Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Salesforce/SFR-Embedding-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Salesforce/SFR-Embedding-Mistral with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Salesforce/SFR-Embedding-Mistral") 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 Salesforce/SFR-Embedding-Mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Salesforce/SFR-Embedding-Mistral")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Salesforce/SFR-Embedding-Mistral") model = AutoModel.from_pretrained("Salesforce/SFR-Embedding-Mistral") - Notebooks
- Google Colab
- Kaggle
Recommended infrastructure & text embedding inference (TEI) compatibility
#12
by navaprasannaraj - opened
Hi,
1-) I would like to get your advice related to the recommended infrastructures to run this model:
- if CPU (ram needed, number of cpus, storage disk, inference time per query)
- if GPU(ram needed, number of GPUs,type GPU (a100, l40s, can A100 partionned into MIG works), storage disk, inference time per query)
2-) Is the model compatible to load & run with text embedding inference ?