Feature Extraction
Transformers
Safetensors
qwen3_5
image-text-to-text
multimodal-embedding
retrieval
evaluation
custom_code
Instructions to use woooooz/WeMM-Embedding-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use woooooz/WeMM-Embedding-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="woooooz/WeMM-Embedding-9B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("woooooz/WeMM-Embedding-9B", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("woooooz/WeMM-Embedding-9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download evaluation/vendor/transformers/utils/dummy_tokenizers_objects.py from woooooz/WeMM-Embedding-9B: direct link, hf CLI and curl.
- Browser
- Download file 304 Bytes
-
https://huggingface.co/woooooz/WeMM-Embedding-9B/resolve/main/evaluation/vendor/transformers/utils/dummy_tokenizers_objects.py
- Command line
-
hf download hf://woooooz/WeMM-Embedding-9B/evaluation/vendor/transformers/utils/dummy_tokenizers_objects.py
-
curl -L -o dummy_tokenizers_objects.py https://huggingface.co/woooooz/WeMM-Embedding-9B/resolve/main/evaluation/vendor/transformers/utils/dummy_tokenizers_objects.py
304 Bytes
| # This file is autogenerated by the command `make fix-copies`, do not edit. | |
| from ..utils import DummyObject, requires_backends | |
| class PreTrainedTokenizerFast(metaclass=DummyObject): | |
| _backends = ["tokenizers"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["tokenizers"]) | |