Qwen3.8-27B-MXFP4-mlx

Qwen3.8-27B — Unsloth MXFP4 + MXFP8 (MLX)

Brooooooklyn/Qwen3.8-27B-MXFP4-mlx is an Apple Silicon MLX quantization of Qwen/Qwen3.8-27B. The model has a 64-layer dense Qwen3.5-family language backbone with 48 linear-attention and 16 full-attention layers, a BF16 vision tower, and one preserved MTP layer.

This model is part of the Unsloth NVFP4 Tensor-Class Recipe for MLX — macOS + DGX collection.

The source was the all-BF16 checkpoint at revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.

Quantization recipe

This is a data-free, weight-only Apple translation of the Unsloth Qwen3.6 NVFP4 recipe, applied to Qwen3.8's matching dense Qwen3.5-family tensor layout:

• tensors assigned NVFP4 by the recipe are stored as MXFP4, 4-bit with group size 32;
• tensors assigned FP8 by the recipe are stored as MXFP8, 8-bit with group size 32;
• every excluded tensor stays BF16.

Both MXFP classes use MLX weight-only quantized matmul with BF16/A16 activations. This ports the recipe's tensor-class selection; it does not claim numerical or performance parity with NVIDIA NVFP4, calibrated W4A4/W8A8 execution, or FP8 KV-cache quantization.

No imatrix, calibration dataset, AWQ-style pre-scaling, activation calibration, NVFP4 global scale, or FP8 KV-cache calibration was used.

The allocation contains 168 MXFP4 modules and 233 MXFP8 modules. The final eight dense FFNs intentionally use the higher class. The MTP subtree is kept inline in the main shards; mlx-node detects it at load and enables native MTP speculative decoding by default unless enableMtp: false is requested.

Compatibility and usage

This checkpoint requires @mlx-node/lm and @mlx-node/core 0.0.10 or a newer release that supports the same Qwen3.5-family MXFP and inline-MTP formats.

npm install @mlx-node/lm@^0.0.10 @mlx-node/core@^0.0.10

import { loadSession } from '@mlx-node/lm';

const session = await loadSession('./Qwen3.8-27B-MXFP4-mlx');
const result = await session.send('Explain the purpose of a unit test in one sentence.');
console.log(result.text);

Reproduction

Converter checkout: mlx-node e281f0bb (package version 0.0.10). The invocation was:

mlx convert \
  --input /Users/brooklyn/.mlx-node/models/qwen3.8-27b \
  --output /Users/brooklyn/.mlx-node/models/qwen3.8-27b-unsloth-mxfp4-mlx \
  --model-type qwen3_5 \
  --dtype bfloat16 \
  --quantize \
  --q-recipe unsloth \
  --q-mxfp

No --imatrix-path was supplied. The converter therefore applied the fixed tensor-class map without AWQ pre-scaling.

Validation

The five-shard SafeTensors index contains 1,600 entries and reports metadata.total_size = 23,277,610,464 bytes. Header and index validation confirmed exact shard closure, valid physical offsets, 168 MXFP4 groups, 233 MXFP8 groups, 401 scale sidecars, no bias sidecars, and identical quantization and quantization_config blocks.

All 333 vision/merger tensors and all 15 mtp.* tensors remain BF16 without quantization sidecars. No imatrix or calibration artifact is present. The tokenizer, chat template, generation config, and preprocessor assets are byte-identical to the pinned source snapshot.

A one-token mlx-node smoke test loaded the checkpoint and produced OK. A second explicit native-MTP smoke detected hasMtpWeights() = true, completed a four-token deterministic generation with enableMtp: true, and reported an MTP decode cycle. These plumbing checks do not validate model quality, long-context behavior, vision quality, or benchmark performance.

License and attribution

The source model card declares the Apache-2.0 license. Model capability and training credit belong to the Qwen Team. The tensor-class recipe is credited to Unsloth. This repository converts the pinned BF16 source weights into the mixed MXFP4/MXFP8 MLX representation described above.