zainmerchan/Julia-1-MLX

Julia-1 MLX

Julia-1 for Apple silicon. Runs Supersonic Labs' Julia-1 decision model on the Mac's GPU with MLX through the julia-mlx runtime: the same answers as the official PyTorch runtime on its published evaluations, 5–14× faster on the same Mac. The upstream model repository is SupersonicLabs/Julia-1.

The files in this repository are Supersonic Labs' Julia-1 checkpoint, unchanged (model.safetensors SHA-256 df853bf7fe424420011f3d0c47a05d7341aa9eefa7fb9f203ea4aada4ad95b72). The runtime maps it into MLX directly, so no converted copy is needed. Precision (dtype="float16") and embedding placement are load-time options rather than separate files.

This is an independent project, not affiliated with or endorsed by Supersonic Labs.

Usage

pip install julia-mlx

from julia_mlx import load_model

engine = load_model("zainmerchan/Julia-1-MLX", max_length=1024, head_length=512, strict_encoding=True)
result = engine.predict(
    state="I was charged twice for the same order.",
    questions={
        "team": {
            "type": "choice",
            "instructions": "Which team should handle this request?",
            "criteria": {"billing": "Billing and payment disputes", "shipping": "Shipping and delivery", "access": "Account access and login"},
        },
    },
)
print(result["answers"]["team"]["choice"])

load_model("SupersonicLabs/Julia-1") loads the upstream repository the same way.

Accuracy

Supersonic's published CPU evaluation, rerun on the same pinned data and settings. FP32 is the default.

Evaluation	Published (PyTorch CPU)	MLX FP32	Same winner as PyTorch
typed-decisions (choice / noul / score)	1,451 / 2,000 (426 / 483 / 542)	1,451 (426 / 483 / 542)	2,000 / 2,000
AG News pilot, 4 labels	94 / 100	94	100 / 100
DAIR Emotion pilot, 6 labels	86 / 100	86	100 / 100
Banking77 pilot, 72 labels	60 / 100 via an unpublished shortlist	62 via Router (PyTorch: 62)	100 / 100

The largest FP32 logit difference from PyTorch is 0.00115. See the runtime README for FP16 results and the evaluation protocol.

Performance

On an Apple M4 Pro, against the reference PyTorch runtime on the same machine's CPU:

Workload	PyTorch CPU	MLX FP32	MLX FP16
Single call, median	34.15 ms	6.62 ms	6.41 ms
2,000 typed questions, batch 16	24.46 /s	143.37 /s	169.02 /s
16 requests × 8,192 tokens	83.41 s	5.91 s	4.97 s

Attribution

• Julia-1 (weights, decision head, typed API) by Supersonic Labs, Apache-2.0: SupersonicLabs/Julia-1.
• mmBERT-small by Johns Hopkins CLSP (jhu-clsp/mmBERT-small), the encoder and tokenizer Julia-1 builds on.
• ModernBERT by Answer.AI and LightOn, the encoder architecture.
• The MLX encoder derives from pappitti/modernbert-mlx (MIT).

The weights keep their Apache-2.0 license. The julia-mlx runtime is MIT-licensed.