Sentence Similarity
sentence-transformers
Safetensors
llama
feature-extraction
text-embeddings-inference
8-bit precision
bitsandbytes
Instructions to use velvetScar/llm2vec-llama-3.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use velvetScar/llm2vec-llama-3.1-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("velvetScar/llm2vec-llama-3.1-8B") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 96 Bytes
f0230fe | 1 2 3 4 5 6 7 8 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "__main__.LLM2VecWrapper"
}
] |