Sentence Similarity
Transformers
Safetensors
multilingual
nllb-llm2vec
image-feature-extraction
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
text-reranking
feature-extraction
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
custom_code
Instructions to use fdschmidt93/NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fdschmidt93/NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fdschmidt93/NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 968556b0c8165f063432e8250e54984c2357ead2746212b89919c73ec5260187
- Size of remote file:
- 4.92 GB
- SHA256:
- 276dfcdc542f28f6c978a1921d4e876a502e3506acd69bffbff5078e5c6e9662
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