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Sponsored by
APIMart
caiovicentino1 / Eikos-4B-MLX-4bit
Model's Last Updated: October 02 2026
text-classification
Introduction of Eikos-4B-MLX-4bit
Model Details of Eikos-4B-MLX-4bit
Eikos-4B-MLX-4bit
MLX 4-bit build of
Eikos-4B
: MLX weights quantized to 4 bits (group size 64;
mlx_lm convert -q --q-bits 4 --q-group-size 64
) for Apple Silicon.
The vision tower, MTP weights, embeddings and LM head are kept in higher precision. The prompt format, the
letter readout and the calibration (
calib.json
, T = 1) are the same as the bf16 model.
Eikos answers typed decisions (yes/no, one of N options, ordinal scores) about a given state in one forward pass, with a calibrated probability for every option. See the Eikos-4B card for what the model does, how it was trained, the full evaluation and its limitations.
Use
pip install mlx mlx-lm
hf download caiovicentino1/Eikos-4B-MLX-4bit --local-dir Eikos-4B-MLX-4bit
python Eikos-4B-MLX-4bit/mlx_decide.py Eikos-4B-MLX-4bit # runs a few demo decisions
import sys; sys.path.insert(0, "Eikos-4B-MLX-4bit")
from decision_core import options_of
from mlx_decide import MLXDecider
d = MLXDecider("Eikos-4B-MLX-4bit")
q = {"type": "noul", "instructions": "Under rule 4.2, can this order be executed as submitted?",
"criteria": {"true": "complies with rule 4.2", "false": "breaches rule 4.2"}}
probs, n_tokens = d.dist("Order ticket #A-2231 ... Rule 4.2: ... Approvals on file: none.", q, options_of(q))
mlx_decide.py
uses the same letter readout, prompt and calibration as the GPU builds. It also answers several
questions over one state in a single cached batch (
dist_many_cached
).
Validation against bf16
Same 7,371 items for both builds (7 suites, never used in training), MLX run with its CUDA backend on Linux. The release gate was fixed before looking at results: accuracy within 1 point of bf16, ECE within 0.01, and at least 97% of answers unchanged.
| Eikos-4B (bf16) | Eikos-4B-MLX-4bit | |
|---|---|---|
| Size | 9.3 GB | 2.4 GB |
| JevBench public — original / hard | 91.7 / 73.9 | 95.8 / 73.0 |
| DecisionBench — medium / hard | 77.1 / 66.6 | 78.8 / 64.8 |
| General battery (9 tasks) | 75.7 | 75.3 |
| Finance (CUAD, sentiment, FinQA-judge) | 74.7 | 76.0 |
| Trade rules — seen / unseen | 74.5 / 76.0 | 72.4 / 76.0 |
| Compositional rules — same type / new domain / rulebooks | 96.0 / 91.5 / 91.7 | 95.0 / 90.5 / 90.7 |
| ECE (lower is better) | 0.033 | 0.021 |
| ≥0.90 confidence: decides / error | 34.7% / 2.5% | 32.5% / 2.7% |
| Same answer as bf16 (all / confident ≥0.9) | — | 92.5% / 99.8% |
It passes our release gate on accuracy and calibration, but not on answer agreement (92.5% of answers are the same as bf16; the gate asks for 97%). Most changed answers (93%) are on items where the bf16 model itself was unsure (confidence below 0.7); on decisions the bf16 model takes with confidence ≥0.9, 99.8% of answers are the same.
License
MIT for our contributions (
LICENSE
). The base model, Qwen3.5-4B, is Apache-2.0 (
LICENSE-Qwen
);
attributions are in
NOTICE
. Not legal, tax or investment advice.
Runs of caiovicentino1 Eikos-4B-MLX-4bit on huggingface.co
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More Information About Eikos-4B-MLX-4bit huggingface.co Model
Url of Eikos-4B-MLX-4bit
Eikos-4B-MLX-4bit huggingface.co Url
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Other API from caiovicentino1
Updated:April 09 2026
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