Feature Extraction
sentence-transformers
Safetensors
Transformers
multilingual
llama_bidirec
text
sentence-similarity
mteb
mmteb
custom_code
text-embeddings-inference
Instructions to use nvidia/llama-embed-nemotron-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/llama-embed-nemotron-8b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/llama-embed-nemotron-8b", trust_remote_code=True) 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 nvidia/llama-embed-nemotron-8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/llama-embed-nemotron-8b", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/llama-embed-nemotron-8b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.1.2", | |
| "transformers": "4.57.1", | |
| "pytorch": "2.8.0+cu128" | |
| }, | |
| "prompts": { | |
| "query": "Instruct: Given a question, retrieve passages that answer the question\nQuery: ", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |