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license: apache-2.0
base_model: SupraLabs/Supra-1.5-50M-Instruct-exp
tags:
- mlx
- quantized
- apple-silicon
---
# supra-1.5-50m-instruct-exp-mxfp4-mlx
MLX quantization of [SupraLabs/Supra-1.5-50M-Instruct-exp](https://huggingface.co/SupraLabs/Supra-1.5-50M-Instruct-exp) for Apple Silicon.
**Variant**: Block float MX FP4
**Disk size**: 28 MB
**Quantized by**: [sahilchachra](https://huggingface.co/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) | 1675.62 | 1025.59 |
| Peak memory (GB) | 0.126 | 0.223 |
| Disk size (MB) | 28 | 101 |
### Quality
| Benchmark | This model | FP16 baseline | n |
|---|---:|---:|---:|
| IFEval (instruction following) | 20.5% | 15.9% | 44 |
| Alpaca-cleaned (instruct F1 vs reference) | 36.8 | 40.9 | 50 |
### Context scaling (decode tok/s)
| Context length | Decode tok/s |
|---:|---:|
| ~128 tokens | 1675.2 |
| ~256 tokens | 1685.8 |
| ~512 tokens | 1655.6 |
| ~1024 tokens | 1685.9 |
## Usage
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("sahilchachra/supra-1.5-50m-instruct-exp-mxfp4-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](https://huggingface.co/sahilchachra/supra-1.5-50m-instruct-exp-mxfp4-mlx) | Block float MX FP4 ← this model |
| [sahilchachra/supra-1.5-50m-instruct-exp-mxfp8-mlx](https://huggingface.co/sahilchachra/supra-1.5-50m-instruct-exp-mxfp8-mlx) | Block float MX FP8 |
## 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](https://huggingface.co/SupraLabs/Supra-1.5-50M-Instruct-exp) for the original model's license
## Original model
See [SupraLabs/Supra-1.5-50M-Instruct-exp](https://huggingface.co/SupraLabs/Supra-1.5-50M-Instruct-exp) for full model details and intended use. |