---
language:
- en
license: other
license_name: pleius-internal
tags:
- onnx
- conditional-text-generation
- video-feedback
- distillation
- creator-tools
---

# cortexa-create-feedback (distilled student)

A ~4.4M-parameter conditional decoder distilled from
`M725/cortexa-create-scorer` outputs. Takes CLIP-ViT-B/32 vision
features (mean-pooled across video frames, 768-d) + the 5 Create pillar
scores and emits a creator-vernacular phrase chain about the short-form
video:

```
"first frame slaps | feels intentional"
"thumb stopping | shareable"
"filler | feels rushed | first frame is nothing"
"feels off beat | slow open | no payoff"
```

## Files

| file | purpose |
|---|---|
| `student_int8.onnx` | TinyTransformer decoder, 4 layers / 256-dim / 4 heads, INT8 dynamic-quantized. 6.9 MB. |
| `tokenizer.json`    | Whole-phrase tokenizer (vocab ~138; specials `<pad>`, `<bos>`, `<eos>`, `<sep>`). |
| `config.json`       | Encoder dim, pillar names, vocab size, special-token ids. |

## Inference shape

```
inputs:
  encoder_feats   (1, 768)  float32   # mean-pooled CLIP-ViT-B/32 vision across frames
  scores          (1, 5)    float32   # [hook, hold, algorithmic_fit, brand_lift, overall] in [0,1]
  scores_present  (1,)      float32   # 1.0 anchored, 0.0 fast-mode
  input_ids       (1, T)    int64
outputs:
  logits          (1, T, V) float32
```

Same sampling recommendation as `cortexa-marketing-feedback`: temperature
0.8 + top-k 20 + SEP-veto.

## Training

6k phrase triples from 3 real short-form videos
(`public/create-tutorial/*.mp4`) + 1997 synthetic "videos" built by
random-crop + color jitter over COCO stills (each frame goes through
cortexa_v10 separately, so the per-frame curve has real variation). 15
epochs. Val loss 2.39 → 1.97. See
`research/distill_students/train_create.py` in the app repo.

## License

Pleius internal — see https://pleius.com. Not for redistribution.