Add detailed model card

README.md

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+---
+base_model: nvidia/Cosmos-Reason2-2B
+license: other
+tags:
+  - egonormia
+  - social-norms
+  - video-understanding
+  - sft
+  - multitask
+  - short-cot
+---
+
+# EgoNormia-Cosmos-Reason2-2B-v5-shortcot
+
+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. This v5 variant keeps the same 3-task setup as v4, but compresses the reasoning traces into short 1-sentence CoT supervision.
+
+## Training
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | nvidia/Cosmos-Reason2-2B (Qwen3-VL-2B) |
+| Tasks | Action + Justification + Sensibility (multi-task) |
+| Train samples | 4959 |
+| Training file | `data/egonormia_llava_shortcot_train.json` |
+| CoT style | Short CoT, 1-sentence distilled traces |
+| CoT length | median ~25 words (compressed from ~64 words) |
+| Epochs | 3 |
+| Global batch | 64 (8 replicas x 8 per replica) |
+| Learning rate | 1e-5 (cosine decay, 3% warmup) |
+| Context length | 8192 |
+| Video input | `video_prev.mp4`, 8 frames |
+| Hardware | 8x A100-SXM4-80GB |
+| Seed 1 run dir | `outputs/egonormia_sft/20260228141559/` |
+| Seed 2 run dir | `outputs/egonormia_sft/20260301002022/` |
+| Uploaded checkpoint | seed2 `step_150` |
+
+## Evaluation (200 verified test samples)
+
+| Model | Action | Justification | Both | S-IoU |
+|-------|--------|---------------|------|-------|
+| Zero-shot | 58.5% | 81.5% | 51.0% | 0.516 |
+| v3 best (`step_175`) | 78.0% | **97.0%** | 77.0% | **0.664** |
+| v5 seed1 (`step_155`) | 80.5% | 95.5% | 78.5% | 0.618 |
+| **v5 seed2 (`step_150`)** | **82.0%** | 95.5% | **78.5%** | 0.634 |
+
+Average over the two seed-wise best checkpoints:
+- Action: 81.25%
+- Justification: 95.5%
+- Both: 78.5%
+- S-IoU: 0.626
+
+## Robustness (option shuffle)
+
+| Checkpoint | Delta Action | Delta S-IoU | Sign test p | Verdict |
+|------------|--------------|-------------|-------------|---------|
+| seed1 `step_155` | -2.0pt | -0.035 | 0.585 | pass |
+| seed2 `step_150` | -5.0pt | -0.027 | 0.076 | pass |
+
+## Notes
+
+- v5 recovers the robustness lost in v4 while keeping stronger action accuracy than v3.
+- Best S-IoU still trails v3 (`0.634` vs `0.664`), so the gain is mainly in action / joint accuracy tradeoff rather than sensibility quality.
+- On this run family, explicit think-mode inference hurts performance: for seed2, no-think `step_150` reaches 82.0% action / 78.5% both, while think mode peaks lower at 78.0% action / 72.5% both.
+
+## Usage
+
+```python
+from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
+
+model = Qwen3VLForConditionalGeneration.from_pretrained(
+    "robertzty/EgoNormia-Cosmos-Reason2-2B-v5-shortcot",
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained("robertzty/EgoNormia-Cosmos-Reason2-2B-v5-shortcot")
+```