How to use from the
Use from the
MLX library
# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir supra-1.5-50m-instruct-exp-mxfp8-mlx sahilchachra/supra-1.5-50m-instruct-exp-mxfp8-mlx

supra-1.5-50m-instruct-exp-mxfp8-mlx

MLX quantization of SupraLabs/Supra-1.5-50M-Instruct-exp for Apple Silicon.

Variant: Block float MX FP8
Disk size: 53 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) 670.29 1025.59
Peak memory (GB) 0.152 0.223
Disk size (MB) 53 101

Quality

Benchmark This model FP16 baseline n
IFEval (instruction following) 22.7% 15.9% 44
Alpaca-cleaned (instruct F1 vs reference) 41.0 40.9 50

Context scaling (decode tok/s)

Context length Decode tok/s
~128 tokens 659.6
~256 tokens 644.8
~512 tokens 683.7
~1024 tokens 693.0

Usage

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("sahilchachra/supra-1.5-50m-instruct-exp-mxfp8-mlx")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256, verbose=True)

All variants in this collection

Model Variant
sahilchachra/supra-1.5-50m-instruct-exp-mxfp4-mlx Block float MX FP4
sahilchachra/supra-1.5-50m-instruct-exp-mxfp8-mlx Block float MX FP8 ← this model

Notes

  • Requires Apple Silicon (M1 or later) with MLX
  • Benchmarks run on Apple M4 Pro, 24 GB unified memory
  • License: see SupraLabs/Supra-1.5-50M-Instruct-exp for the original model's license

Original model

See SupraLabs/Supra-1.5-50M-Instruct-exp for full model details and intended use.

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