| license: mit | |
| library_name: kronos-mlx | |
| pipeline_tag: time-series-forecasting | |
| tags: | |
| - mlx | |
| - apple-silicon | |
| - finance | |
| - kronos | |
| - time-series | |
| base_model: NeoQuasar/Kronos-base | |
| # Kronos-base (MLX) | |
| Apple [MLX](https://github.com/ml-explore/mlx) port of [`NeoQuasar/Kronos-base`](https://huggingface.co/NeoQuasar/Kronos-base) — the 102M-parameter Kronos variant. d_model=832, n_layers=12, max_context=512. Pair with [`gxcsoccer/kronos-mlx-tokenizer-base`](https://huggingface.co/gxcsoccer/kronos-mlx-tokenizer-base). | |
| ## Usage | |
| ```python | |
| 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 %**): | |
| ```python | |
| 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](https://github.com/shiyu-coder/Kronos) (AAAI 2026) | |
| - PyTorch weights: [NeoQuasar/Kronos-base](https://huggingface.co/NeoQuasar/Kronos-base) | |
| - MLX port: [github.com/gxcsoccer/kronos-mlx](https://github.com/gxcsoccer/kronos-mlx) | |