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
File size: 375 Bytes
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"backend": "tokenizers",
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"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"local_files_only": false,
"model_max_length": 512,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}
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