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License
mit
Tasks
feature-extraction
Frameworks
transformers
Hardware-aware snippets
Runtime-specific quick starts for BAAI/bge-small-en. Detected hardware: No explicit hardware metadata.
Task: feature-extractionTransformers
NVIDIA CUDA path
Best default for NVIDIA GPUs and hosted accelerator nodes.
pip install torch transformers accelerate
python - <<'PY'
from transformers import pipeline
model_id = "BAAI/bge-small-en"
pipe = pipeline(
task="feature-extraction",
model=model_id,
device_map="auto",
model_kwargs={"torch_dtype": "auto"},
)
print(pipe("Hello from Inferix"))
PYModel lineage
1 reposBase model
BAAI/bge-small-en
Finetuned
1bge-small-enselected
Quantizations
0No quantization variants detected.
Model info
Namespace
BAAI
Visibility
public
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0
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0
License: mit
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Base model
Not specifiedTrained on datasets
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0/0 fitNot available for inference yet.
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Tags
importedhuggingfaceminiotransformerspytorchsafetensorsbertfeature-extractionmtebsentence transformersenarxiv:2311.13534arxiv:2310.07554arxiv:2309.07597license:mitmodel-indextext-embeddings-inferenceendpoints_compatibledeploy:azureregion:us