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 quantization_info.json from aghatage/SFR-Embedding-2_R-4bit-NF4: direct link, hf CLI and curl.
- Browser
- Download file 278 Bytes
-
https://huggingface.co/aghatage/SFR-Embedding-2_R-4bit-NF4/resolve/main/quantization_info.json
- Command line
-
hf download hf://aghatage/SFR-Embedding-2_R-4bit-NF4/quantization_info.json
-
curl -L -o quantization_info.json https://huggingface.co/aghatage/SFR-Embedding-2_R-4bit-NF4/resolve/main/quantization_info.json
278 Bytes
| { | |
| "quantization_method": "bitsandbytes_nf4", | |
| "base_model": "Salesforce/SFR-Embedding-2_R", | |
| "skip_patterns": [ | |
| "embed_tokens", | |
| "layernorm", | |
| "norm", | |
| "layers.0.self_attn", | |
| "layers.1.self_attn" | |
| ], | |
| "size_mb": 3578.508056640625, | |
| "phase1_outliers": 31 | |
| } |