Text Ranking
Transformers
PyTorch
Safetensors
multilingual
llama_bidirec
text-classification
text
reranker
cross-encoder
retrieval
semantic-search
custom_code
text-embeddings-inference
Instructions to use nvidia/llama-nemotron-rerank-1b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/llama-nemotron-rerank-1b-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("nvidia/llama-nemotron-rerank-1b-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nvidia/llama-nemotron-rerank-1b-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.47 GB
-
https://huggingface.co/nvidia/llama-nemotron-rerank-1b-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nvidia/llama-nemotron-rerank-1b-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nvidia/llama-nemotron-rerank-1b-v2/resolve/main/pytorch_model.bin
2.47 GB
- Xet hash:
- 1d13d183acfbc99822b1494b25207c83e563771247619eb28343e62bb0dd86dd
- Size of remote file:
- 2.47 GB
- SHA256:
- cbe7ebaf3499ad20d5f51753d57a30f229ff47edd5e5556732b3629a8a41c37b
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