Feature Extraction
Transformers
Safetensors
sentence-transformers
English
nvembed
mteb
custom_code
Eval Results (legacy)
Instructions to use nvidia/NV-Embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NV-Embed-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/NV-Embed-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/NV-Embed-v2", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use nvidia/NV-Embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/NV-Embed-v2", 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
Howto run nvidia/NV-Embed-v2 on GPU
#26
by hozbey - opened
Hello,
I am using "nvidia/NV-Embed-v2" with Open-WebUI for RAG(Retrieval-augmented generation). But the Model is running on CPU.
Would you please comment that how could I run "nvidia/NV-Embed-v2" on GPU resources?
Best Regards
Huseyin A. Ozbey
load like this
model_embed_nvidia = AutoModel.from_pretrained(
f"{MOUNT_DIR}/NV-Embed-v2", device_map={"": "cuda:0"}, trust_remote_code=True
)
load like this
model_embed_nvidia = AutoModel.from_pretrained( f"{MOUNT_DIR}/NV-Embed-v2", device_map={"": "cuda:0"}, trust_remote_code=True )
This will only use the first GPU. Try this
model = AutoModel.from_pretrained('nvidia/NV-Embed-v2', trust_remote_code=True, device_map="auto")