---
base_model: nvidia/Cosmos-Reason2-2B
license: other
tags:
  - egonormia
  - social-norms
  - video-understanding
  - sft
  - multitask
---

# EgoNormia-Cosmos-Reason2-2B-v3

Multi-task SFT fine-tune of [nvidia/Cosmos-Reason2-2B](https://huggingface.co/nvidia/Cosmos-Reason2-2B) on the [EgoNormia](https://opensocial.world) social norm benchmark. Trained jointly on action selection, justification selection, and sensibility identification.

## Training

| Parameter | Value |
|-----------|-------|
| Base model | nvidia/Cosmos-Reason2-2B (Qwen3-VL-2B) |
| Tasks | Action + Justification + Sensibility (multi-task) |
| Train samples | 4959 (1653 per task x 3) |
| Epochs | 3 |
| Global batch | 64 (8 replicas x 8 per replica) |
| Learning rate | 1e-5 (cosine decay, 3% warmup) |
| Video input | `video_prev.mp4`, 8 frames |
| Hardware | 8x A100-SXM4-80GB |
| Best checkpoint | `step_150` / 231 total steps |

## Evaluation (200 verified test samples)

| Model | Action | Justification | Both | S-IoU | Parse(A/J/S) |
|-------|--------|---------------|------|-------|--------------|
| Zero-shot | 58.5% | 81.5% | 51.0% | 0.516 | -/-/- |
| v2 best | 82.0% | 84.0% | 71.5% | 0.0%* | 100/100/0% |
| **v3 `step_150`** | 79.5% | **96.5%** | **77.0%** | **0.630** | 100/100/100% |

*v2 S-IoU = 0% because the model collapses on the sensibility output format.

## Notes

- v3 is the first version that fully solves the output-format collapse from v2.
- Relative to v2, it trades a small amount of peak action accuracy for large gains in justification quality, sensibility parsing, and overall benchmark completeness.
- Later versions mainly explore how to improve v3's action / robustness tradeoff without breaking formatting.

## Usage

```python
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "robertzty/EgoNormia-Cosmos-Reason2-2B-v3",
    torch_dtype="bfloat16",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("robertzty/EgoNormia-Cosmos-Reason2-2B-v3")
```