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
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README.md
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on BRIGHT (reasoning-intensive retrieval).
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When employed for retrieval-augmented generation (RAG), ReasonIR-8B also brings substantial gains on MMLU and GPQA.
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- Repository: https://github.com/facebookresearch/ReasonIR
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- Paper: https://arxiv.org/abs/2504.20595
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## Usage
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Make sure to install `transformers>=4.47.0` first!
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on BRIGHT (reasoning-intensive retrieval).
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When employed for retrieval-augmented generation (RAG), ReasonIR-8B also brings substantial gains on MMLU and GPQA.
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- Paper: https://arxiv.org/abs/2504.20595
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- Repository: https://github.com/facebookresearch/ReasonIR
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- Data: https://huggingface.co/datasets/reasonir/reasonir-data
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## Usage
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Make sure to install `transformers>=4.47.0` first!
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