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Qwen3-VL-Embedding-2B model trained on VDR query-document screenshot pairs

Name: Qwen3-VL-Embedding-2B-vdr
Availability: InStock

This is a sentence-transformers model finetuned from Qwen/Qwen3-VL-Embedding-2B on the llamaindex-vdr-en-train-preprocessed dataset, which is post-processed from the dataset released in Visual Document Retrieval Goes Multilingual. It maps queries and PDF document screenshots to a 1024-dimensional dense vector space and can be used for visual document retrieval and more.

Read my Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers blogpost to learn more about this model and how it was trained, or see the training script at training_visual_document_retrieval.py.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Qwen/Qwen3-VL-Embedding-2B
Maximum Sequence Length: 262144 tokens
Output Dimensionality: 2048, 1536, 1024 (default), 512, 256, 128, or 64 dimensions with truncate_dim
Similarity Function: Cosine Similarity
Supported Modalities: Text, Image, Video, Message
Training Dataset:
- llamaindex-vdr-en-train-preprocessed
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'message_format': 'structured', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'unpad_inputs': False, 'architecture': 'Qwen3VLModel'})
  (1): Pooling({'embedding_dimension': 2048, 'pooling_mode': 'lasttoken', 'include_prompt': True})
  (2): Normalize({})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers[image]

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/Qwen3-VL-Embedding-2B-vdr")
# Run inference
queries = [
    'Which line appears longer in the provided Müller-Lyer illusion example, A or B?',
]
documents = [
    'https://huggingface.co/tomaarsen/Qwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_0.jpg',
    'https://huggingface.co/tomaarsen/Qwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_1.jpg',
    'https://huggingface.co/tomaarsen/Qwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_2.jpg',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 2048] [3, 2048]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.5869, -0.1090,  0.1076]])

Evaluation

This model was evaluated on the evaluation dataset: 300 text queries against a corpus of 1500 document screenshots (300 positives plus 4 hard negatives per query). See the training blogpost for full context.

Model Size vs NDCG@10

This model achieves an NDCG@10 of 0.947, up from the base Qwen/Qwen3-VL-Embedding-2B model's 0.888, and ahead of every other VDR model I tested:

Full NDCG@10 numbers by model (20 models)

Model	Parameters	NDCG@10
tomaarsen/Qwen3-VL-Embedding-2B-vdr	2.1B	0.947
Qwen/Qwen3-VL-Embedding-8B	8.1B	0.923
nvidia/omni-embed-nemotron-3b	4.7B	0.915
nvidia/llama-nemotron-embed-vl-1b-v2	1.7B	0.912
nomic-ai/nomic-embed-multimodal-7b	8.3B	0.912
llamaindex/vdr-2b-multi-v1	2.2B	0.912
llamaindex/vdr-2b-v1	2.2B	0.911
nomic-ai/nomic-embed-multimodal-3b	3.8B	0.899
Qwen/Qwen3-VL-Embedding-2B	2.1B	0.888
LCO-Embedding/LCO-Embedding-Omni-7B	8.9B	0.888
LCO-Embedding/LCO-Embedding-Omni-3B	4.7B	0.860
BAAI/BGE-VL-v1.5-zs	7.6B	0.800
BAAI/BGE-VL-v1.5-mmeb	7.6B	0.797
BAAI/BGE-VL-MLLM-S2	7.6B	0.792
BidirLM/BidirLM-Omni-2.5B-Embedding	2.5B	0.775
royokong/e5-v	8.4B	0.767
BAAI/BGE-VL-MLLM-S1	7.6B	0.710
sentence-transformers/clip-ViT-L-14	428M	0.611
BAAI/BGE-VL-large	428M	0.467
BAAI/BGE-VL-base	150M	0.335

This 2B model outperforms even the 8B Qwen3-VL-Embedding model on this task.

Matryoshka Dimensions vs NDCG@10

The comparison above uses full-size 2048-dim embeddings. Thanks to the Matryoshka training, this model also holds up well when truncated to fewer dimensions, letting you trade off embedding size and retrieval quality at deployment time:

[!NOTE] Peak performance is at the full 2048 dimensions (0.948), but the model stays within 0.3% of peak all the way down to 512 (4x smaller), and retains over 92% of peak even at 64 (32x smaller). Matryoshka training concentrates the most important information in the earlier dimensions, so moderate truncation costs very little performance.

Full NDCG@10 numbers by dimension

Dimensions	Base NDCG@10	Finetuned NDCG@10
2048 (full)	0.8961 (100%)	0.9480 (100%)
1536	0.8940 (99.8%)	0.9439 (99.6%)
1024	0.8941 (99.8%)	0.9464 (99.8%)
512	0.8760 (97.8%)	0.9451 (99.7%)
256	0.8347 (93.2%)	0.9372 (98.9%)
128	0.7888 (88.0%)	0.9058 (95.5%)
64	0.6852 (76.5%)	0.8758 (92.4%)

The gap between 1024 and 2048 dimensions is small (0.946 vs. 0.948), so this model ships with truncate_dim=1024 set in its configuration. That means SentenceTransformer("tomaarsen/Qwen3-VL-Embedding-2B-vdr") produces 1024-dimensional embeddings by default, halving the storage footprint compared to the full 2048. Pass truncate_dim=N when loading to override it.

