Feature Extraction
sentence-transformers
Safetensors
English
mistral
embeddings
quantization
4-bit precision
bitsandbytes
nf4
text-embeddings-inference
Instructions to use aghatage/SFR-Embedding-2_R-4bit-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aghatage/SFR-Embedding-2_R-4bit-NF4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aghatage/SFR-Embedding-2_R-4bit-NF4") 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] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from aghatage/SFR-Embedding-2_R-4bit-NF4: direct link, hf CLI and curl.
- Browser
- Download file 493 kB
-
https://huggingface.co/aghatage/SFR-Embedding-2_R-4bit-NF4/resolve/main/tokenizer.model
- Command line
-
hf download hf://aghatage/SFR-Embedding-2_R-4bit-NF4/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/aghatage/SFR-Embedding-2_R-4bit-NF4/resolve/main/tokenizer.model
493 kB
- Xet hash:
- 32726c94d96b1a5c442963faa3a900decf397fe67b5c530097a9f8b2506c63b6
- Size of remote file:
- 493 kB
- SHA256:
- dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
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