---
license: apache-2.0
tags:
- executorch
- xnnpack
- pte
- on-device
- image-text-to-text
base_model:
- Qwen/Qwen3-VL-2B-Instruct
---
# Qwen3-VL-2B-Instruct — ExecuTorch

A vision-language model as one `.pte` with three entry points. Ask it about a photograph on
the device; nothing leaves it.

- **Source**: Qwen/Qwen3-VL-2B-Instruct — 2.13B parameters, 28-layer decoder, 24-layer vision tower
- **License**: apache-2.0
- **Input**: a 512x512 RGB photograph and a question
- **Output**: text

## Methods

| method | takes | returns |
|---|---|---|
| `vision_encoder` | `pixel_values [1024, 1536]` | `merged [256, 2048]` **and three deepstack maps `[256, 2048]`** |
| `token_embeddings` | `ids [1, seq]` | `rows [1, seq, 2048]` |
| `text_model` | `rows`, `positions [3, 1, seq]`, `slots [1, seq]`, three deepstack maps `[1, seq, 2048]` | `logits [1, seq, 151936]` |

Three methods rather than three files because ExecuTorch copies a mutable buffer into every
method that names it, so a prefill method and a decode method would each get their own KV
cache and neither would see the other's writes. One graph serves a 277-token prompt and a
single token.

## What the caller owns

Three things, and getting any of them wrong does not throw — it answers about the wrong
thing:

1. **Splice** the 256 vision rows into the prompt embedding at the image token positions.
2. **Scatter** the three deepstack maps to those same positions, zero everywhere else.
   Qwen3-VL adds features from vision blocks 5, 11 and 17 into decoder layers 0, 1 and 2,
   and only at image positions. Upstream writes that as
   `hidden_states[visual_pos_masks, :] += embeds`, which does not export; here the maps
   arrive already placed, so the graph just adds them.
3. **Pass Qwen's own 3-D positions** from `get_rope_index`, not `arange`. The rope is
   interleaved M-RoPE with `mrope_section [24, 20, 20]` — time, height, width. A 1-D
   position array gives every image patch the same angle and reads as a mild degradation
   rather than an error.

`convert/run_qwen3_vl.py` does all three in 40 lines.

## Verification (Mac arm64, executorch 1.4.0, torch 2.13.0)

Parity is the last-row logits of a real prompt against `Qwen3VLForConditionalGeneration` in
eager fp32, per question, because a model of this size can hold a scene and lose the small
print. Both questions are about the same photograph.

| build | file | size (MB) | corr, "name on the sign" | corr, "describe it" | vision (ms) | prefill (ms) | decode |
|---|---|---|---|---|---|---|---|
| 8da8w | `qwen3_vl_2b_instruct_xnnpack_8da8w.pte` | 2453.1 | 0.993435 | 0.992611 | 568.7 | 590.6 | 17.1 tok/s |
| 8da4w *(not shipped)* | `qwen3_vl_2b_instruct_xnnpack_8da4w.pte` | 1697.5 | 0.739964 | 0.948887 | 566.7 | 739.4 | 19.8 tok/s |

The prompt is 277 tokens, of which 256 are the picture. `vision_encoder` runs
once per image, `text_model` once per prompt and once per generated token.

### What it says

Asked for the name on a pub sign, against the eager model's *"Lamb & Flag"*:

- **8da8w** — Based on the image provided, the name of the pub is **The Lamb & Flag**.

This name is visible on the sign above the entrance to the establishment. The sign also includes the address "
- **8da4w** — Based on the text visible on the sign above the entrance of the establishment, the name of the pub is **Pine Purpose**.

The sign is located on the front of the building, just above

## The rewrites, and what each was checked against

Neither this repo's generic VL bundle nor `export_llm` reaches Qwen3-VL. Five things had to
change, each measured against the module it replaced:

| what | why | checked |
|---|---|---|
| vision constants precomputed | position embeddings are bilinearly interpolated from `grid_thw` on every call | part of the 0.000e+00 below |
| variable-length attention → plain SDPA | one image is one sequence, so `cu_seqlens` is `[0, 1024]` | exact |
| patch embedding `Conv3d` → `F.linear` | portable convolution takes 3-D or 4-D only: `Expect input tensor to be 3-D or 4-D, but got, 5` | max_abs_diff 4.9e-04 |
| deepstack mask-assignment → add | boolean-mask assignment does not export | first-step logits corr 1.000000 |
| embedding table quantised by hand | see below | corr 0.999965 |

With the first two in place and no quantisation, the vision wrapper reproduces
`Qwen3VLVisionModel` at **max_abs_diff 0.000e+00** across all four outputs, and the decoder
wrapper reproduces the full model's first-step logits at **corr 1.000000**.

## Two traps worth knowing

**The token embedding table escapes quantisation.** It is an `nn.Embedding`, so a filter
written as `isinstance(module, nn.Linear)` skips it — and `tie_word_embeddings` does not
save it, because `quantize_` swaps `lm_head.weight` for a new tensor while the embedding
keeps pointing at the old fp32 one. At 151,936 x 2048 that is **1.24 GB left in fp32**: the
first export of this model came out at 2630 MB. torchao's own `IntxWeightOnlyConfig` does
reach an `nn.Embedding` but will not lower — `Missing out variants:
{'torchao::dequantize_affine'}` — so the quantisation is written out by hand, one scale
per row.

**512x512 is where small text lives or dies, and the resampling filter decides.** Asked
for the name on the sign, **the eager fp32 model** answers:

| resize filter | eager fp32 |
|---|---|
| BICUBIC | "Lamb & Flag" — right |
| BILINEAR | "The Lamb & Flag" — right |
| LANCZOS | "The Pigeon House" — wrong |
| NEAREST | "Pompadour" — wrong |

That is before any quantisation. If fine print matters for your use, the preprocessing is
not a detail; `convert/export_qwen3_vl.py` puts the resize behind one helper for that reason.

## Not shipped

**8da4w** — the same bundle with a 4-bit decoder — is 1697.5 MB against
2453.1 MB, and it does not hold: logits correlation 0.740 on the sign question
against 0.993, and it reads the sign as *"Pine Purpose"*. Measured where the loss is,
one arm at a time: the vision tower at int8 costs corr 0.990 against fp32's 0.99982, the
hand-quantised table costs 0.99997, and the 4-bit decoder is the rest. Reported here rather
than dropped, because a 1.7 GB 2B vision-language model is worth wanting and this is what it
costs.

## Conversion

```bash
python convert/export_qwen3_vl.py            # check both halves against eager
python convert/export_qwen3_vl.py --export   # write the bundle
python convert/check_qwen3_vl.py 8da8w       # parity and speed
python convert/run_qwen3_vl.py photo.jpg "what is on the sign?"
```

(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))