Feature Extraction
Transformers
ONNX
English
bert
mteb
sparse
sparsity
quantized
embeddings
int8
deepsparse
Eval Results (legacy)
Instructions to use RedHatAI/bge-small-en-v1.5-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/bge-small-en-v1.5-quant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RedHatAI/bge-small-en-v1.5-quant")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RedHatAI/bge-small-en-v1.5-quant") model = AutoModel.from_pretrained("RedHatAI/bge-small-en-v1.5-quant") - Notebooks
- Google Colab
- Kaggle
Ctrl+K