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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"| 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. |
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) float32research.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.