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sahilchachra/supra-50m-instruct-fp16-mlx

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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supra-50m-instruct-fp16-mlx

MLX quantization of SupraLabs/Supra-50M-Instruct for Apple Silicon.

Variant: BFloat16 (lossless reference) Disk size: 201 MB Quantized by: sahilchachra

Benchmark results

Evaluated on Apple M4 Pro with MLX. Model loaded once; performance and quality measured in a single pass.

Performance

	This model	FP16 baseline
Decode tok/s (avg, long traces)	1270.13	1270.13
Peak memory (GB)	0.223	0.223
Disk size (MB)	201	201

Quality

Benchmark	This model	FP16 baseline	n
IFEval (instruction following)	15.9%	15.9%	44
Alpaca-cleaned (instruct F1 vs reference)	36.2	36.2	50

Context scaling (decode tok/s)

Context length	Decode tok/s
~128 tokens	1294.4
~256 tokens	1274.3
~512 tokens	1277.6
~1024 tokens	1234.2

Usage

bash

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("sahilchachra/supra-50m-instruct-fp16-mlx")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256, verbose=True)

All variants in this collection

Model	Variant
sahilchachra/supra-50m-instruct-8bit-mlx	Affine int8
sahilchachra/supra-50m-instruct-optiq-5bpw-mlx	OptiQ mixed-precision (target 5.0 bpw)

Notes

—Requires Apple Silicon (M1 or later) with MLX
—Benchmarks run on Apple M4 Pro, 24 GB unified memory
—License: see SupraLabs/Supra-50M-Instruct for the original model's license

Original model

See SupraLabs/Supra-50M-Instruct for full model details and intended use.