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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/SFR-Embedding-Mistral") model = AutoModelForMultimodalLM.from_pretrained("Salesforce/SFR-Embedding-Mistral") - Notebooks
- Google Colab
- Kaggle
GGUF Format
#19
by kalle07 - opened
you know llama.cpp
it can convert models to GGUF useful for all peoples...
can you generate GGUF of your models ?
There may already be gguf conversion of this model on ollama.com/models under the name avr/sfr-embedding-mistral. Looking at the size, the fp16 lines up with the safetensors here.