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

# cortexa-marketing-feedback (distilled student)

A ~4.4M-parameter conditional decoder distilled from
`M725/cortexa-marketing-scorer` outputs. Takes CLIP-ViT-B/32 vision
features (768-d) + the 4 Marketing pillar scores (or a "no-scores"
sentinel for fast mode) and emits a creator-vernacular phrase chain:

```
"scroll stopping | clear cta | thumb stopping"
"forgettable | looks clean | low contrast text"
"lazy design | model looks fake | low contrast"
```

The student is meant to be the *feedback callout* shown on the result
screen for paid users — plain-language pros and cons that go alongside
the scorer's numeric output.

## 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 ~115; specials `<pad>`, `<bos>`, `<eos>`, `<sep>`). |
| `config.json`       | Encoder dim, pillar names, vocab size, special-token ids — read by the TS/JS runtime to shape inputs. |

## Inference shape

```
inputs:
  encoder_feats   (1, 768)  float32   # mean-pooled CLIP-ViT-B/32 vision output
  scores          (1, 4)    float32   # [universal_appeal, demographic_appeal, audience_drive, engagement] in [0,1]
  scores_present  (1,)      float32   # 1.0 anchored, 0.0 fast-mode
  input_ids       (1, T)    int64     # decoder context
outputs:
  logits          (1, T, V) float32
```

Greedy decode works; **temperature 0.8 + top-k 20 + SEP-veto** is the
recommended sampling config when running on more than one input
(prevents the greedy "forgettable | forgettable | forgettable" collapse
the v0 model exhibited).

## Training

15k phrase triples from 5k COCO photos. Each photo scored locally
against the cortexa_v10 head; phrase chains generated by
`research.distill_adjectives.phrase_rules.scores_to_phrase`. 12 epochs,
AdamW, cosine schedule. Val loss 2.31 → 1.87. See
`research/distill_students/train_marketing.py` in the app repo.

## License

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