Qwen3-VL-Reranker-8B-vehicle-reid-lora

Qwen3-VL-Reranker-8B LoRA adapter trained on vehicle re-identification hard negatives

This is a Cross Encoder model finetuned from Qwen/Qwen3-VL-Reranker-8B using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• Model Type: Cross Encoder
• Base model: Qwen/Qwen3-VL-Reranker-8B <!-- at revision b212dc8c91a8164aef1ea2de9c1a867611e75c04 -->
• Maximum Sequence Length: 262144 tokens
• Number of Output Labels: 1 label
• Supported Modalities: Text, Image, Video, Message

<!-- - **Training Dataset:** Unknown -->

• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'any-to-any', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}, 'image': {'method': 'forward', 'method_output_name': 'logits'}, 'video': {'method': 'forward', 'method_output_name': 'logits'}, 'message': {'method': 'forward', 'method_output_name': 'logits', 'format': 'structured'}}, 'module_output_name': 'causal_logits', 'processing_kwargs': {'chat_template': {'chat_template': 'reranker', 'add_generation_prompt': True}}, 'architecture': 'Qwen3VLForConditionalGeneration'})
  (1): LogitScore({'true_token_id': 9693, 'false_token_id': 2152, 'module_input_name': 'causal_logits'})
)

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("yaobaishen/Qwen3-VL-Reranker-8B-vehicle-reid-lora")
# Get scores for pairs of inputs
pairs = [
    ['/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg', '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/positive.jpg'],
    ['/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg', '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_01.jpg'],
    ['/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg', '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_02.jpg'],
    ['/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg', '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_03.jpg'],
    ['/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg', '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_04.jpg'],
]
scores = model.predict(pairs)
print(scores)
# [ 4.375  -6.5625 -6.125  -7.     -4.75  ]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg',
    [
        '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/positive.jpg',
        '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_01.jpg',
        '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_02.jpg',
        '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_03.jpg',
        '/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_04.jpg',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Cross Encoder Reranking

• Dataset: vehicle-reid-eval-hard
• Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

  {
      "at_k": 10
  }
  

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 5,766 training samples
• Columns: <code>query</code>, <code>document</code>, and <code>label</code>
• Approximate statistics based on the first 100 samples:

  	query	document	label
  type	string	string	int
  modality	image	image
  details			<ul><li>0: ~82.69%</li><li>1: ~17.31%</li></ul>

• Samples:

  query	document	label
  <code>/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg</code>	<code>/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/positive.jpg</code>	<code>1</code>
  <code>/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg</code>	<code>/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_01.jpg</code>	<code>0</code>
  <code>/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/query.jpg</code>	<code>/mnt/smt_nas/huwei/test_data/genvict/qwen3_vl_reranker_8b_hard_negative_dataset/samples/sample_000793/hard_negative_02.jpg</code>	<code>0</code>

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": 5.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 1
• num_train_epochs: 2
• warmup_steps: 0.1
• gradient_accumulation_steps: 8
• bf16: True
• gradient_checkpointing: True
• gradient_checkpointing_kwargs: {'use_reentrant': False}
• per_device_eval_batch_size: 1
• save_only_model: True
• load_best_model_at_end: True

All Hyperparameters

<details><summary>Click to expand</summary>

• per_device_train_batch_size: 1
• num_train_epochs: 2
• max_steps: -1
• learning_rate: 5e-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: 8
• 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: True
• gradient_checkpointing_kwargs: {'use_reentrant': False}
• 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: 1
• 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: True
• 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: True
• 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
• dataloader_multiprocessing_context: None
• dataloader_in_order: True
• 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
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}
• warmup_ratio: None

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Training Time

• Training: 1.3 hours
• Evaluation: 4.9 minutes
• Total: 1.4 hours

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 6.1.0.dev0
• Transformers: 5.15.1
• PyTorch: 2.8.0+cu128
• Accelerate: 1.14.0
• Datasets: 5.0.1
• Tokenizers: 0.22.2

Additional Resources

• Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

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",
}

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