Instructions to use optimum/bge-base-en-v1.5-neuronx-bs-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum/bge-base-en-v1.5-neuronx-bs-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="optimum/bge-base-en-v1.5-neuronx-bs-1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("optimum/bge-base-en-v1.5-neuronx-bs-1") model = AutoModel.from_pretrained("optimum/bge-base-en-v1.5-neuronx-bs-1") - Notebooks
- Google Colab
- Kaggle
This model is compiled for neuronx devices (eg. on inf2 instance).
This original checkpoint is BAAI/bge-base-en-v1.5.
Export
Here below is the command used for exporting this model:
optimum-cli export neuron -m BAAI/bge-base-en-v1.5 --sequence_length 384 --batch_size 1 --task feature-extraction bge_emb/
Usage
To use the compiled artifacts for inference, here is an example:
from transformers import AutoTokenizer
from optimum.neuron import NeuronModelForSenetenceTransformers
emb_model = NeuronModelForSenetenceTransformers.from_pretrained("optimum/bge-base-en-v1.5-neuronx")
inputs = tokenizer("Hamilton is considered to be the best musical of human history.", return_tensors="pt")
emb = emb_model(**inputs)
# ["token_embeddings", "sentence_embedding"]
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