Feature Extraction
Transformers
Safetensors
qwen3_vl
image-text-to-text
fp8
multimodal embedding
qwen
embedding
compressed-tensors
Instructions to use PIA-SPACE-LAB/Qwen3-VL-Embedding-2B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PIA-SPACE-LAB/Qwen3-VL-Embedding-2B-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PIA-SPACE-LAB/Qwen3-VL-Embedding-2B-FP8")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("PIA-SPACE-LAB/Qwen3-VL-Embedding-2B-FP8") model = AutoModelForImageTextToText.from_pretrained("PIA-SPACE-LAB/Qwen3-VL-Embedding-2B-FP8") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": null, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessorFast", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "input_data_format": null, | |
| "max_pixels": 1310720, | |
| "merge_size": 2, | |
| "min_pixels": 4096, | |
| "pad_size": null, | |
| "patch_size": 16, | |
| "processor_class": "Qwen3VLProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "longest_edge": 1310720, | |
| "shortest_edge": 4096 | |
| }, | |
| "temporal_patch_size": 2 | |
| } | |