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
File size: 628 Bytes
64ccba5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"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
}
|