Kronos-base (MLX)
Apple MLX port of NeoQuasar/Kronos-base — the 102M-parameter Kronos variant. d_model=832, n_layers=12, max_context=512. Pair with gxcsoccer/kronos-mlx-tokenizer-base.
Usage
from kronos_mlx import Kronos, KronosTokenizer, KronosPredictor
tokenizer = KronosTokenizer.from_pretrained("gxcsoccer/kronos-mlx-tokenizer-base")
model = Kronos.from_pretrained("gxcsoccer/kronos-mlx-base")
predictor = KronosPredictor(model, tokenizer, max_context=512)
For 8-bit Linear weight quantization (390 MB → ~115 MB, -71 %):
model = Kronos.from_pretrained("gxcsoccer/kronos-mlx-base", bits=8)
8-bit on Kronos-base is much higher fidelity than on Kronos-small thanks to the larger model's redundancy — recommended for memory-constrained Apple Silicon.
Original
- Upstream: shiyu-coder/Kronos (AAAI 2026)
- PyTorch weights: NeoQuasar/Kronos-base
- MLX port: github.com/gxcsoccer/kronos-mlx
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Model size
0.1B params
Tensor type
F32
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Base model
NeoQuasar/Kronos-base
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir kronos-mlx-base gxcsoccer/kronos-mlx-base