Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
multilingual
llama_bidirec
text
text-embeddings
retrieval
semantic-search
custom_code
text-embeddings-inference
Instructions to use nvidia/llama-nemotron-embed-1b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/llama-nemotron-embed-1b-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/llama-nemotron-embed-1b-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] - Transformers
How to use nvidia/llama-nemotron-embed-1b-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/llama-nemotron-embed-1b-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/llama-nemotron-embed-1b-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Remove unused imports
#15
by oliverholworthy - opened
- llama_bidirectional_model.py +1 -12
llama_bidirectional_model.py
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from typing import List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.cache_utils import Cache, HybridCache
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from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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SequenceClassifierOutputWithPast,
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)
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from transformers.models.llama.configuration_llama import LlamaConfig
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from transformers.models.llama.modeling_llama import (
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LlamaModel,
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LlamaPreTrainedModel,
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)
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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import torch
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from transformers.cache_utils import Cache
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from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
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from transformers.models.llama.configuration_llama import LlamaConfig
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from transformers.models.llama.modeling_llama import (
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LlamaModel,
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)
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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