license, library_name, base_model, base_model_relation, pipeline_tag, tags, language

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

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

Quantized Model Overview

This repository contains a 4-bit AWQ derivative of 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

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, 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