Metrics

Information Retrieval

Dataset: vdr-eval-hard
Evaluated with InformationRetrievalEvaluator

Metric	vdr-eval-hard
cosine_accuracy@1	0.8933
cosine_accuracy@3	0.97
cosine_accuracy@5	0.9833
cosine_accuracy@10	1.0
cosine_precision@1	0.8933
cosine_precision@3	0.3233
cosine_precision@5	0.1967
cosine_precision@10	0.1
cosine_recall@1	0.8933
cosine_recall@3	0.97
cosine_recall@5	0.9833
cosine_recall@10	1.0
cosine_ndcg@10	0.9485
cosine_mrr@10	0.9318
cosine_map@100	0.9318

Training Details

Training Dataset

llamaindex-vdr-en-train-preprocessed

Dataset: llamaindex-vdr-en-train-preprocessed using the train subset.
Size: 10,000 training samples
Columns: query, image, and negative_0
Approximate statistics based on the first 1000 samples:
query image negative_0
type string image image
details
min: 26 tokens
mean: 36.31 tokens
max: 62 tokens
min: 700x709 px
mean: 1416x1648 px
max: 2100x2064 px
min: 827x709 px
mean: 1438x1633 px
max: 2583x1897 px

Samples:

query	image	negative_0
`What are the new anthropological perspectives on development as discussed by Quarles Van Ufford and Giri in 2003?`
`What are the three main positions anthropologists have taken in relation to development, as discussed by David Lewis?`
`Who are the three sisters known as the Fates in Greek mythology?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "CachedMultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 2048, 5 1536, 6 1024, 7 512, 8 256, 9 128, 10 64 11 ], 12 "matryoshka_weights": [ 13 1, 14 1, 15 1, 16 1, 17 1, 18 1, 19 1 20 ], 21 "n_dims_per_step": -1 22}

Evaluation Dataset

llamaindex-vdr-en-train-preprocessed

Dataset: llamaindex-vdr-en-train-preprocessed using the eval subset.
Size: 300 evaluation samples
Columns: query, image, negative_0, negative_1, negative_2, and negative_3

Approximate statistics based on the first 300 samples:

	query	image	negative_0	negative_1	negative_2	negative_3
type	string	image	image	image	image	image
details	min: 27 tokens mean: 36.48 tokens max: 66 tokens	min: 334x481 px mean: 1425x1636 px max: 2229x1890 px	min: 992x709 px mean: 1444x1635 px max: 2051x1866 px	min: 937x709 px mean: 1437x1642 px max: 2044x1939 px	min: 872x709 px mean: 1441x1642 px max: 2044x2696 px	min: 1008x756 px mean: 1423x1654 px max: 2044x1866 px

Samples:

query	image	negative_0	negative_1	negative_2	negative_3
`Which line appears longer in the provided Müller-Lyer illusion example, A or B?`
`When did Hyundai begin its initial rural car-sharing program in Spain?`
`What is the formula for calculating the time to move to a target according to Fitts' Law?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "CachedMultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 2048, 5 1536, 6 1024, 7 512, 8 256, 9 128, 10 64 11 ], 12 "matryoshka_weights": [ 13 1, 14 1, 15 1, 16 1, 17 1, 18 1, 19 1 20 ], 21 "n_dims_per_step": -1 22}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
num_train_epochs: 1
learning_rate: 2e-05
warmup_steps: 0.1
bf16: True
per_device_eval_batch_size: 64
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

per_device_train_batch_size: 64
num_train_epochs: 1
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: steps
per_device_eval_batch_size: 64
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	vdr-eval-hard_cosine_ndcg@10
-1	-1	-	-	0.8629
0.0510	8	9.0764	-	-
0.1019	16	6.7445	8.3241	0.8987
0.1529	24	6.4662	-	-
0.2038	32	6.7289	8.3168	0.9090
0.2548	40	6.8125	-	-
0.3057	48	6.5780	8.0669	0.9261
0.3567	56	6.6617	-	-
0.4076	64	6.5015	7.9664	0.9232
0.4586	72	6.4136	-	-
0.5096	80	6.3391	8.0128	0.9381
0.5605	88	6.5283	-	-
0.6115	96	6.3356	7.8704	0.9443
0.6624	104	6.1200	-	-
0.7134	112	6.3023	7.5771	0.9481
0.7643	120	6.4604	-	-
0.8153	128	6.1659	8.1032	0.9508
0.8662	136	6.1308	-	-
0.9172	144	6.2576	7.5917	0.9454
0.9682	152	6.4182	-	-
1.0	157	-	7.1206	0.9485
-1	-1	-	-	0.9485

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.4.0.dev0
Transformers: 5.5.0.dev0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0.dev0
Datasets: 4.3.0
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

CachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}