Feature Extraction
Transformers
Safetensors
sentence-transformers
English
llama
custom_code
text-embeddings-inference
Instructions to use reasonir/ReasonIR-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reasonir/ReasonIR-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="reasonir/ReasonIR-8B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("reasonir/ReasonIR-8B", trust_remote_code=True) model = AutoModel.from_pretrained("reasonir/ReasonIR-8B", trust_remote_code=True) - sentence-transformers
How to use reasonir/ReasonIR-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("reasonir/ReasonIR-8B", 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
Add pipeline tag and library name
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license: cc-by-sa-4.0
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- meta-llama/Llama-3.1-8B
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## Model Summary
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ReasonIR-8B is the first retriever specifically trained for general reasoning tasks, achieving the state-of-the-art retrieval performance
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on BRIGHT (reasoning-intensive retrieval).
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base_model:
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language:
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- en
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license: cc-by-sa-4.0
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pipeline_tag: feature-extraction
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library_name: transformers
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## Model Summary
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ReasonIR-8B is the first retriever specifically trained for general reasoning tasks, achieving the state-of-the-art retrieval performance
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on BRIGHT (reasoning-intensive retrieval).
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