---
license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3-VL-Embedding-2B
base_model_relation: quantized
pipeline_tag: feature-extraction
tags:
- transformers
- qwen3_vl
- multimodal embedding
- embedding
- feature-extraction
- quantized
- awq
- 4bit
- compressed-tensors
- custom_code
language:
- en
- zh
- multilingual
---

<p align="center">
    <img src="https://model-demo.oss-cn-hangzhou.aliyuncs.com/Qwen3-VL-Embedding.png" width="400"/>
</p>

# Qwen3-VL-Embedding-2B-AWQ-4bit

[![Technical Report](https://img.shields.io/badge/Report-arXiv%202601.04720-8B0000.svg)](https://arxiv.org/abs/2601.04720)
[![Blog](https://img.shields.io/badge/Blog-Qwen3--VL--Embedding-0F172A.svg)](https://qwen.ai/blog?id=qwen3-vl-embedding)
[![GitHub](https://img.shields.io/badge/GitHub-Qwen3--VL--Embedding-333.svg?logo=github)](https://github.com/QwenLM/Qwen3-VL-Embedding)
[![Base Model](https://img.shields.io/badge/HuggingFace-Qwen3--VL--Embedding--2B-black.svg?logo=huggingface)](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B)

## Quantized Model Overview

This repository contains a 4-bit AWQ derivative of [`Qwen/Qwen3-VL-Embedding-2B`](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) prepared for direct vLLM deployment through the `compressed-tensors` backend.

### What Was Quantized

- **Quantization method:** `llm-compressor` AWQ (`W4A16_ASYM`)
- **Export format:** `compressed-tensors`
- **Runtime backend:** vLLM `compressed-tensors`
- **Weight format:** 4-bit grouped asymmetric integer weights
- **Group size:** `128`
- **Calibration pipeline:** `layer_sequential`
- **Quantized modules:** text-side `Linear` layers in the Qwen3-VL decoder
- **Left unquantized:** all `model.visual*` modules and `lm_head`

### Calibration Data

This checkpoint was built from the same **1000-sample mixed retrieval manifest** as the FP16 and NVFP4 workflow, but the final AWQ pass used **876 text-only samples** and skipped **124 image-bearing rows** because the vision stack remained excluded from quantization.

Calibration sources:

- Polish text retrieval: `mteb/MSMARCO-PL`, `mteb/NQ-PL`, `mteb/FiQA-PL`
- Multilingual text retrieval: MIRACL hard-negative slices for `en`, `de`, `es`, `fr`, `ja`
- Multimodal retrieval in the master manifest: `vidore/colpali_train_set` and `lmms-lab/flickr30k`
- Hard-negative augmentation: MIRACL-derived negatives

### Local Benchmark Setup

The numbers below are from local full benchmark runs using the same harness for stock FP16 and quantized checkpoints.

Benchmark tasks:

- `mteb/MSMARCO-PL`
- `mteb/NQ-PL`
- MIRACL hard-negative slices: `en`, `de`, `es`, `fr`, `ja`
- `vidore/vidore_v3_industrial`
- `vidore/vidore_v3_computer_science`

Metrics:

- `nDCG@10`
- `Recall@10`
- `MRR@10`

### Baseline Comparison

Compared with the stock FP16 `Qwen/Qwen3-VL-Embedding-2B` checkpoint on the local full benchmark:

| Metric | Stock FP16 | AWQ 4-bit | Delta |
| --- | ---: | ---: | ---: |
| `nDCG@10` | `0.56222` | `0.54474` | `-0.01748` |
| `Recall@10` | `0.64934` | `0.63544` | `-0.01390` |
| `MRR@10` | `0.78883` | `0.80040` | `+0.01157` |
| Benchmark wall time | `434.853 s` | `435.140 s` | `0.07% slower` |
| Average request latency | `0.332726 s` | `0.333469 s` | `+0.000743 s` |
| Throughput | `18.4338 rps` | `18.4217 rps` | `-0.0121 rps` |

Notes:

- This was the **better multimodal** quantized checkpoint of the two we tested.
- It preserved the ViDoRe image benchmarks substantially better than NVFP4 and improved `vidore_v3_computer_science` over the FP16 baseline.
- It did **not** produce a meaningful runtime speedup versus the FP16 checkpoint in this harness.
- The AWQ export is larger than the NVFP4 export and took much longer to build.

### vLLM Usage

```bash
HF_TOKEN=hf_xxx \
vllm serve LifetimeMistake/Qwen3-VL-Embedding-2B-AWQ-4bit \
  --runner pooling \
  --convert embed \
  --trust-remote-code \
  --quantization compressed-tensors \
  --limit-mm-per-prompt '{"image":1}'
```

If your vLLM build does not automatically pick up the bundled `chat_template.jinja`, download the repo locally and pass `--chat-template /path/to/chat_template.jinja`.

## Base Model Introduction

This model is a quantized derivative of [`Qwen/Qwen3-VL-Embedding-2B`](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B), the 2B member of Qwen’s multimodal embedding series.

Upstream model highlights:

- Multimodal inputs: text, images, screenshots, video, and mixed text+vision inputs
- 30+ language support
- 32k context length
- Output dimension up to `2048`, with support for smaller embedding dimensions
- Instruction-aware retrieval behavior, with English instructions recommended even for multilingual tasks

For the full base model card, broader benchmark tables, and upstream usage examples, see:

- Base model: https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B
- GitHub: https://github.com/QwenLM/Qwen3-VL-Embedding
- Technical report: https://arxiv.org/abs/2601.04720