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
Error during training with LoRA & Sentence Transformers
#33
by cc4718 - opened
trainer.train() throws the following error:NVEmbedModel.forward() got an unexpected keyword argument 'inputs_embeds'
The error appears when using LoRA.
model = SentenceTransformer(model_id, device=device, trust_remote_code=True, model_kwargs=kwargs)
peft_config = LoraConfig(
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
task_type=TaskType.FEATURE_EXTRACTION,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
)
model._modules["0"].auto_model = get_peft_model(
model._modules["0"].auto_model, peft_config
)
sentence-transformers==3.1.1
transformers==4.45.2
cc4718 changed discussion title from Error during training with Sentence Transformers to Error during training with LoRA & Sentence Transformers