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:
- fd0498b050f369b0b10254fe00e8636bb265b71b8690310591a18ac3676d9372
- Size of remote file:
- 11.4 MB
- SHA256:
- 7bbd7da4557f4f46591cf4eec87298afe7a5015f11a8449304b84821f2475d0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.