Hi Cosmos3 authors,
Thank you for the great work and for releasing the Cosmos3 models and code!
I am trying to reproduce the reported MVBench performance for nvidia/Cosmos3-Nano (reasoner). However, my result is much lower than the number reported in the paper:
- Reported MVBench: 73.2%
- My reproduction: 62.0%
My evaluation configuration is:
| Param |
Value |
| model |
nvidia/Cosmos3-Nano (reasoner) |
| thinking |
on (temp=0.6, top_p=0.95, top_k=20, seed=1234) |
| max_new_tokens |
8192 |
| max_time |
180 s/sample hard cap |
| attn |
sdpa, bf16 |
| fps |
2.0 |
| max_frames |
128 |
| max_tokens_per_frame |
768 |
| frame rounding |
even (FRAME_FACTOR=2) |
Could you please clarify the exact evaluation configuration used for the MVBench result in the paper?
In particular, I would like to ask:
- Was the reported MVBench result evaluated with thinking enabled or disabled?
- What FPS, maximum number of frames, and maximum tokens per frame were used?
- Was there any benchmark-specific prompt template or answer extraction rule?
- Were any special decoding settings used, such as greedy decoding vs. sampling?
- Is
max_frames=128 too small for reproducing the reported MVBench result?
Any suggestions on the correct configuration would be greatly appreciated.
Thank you very much!
Best,
Xin
Hi Cosmos3 authors,
Thank you for the great work and for releasing the Cosmos3 models and code!
I am trying to reproduce the reported MVBench performance for
nvidia/Cosmos3-Nano (reasoner). However, my result is much lower than the number reported in the paper:My evaluation configuration is:
nvidia/Cosmos3-Nano (reasoner)temp=0.6,top_p=0.95,top_k=20,seed=1234)FRAME_FACTOR=2)Could you please clarify the exact evaluation configuration used for the MVBench result in the paper?
In particular, I would like to ask:
max_frames=128too small for reproducing the reported MVBench result?Any suggestions on the correct configuration would be greatly appreciated.
Thank you very much!
Best,
Xin