Feature Extraction
sentence-transformers
Safetensors
Transformers
multilingual
qwen3
text-embeddings
alfotech
silas
rag
semantic-search
vector-search
Eval Results (legacy)
text-embeddings-inference
Instructions to use alfotech/silas-embedding-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use alfotech/silas-embedding-0.6b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("alfotech/silas-embedding-0.6b") 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] - Transformers
How to use alfotech/silas-embedding-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="alfotech/silas-embedding-0.6b")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("alfotech/silas-embedding-0.6b") model = AutoModel.from_pretrained("alfotech/silas-embedding-0.6b", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- be8c7b60418ebd3d7ec08cee01173a850f5b770b2ff4e4334832cbf551e77fd8
- Size of remote file:
- 3.81 MB
- SHA256:
- c54bd71b39a15116daba63688257ea0e79c48a0b77fbb6c2047a1d987f4c6a2d
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