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Qwen3-VL-Embedding-2B model trained on

Name: qwen3-vl-2b-vdr
Availability: InStock

This is a sentence-transformers model finetuned from tomaarsen/Qwen3-VL-Embedding-2B on the vdr-multilingual-train dataset. It maps sentences & paragraphs to a 2048-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: tomaarsen/Qwen3-VL-Embedding-2B
Maximum Sequence Length: 262144 tokens
Output Dimensionality: 2048 dimensions
Similarity Function: Cosine Similarity
Supported Modalities: Text, Image, Video, Message
Training Dataset:
- vdr-multilingual-train
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

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/qwen3-vl-2b-vdr")
# Run inference
queries = [
    'What is the quarter-on-quarter growth rate of Klook in Asia-Pacific as of October 2022?',
]
documents = [
    'https://huggingface.co/tomaarsen/qwen3-vl-2b-vdr/resolve/main/assets/image_0.jpg',
    'https://huggingface.co/tomaarsen/qwen3-vl-2b-vdr/resolve/main/assets/image_1.jpg',
    'https://huggingface.co/tomaarsen/qwen3-vl-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([[0.5789, 0.0973, 0.0304]])

Evaluation

Metrics

Information Retrieval

Dataset: vdr-eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9533
cosine_accuracy@3	0.99
cosine_accuracy@5	0.9933
cosine_accuracy@10	0.9933
cosine_precision@1	0.9533
cosine_precision@3	0.33
cosine_precision@5	0.1987
cosine_precision@10	0.0993
cosine_recall@1	0.9533
cosine_recall@3	0.99
cosine_recall@5	0.9933
cosine_recall@10	0.9933
cosine_ndcg@10	0.9764
cosine_mrr@10	0.9707
cosine_map@100	0.9709

Training Details

Training Dataset

vdr-multilingual-train

Dataset: vdr-multilingual-train at 6b92b5c
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 1024, 6 512, 7 256, 8 128, 9 64 10 ], 11 "matryoshka_weights": [ 12 1, 13 1, 14 1, 15 1, 16 1, 17 1 18 ], 19 "n_dims_per_step": -1 20}

Evaluation Dataset

vdr-multilingual-test

Dataset: vdr-multilingual-test at 9e26ae1
Size: 300 evaluation samples
Columns: query and image
Approximate statistics based on the first 300 samples:
query image
type string image
details
min: 27 tokens
mean: 34.26 tokens
max: 65 tokens
min: 827x1125 px
mean: 1371x1709 px
max: 2045x2045 px

Samples:

query	image
`What is the quarter-on-quarter growth rate of Klook in Asia-Pacific as of October 2022?`
`When should spinach be planted and harvested?`
`How does the discharge of sewage into a river affect the concentration of dissolved oxygen?`

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

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
eval_strategy: steps
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_cosine_ndcg@10
-1	-1	-	-	0.9790
0.0510	8	7.9663	-	-
0.1019	16	5.9054	4.6686	0.9826
0.1529	24	5.6008	-	-
0.2038	32	5.6521	4.5979	0.9810
0.2548	40	5.7503	-	-
0.3057	48	5.5388	4.6358	0.9802
0.3567	56	5.5883	-	-
0.4076	64	5.4430	4.6014	0.9812
0.4586	72	5.4762	-	-
0.5096	80	5.4937	4.6229	0.9785
0.5605	88	5.4991	-	-
0.6115	96	5.2465	4.5517	0.9781
0.6624	104	5.1596	-	-
0.7134	112	5.2998	4.6642	0.9777
0.7643	120	5.4130	-	-
0.8153	128	5.2071	4.5448	0.9781
0.8662	136	5.1424	-	-
0.9172	144	5.1973	4.6617	0.9764
0.9682	152	5.3651	-	-
-1	-1	-	-	0.9764

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 2.882 kWh
Carbon Emitted: 0.771 kg of CO2
Hours Used: 9.675 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.4.0.dev0
Transformers: 5.3.0.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}
}

	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