Instructions to use EmbeddedLLM/bge-base-en-v1.5-onnx-o4-o2-gpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EmbeddedLLM/bge-base-en-v1.5-onnx-o4-o2-gpu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="EmbeddedLLM/bge-base-en-v1.5-onnx-o4-o2-gpu")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("EmbeddedLLM/bge-base-en-v1.5-onnx-o4-o2-gpu") model = AutoModel.from_pretrained("EmbeddedLLM/bge-base-en-v1.5-onnx-o4-o2-gpu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e37d6b73b3fa79276b039d7ddd196b7ba8f399dc5021359dfa97ecc291ad39b
- Size of remote file:
- 711 kB
- SHA256:
- d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66
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