Multi-Vector Encoder

This is a Multi-Vector Encoder model finetuned from Qwen/Qwen3-VL-Embedding-2B on the llamaindex-vdr-en-train-preprocessed dataset using the sentence-transformers library. It maps inputs to sequences of 128-dimensional token-level vectors and scores them with late interaction (MaxSim), useful for semantic search with late interaction.

Model Details

Model Description

• Model Type: Multi-Vector Encoder
• Base model: Qwen/Qwen3-VL-Embedding-2B
• Maximum Sequence Length: 262144 tokens
• Output Dimensionality: 128 dimensions
• Similarity Function: maxsim
• Supported Modalities: Text, Image, Video, Message
• Training Dataset:
  • llamaindex-vdr-en-train-preprocessed

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Multi-Vector Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Multi-Vector Encoders on Hugging Face

Full Model Architecture

MultiVectorEncoder(
  (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', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'unpad_inputs': False, 'architecture': 'Qwen3VLModel'})
  (1): Dense({'in_features': 2048, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'token_embeddings', 'module_output_name': 'token_embeddings'})
  (2): MultiVectorMask({'skiplist_words': [], 'keep_only_token_ids': None})
  (3): Normalize({'module_input_name': 'token_embeddings', 'module_output_name': 'token_embeddings'})
)

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.

from sentence_transformers import MultiVectorEncoder

# Download from the 🤗 Hub
model = MultiVectorEncoder("tomaarsen/ColQwen3-VL-Embedding-2B-vdr")
# Run inference: each input becomes a sequence of per-token vectors (variable length).
queries = [
    'What are the new anthropological perspectives on development as discussed by Quarles Van Ufford and Giri in 2003?',
]
documents = [
    'https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_0.jpg',
    'https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_1.jpg',
    'https://huggingface.co/tomaarsen/ColQwen3-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[0].shape, document_embeddings[0].shape)
# (48, 128) (352, 128)

# Get the MaxSim similarity scores
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[37.0712, 33.5348, 23.2705]])

Evaluation

Metrics

Multi Vector Information Retrieval

• Dataset: vdr-eval-hard
• Evaluated with MultiVectorInformationRetrievalEvaluator

Training Details

Training Dataset

llamaindex-vdr-en-train-preprocessed

• Dataset: llamaindex-vdr-en-train-preprocessed
• Size: 10,000 training samples
• Columns: query, image, and negative_0
• Approximate statistics based on the first 100 samples:

  	query	image	negative_0
  type	string	image	image
  modality	text	image	image
  details	min: 27 tokensmean: 35.59 tokensmax: 50 tokens	min: 1000x756 pxmean: 1437x1628 pxmax: 2044x1869 px	min: 1008x756 pxmean: 1450x1637 pxmax: 2044x1851 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: CachedMultiVectorMultipleNegativesRankingLoss with these parameters:

  {
      "score_metric": "colbert_scores",
      "mini_batch_size": 1,
      "mini_batch_num_tokens": null,
      "score_mini_batch_size": 1,
      "scale": 1.0,
      "size_average": true,
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• num_train_epochs: 1
• learning_rate: 2e-05
• warmup_steps: 0.1
• max_grad_norm: 30.0
• bf16: True
• learning_rate_mapping: {'^(model\.)?1\.linear': 0.0002}

All Hyperparameters

• 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: 30.0
• 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: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 8
• 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_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {'^(model\.)?1\.linear': 0.0002}
• max_length: None

Training Logs

Training Time

• Training: 2.4 hours
• Evaluation: 26.8 minutes
• Total: 2.8 hours

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.7.0.dev0
• Transformers: 5.14.1
• PyTorch: 2.13.0+cu130
• Accelerate: 1.14.0
• Datasets: 5.0.0
• Tokenizers: 0.22.2

Additional Resources

• Sentence Transformers Documentation: the full documentation site, including training, evaluation, and pre-trained model catalogs.
• PyLate: the upstream library whose features were absorbed into Sentence Transformers for multi-vector / late-interaction models.

Citation

BibTeX

Sentence Transformers

@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",
}

CachedMultiVectorMultipleNegativesRankingLoss

@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}
}