Image Feature Extraction
Transformers
ONNX
Safetensors
English
nomic_bert
feature-extraction
custom_code
Instructions to use nomic-ai/nomic-embed-vision-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nomic-ai/nomic-embed-vision-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nomic-ai/nomic-embed-vision-v1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-vision-v1", trust_remote_code=True) model = AutoModel.from_pretrained("nomic-ai/nomic-embed-vision-v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Add ONNX weights (#2)
Browse files- Add ONNX weights (53badc2709558de3565dc9eb17235dca781d0e59)
Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>
- onnx/model.onnx +3 -0
- onnx/model_bnb4.onnx +3 -0
- onnx/model_fp16.onnx +3 -0
- onnx/model_int8.onnx +3 -0
- onnx/model_q4.onnx +3 -0
- onnx/model_quantized.onnx +3 -0
- onnx/model_uint8.onnx +3 -0
onnx/model.onnx
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onnx/model_int8.onnx
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onnx/model_q4.onnx
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onnx/model_quantized.onnx
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onnx/model_uint8.onnx
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