basal-2.0-mini-MLX-8bit

MLX 8-bit weights for Apple Silicon of basal-2.0-mini (1.5B, base Bielik-1.5B-v3.0-Instruct). Same model, same typed-decision API through basal-serve; the calibration files this folder ships are listed below.

Measured against bf16

Agreement is the share of decisions where the port picks the same option as the bf16 release model on the development set dev v2 (MLX rows: every 2nd dev v2 decision, 1,700 decisions). Bars for this tier: agreement 2-10 >= 0.9161, agreement at reference margin >= 0.2 >= 0.9858, accuracy within 1 point of bf16, ECE at most +0.01. All ports of this tier:

The ports are decision-layer checks against bf16, not new benchmarks; the model's own results are on basal-2.0-mini.

Use

uv venv --python 3.12 --allow-existing ~/basal-mlx && source ~/basal-mlx/bin/activate
uv pip install "basal[mlx] @ https://github.com/rkinas/basal/archive/refs/tags/v2.0.0.tar.gz"
basal-serve --model Remek/basal-2.0-mini-MLX-8bit --mode mlx --port 8000

Speed: 93 ms c1 p50, 223 ms for 5 questions (Mac). Measured on Apple M4 Pro, 24 GB.

Engine. The same basal engine v2.0.0 also serves the experimental basal-omni-1.0 models (images and audio): README.

Calibration

CALIBRATION.json in this folder is the bf16 model's calibration, the default for every mode without a fit of its own. basal-serve --mode mlx loads CALIBRATION.<engine>.json for its engine when the folder has it, else CALIBRATION.json. A calibration fitted on bf16 readouts does not carry over exactly to a quantised port (an outside test of basal-1.5 on Ollama saw 2.3% real error at the 1% threshold with the bf16 file), so a port-specific fit, where it passed, uses the bf16 model's method on that port's own readouts of the same calibration splits, one option order:

• basal-serve --mode mlx: Ships CALIBRATION.mlx.json, fitted on MLX readouts of this port (same method as the bf16 fit, one option order); dev v2 ECE 0.0357 with the bf16 fit -> 0.0337 with this fit (in-sample), 0.0375 held-out (leave-one-family-out).

Where a port uses the bf16 calibration, treat its confidence thresholds as approximate and fit your own with basal-calibrate when they matter.

Results of the model

See the model card basal-2.0-mini for Werdykt, JDI, LEK, PL-Holdout and PL-Real numbers against basal-1.5 and the evaluation notes. This port is not scored separately on those benchmarks.

Limitations

• Validate on your own documents before relying on it: hidden and held-out sets do not cover every domain.
• Polish world knowledge of a small model is limited: put the relevant facts in the state. Legal rules change.
• Exact arithmetic and thresholds are a weak spot of one-pass decisions: compute them in code (or "facts": "auto").
• The calibration is cautious on unseen tasks: no 1% threshold is certified in the shipped files, so "max_error": 0.01 on an act or tool question defers every decision; use 0.05, or certify thresholds on your own data with basal-calibrate.
• act and multi over many labels are experimental. Up to 255 options per question (Ollama: 20); the context is 32,768 tokens (mini: 8,192). The 2.0 models ship no evidence head.
• Decisions with serious consequences for people should be reviewed by a person.

Citation

@software{kinas2026basal20,
  title   = {basal-2.0: typed-decision models and inference engine for Polish and English},
  author  = {Kinas, Remigiusz},
  year    = {2026},
  version = {2.0.0},
  url     = {https://github.com/rkinas/basal}
}

License and attribution

Apache-2.0. Fine-tuned from speakleash/Bielik-1.5B-v3.0-Instruct (Apache-2.0). Bielik is a project of SpeakLeash and ACK Cyfronet AGH.