Instructions to use nvidia/NV-Embed-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/NV-Embed-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/NV-Embed-v1", 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] - Notebooks
- Google Colab
- Kaggle
Regarding max seq length
#33
by sandeep456 - opened
what is the maximum sequence length of input this model can process. in MTEB leaderboard it is mentioned as 32k while on your huggingface model page it is mentioned as 8k-
Model Details
Base Decoder-only LLM: Mistral-7B-v0.1
Pooling Type: Latent-Attention
Embedding Dimension: 4096
Vocab size: 32k
Max Input Token length: 8192
Please help me understand.
Hi, @sandeep456 . Here is the modified answers to your question. Max sequence input length is 32k (same as mistral-7b-v0.1 model).