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.