---
license: apache-2.0
library_name: mlx
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3.5-35B-A3B
tags:
- mlx
- qwen3.5
- vision-language
- mixture-of-experts
- 4-bit
- mtp
---

# Qwen3.5-35B-A3B MLX VLM Affine W4/g64 With Inline Affine W4 MTP

This is a full vision-language MLX conversion of
[`Qwen/Qwen3.5-35B-A3B`](https://huggingface.co/Qwen/Qwen3.5-35B-A3B),
created from exact source revision
`59d61f3ce65a6d9863b86d2e96597125219dc754`.

Every quantizable target and inline-MTP module uses affine 4-bit weights with group size 64. There are no 8-bit router overrides. As in the standard MLX-VLM conversion scope, the BF16 vision tower and normalization vectors remain unquantized.

The indexed safetensors artifact contains:

- The language target: 512 affine W4/g64 modules, including every MoE router.
- The complete BF16 vision tower: 333 tensors and 446,571,248 parameters.
- The complete native MTP subtree under `mtp.*`: 13 affine W4/g64 modules, including both MTP routers, plus BF16 norms.

The MTP head reuses the target embedding and LM head as defined by the source architecture.

## Runtime requirements

Use `mlx-vlm>=0.6.12` for ordinary text and image inference. This artifact was tested with `mlx-vlm 0.6.15`.

```bash
pip install -U "mlx-vlm>=0.6.12" jinja2
```

```bash
mlx_vlm.generate \
  --model EigenLabs/Qwen3.5-35B-A3B-MLX-VL-4bit-g64 \
  --prompt "Explain speculative decoding." \
  --max-tokens 256 \
  --temperature 0
```

Add `--image /path/to/image.jpg` for vision-language inference.

Current public `mlx-vlm` loads and serves the target while ignoring the namespaced `mtp.*` tensors. This repository does not include a Python Qwen MTP driver. The inline MTP payload is intended for runtimes that implement Qwen3.5 MTP, including Darkbloom's MLX-Swift engine.

## Artifact

| Property | Value |
|---|---:|
| Indexed tensors | 2,136 |
| Target and vision tensors | 2,090 |
| Serialized MTP tensors | 46 |
| Source MTP tensors | 785 |
| Source MTP parameters | 844,640,768 |
| Quantized modules | 525, all affine W4/g64 |
| MTP payload | 475,125,888 bytes |
| Combined tensor payload | 20,866,531,040 bytes |
| Safetensors shards | 5 |

The MLX converter reports 4.647 effective bits per target weight after scales, biases, and the unquantized vision tower are included.

Machine-readable provenance and verification evidence are in `quantization_manifest.json`, `verification.json`, and `SHA256SUMS`.

## Local benchmark

Target-only greedy generation on an Apple M4 Max with `mlx-vlm 0.6.15`:

- Prompt: 1,701 tokens
- Generation: 128 tokens
- Warm-up: one discarded run
- Measurements: three runs
- Temperature: `0.0`

| Metric | Median | Mean | Range |
|---|---:|---:|---:|
| Prefill | 1,642.06 tok/s | 1,642.60 tok/s | 1,641.14–1,644.61 tok/s |
| Decode | 113.67 tok/s | 112.22 tok/s | 109.23–113.75 tok/s |
| Peak memory | 23.25 GB | — | — |

These are local machine measurements, not cross-device guarantees. MTP was not active in this benchmark.

## Verification scope

Verified:

- All 27 files in the pinned source snapshot passed Hugging Face cache verification.
- The converted index exactly covers every tensor in all five safetensors files.
- All 525 quantized modules resolve to affine W4/g64; no module has an 8-bit override.
- All 785 source MTP tensors are represented by the 46 serialized MLX tensors.
- Representative affine dequantization checks passed for fusion, attention, expert, and router weights.
- Qwen zero-centered RMSNorm conversion matched exactly.
- `chat_template.jinja` is byte-identical to the official source.
- A clean `mlx-vlm 0.6.15` target-only inference and three-run throughput benchmark completed.

Not yet verified:

- Pixel-grounded image output for this exact conversion.
- Qwen MTP speculative token parity or speedup.
- Video inference.
- Population-level quality after quantization.

## License

The base model is released under Apache 2.0. See the original
[`Qwen/Qwen3.5-35B-A3B`](https://huggingface.co/Qwen/Qwen3.5-35B-A3B)
repository for its model card and license.