Feature Extraction
Transformers
Safetensors
qwen3_vl
image-text-to-text
embeddings
multimodal
retrieval
compositional-reasoning
vision
reranker-distillation
Instructions to use Alibaba-NLP/core-reranker-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/core-reranker-2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Alibaba-NLP/core-reranker-2b")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Alibaba-NLP/core-reranker-2b") model = AutoModelForMultimodalLM.from_pretrained("Alibaba-NLP/core-reranker-2b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "size": { | |
| "longest_edge": 16777216, | |
| "shortest_edge": 65536 | |
| }, | |
| "patch_size": 16, | |
| "temporal_patch_size": 2, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "Qwen3VLProcessor", | |
| "image_processor_type": "Qwen2VLImageProcessorFast", | |
| "input_data_format": null, | |
| "max_pixels": 1310720, | |
| "merge_size": 2, | |
| "min_pixels": 4095, | |
| "pad_size": null, | |
| "processor_class": "Qwen3VLProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null | |
| } | |