---
library_name: mlx
pipeline_tag: image-text-to-text
base_model: QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4
tags:
  - mlx
  - nvfp4
  - fp4
  - quantization
  - quantization-aware-training
  - quasar
  - qwen3_5
---

# Qwen3.8-27B-QUASAR-NVFP4-mlx

MLX conversion of [`QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4`](https://huggingface.co/QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4),
a 4-bit NVFP4 (W4A4) quantization-aware-trained build of
[`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) where **all 496
linear layers** (self-attention, gated delta-net, and MLP) are NVFP4. Converted
from the source `compressed-tensors` `nvfp4-pack-quantized` checkpoint to the
MLX-native `nvfp4` layout for the omlx / mlx-vlm runtime on Apple silicon.

- ~19.15 GiB: 4 weight shards (18.36 GiB) + `mtp.safetensors` (MTP draft head,
  15 tensors, 810 MiB).
- 1695 tensors: 496 packed NVFP4 weights (`uint32`) + 496 E4M3 scales (`uint8`)
  + 703 BF16 (embeddings, lm_head, vision tower, norms, conv1d, A_log, dt_bias)
  + the 15-tensor MTP head.

## Conversion notes

MLX's `nvfp4` kernel is **single-level** and does not carry the per-tensor
global scale, so the two-level source scaling is folded into the per-group E4M3
scales: the E2M1 codes are kept bit-exact and each per-group scale is stored as
`E4M3(decode(weight_scale) / weight_global_scale)`. This single re-rounding of
the (much smaller) scale tensor is the only precision change versus the source;
the packed 4-bit weights themselves are byte-identical.

MTP norm weights carry the MLX `+1.0` RMSNorm shift; the MTP head is a separate
`mtp.safetensors` side file.

Produced with `convert_vllm_nvfp4_to_mlx.py`.