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1from model.direction_kronos import DirectionKronos
2import torch.nn.functional as F
3import numpy as np
4
5model = DirectionKronos.load(
6 head_path="loginil/Kronos-small-btc-15m-dir-zeroshot", # or pathe
7 backbone_path="NeoQuasar/Kronos-small", # for zeroshot
8 # backbone_path="loginil/Kronos-small-btc-15m-v3", # for pathe
9 tokenizer_path="NeoQuasar/Kronos-Tokenizer-base",
10)
11# x: (B, 90, 6) instance-normalized OHLCV+amt
12# stamp: (B, 90, 5) time features
13probs = model.predict_proba(x, stamp) # (B, 2) [P(DOWN), P(UP)]