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, device_map="auto") - 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
- Xet hash:
- da070f8626ee36c29d81d14f7a18b0a943ba6477aaa86b433d0f865f98bf8392
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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