanity's boundless curiosity and spirit of exploration. The astronaut's actions are depicted with precision and grace, highlighting the beauty and isolation of space travel. The video concludes with the astronaut floating freely in space, surrounded by the endless void of space, underscoring the awe-inspiring scale and mystery of the universe. The astronaut's journey serves as a powerful metaphor for human ambition and the quest for knowledge, encapsulating the essence of space exploration. The
Performance Metrics
4.28s
Prediction Time
58.54s
Total Time
All Input Parameters
{ "top_p": 0.7, "video": "https://replicate.delivery/pbxt/M9jsPwn8UqBPUCM6xiFEGcyfaZWXDwJ6EwcVqLPjKzcmctgn/replicate-prediction-jv0zykaqvhrmc0ckt4vtepaep4.mp4", "prompt": "Describe this video in detail", "temperature": 0.4, "max_new_tokens": 256 }
Input Parameters
- top_p
- Top-p sampling probability
- video (required)
- Input video file
- prompt
- Question or prompt about the video
- temperature
- Sampling temperature
- max_new_tokens
- Maximum number of tokens to generate
Output Schema
Output
Example Execution Logs
/root/.pyenv/versions/3.11.10/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /root/.pyenv/versions/3.11.10/lib/python3.11/site-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Version Details
- Version ID
ed5ec3233f7a4331a2b0d33b8a02e62057a335e1a03107d56db9fac10cf3ce55
- Version Created
- December 16, 2024