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gxcsoccer/kronos-mlx-small

sourceHugging Facemitupdated 5mo agoView on Hugging Face
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Kronos-small (MLX)

Apple MLX port of `NeoQuasar/Kronos-small` — a 24.7M-parameter foundation model for financial K-line (candlestick) forecasting (AAAI 2026).

The architecture and weights are unchanged from the upstream PyTorch checkpoint. The only difference is that PyTorch-only buffer tensors (*.rotary.inv_freq, tokenizer.bsq.basis, tokenizer.bsq.group_codebook, …) have been pruned because the `kronos-mlx` package recomputes them from scratch in __init__.

Usage

bash
pip install kronos-mlx
python
from kronos_mlx import Kronos, KronosTokenizer, KronosPredictor

tokenizer = KronosTokenizer.from_pretrained("gxcsoccer/kronos-mlx-tokenizer-base")
model     = Kronos.from_pretrained("gxcsoccer/kronos-mlx-small")

predictor = KronosPredictor(model, tokenizer, max_context=512)

pred_df = predictor.predict(
    df=x_df,
    x_timestamp=x_timestamp,
    y_timestamp=y_timestamp,
    pred_len=120,
)

For 8-bit weight quantization (~70% memory reduction on Linear layers, modest quality loss):

python
model = Kronos.from_pretrained("gxcsoccer/kronos-mlx-small", bits=8)

Numerical parity

Bit-exact integer paths and float divergence within ~5e-5 (max) of the upstream PyTorch reference under identical inputs. See the parity test suite for the validation methodology.

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