Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
xlm-roberta
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-large-instruct") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use intfloat/multilingual-e5-large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="intfloat/multilingual-e5-large-instruct")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intfloat/multilingual-e5-large-instruct") model = AutoModel.from_pretrained("intfloat/multilingual-e5-large-instruct") - Inference
- Notebooks
- Google Colab
- Kaggle
VM Operation issue
#21
by Amit2balag - opened
Hi Folks,
I had application deployed with model intfloat/multilingual-e5-large as embedding llm upon deployment in the VM it was working as good as in the local. Now when I have intfloat/multilingual-e5-large-instruct configured as an embedding model the VM is not even able to load. Going through the documentation & model card the memory consumption & model size as in storage required is not evident anywhere. Can anyone help with these two information??
My VM has 4 GB RAM & 50 GB storage. Please suggest the required VM configuration. I'm using llama3-8b for text generation.
Regards