Feature Extraction
sentence-transformers
Safetensors
multilingual
English
xlm-roberta
embedding
code-search
security
bge-m3
unsloth
qlora
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use kiel2/Kiel-2.5-Poly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kiel2/Kiel-2.5-Poly with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kiel2/Kiel-2.5-Poly") 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
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from kiel2/Kiel-2.5-Poly: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/kiel2/Kiel-2.5-Poly/resolve/main/tokenizer.json
- Command line
-
hf download hf://kiel2/Kiel-2.5-Poly/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kiel2/Kiel-2.5-Poly/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 8609cedc690486f3066b1c41147e9660283f785434f1cdfefb96e120d782e4f2
- Size of remote file:
- 17.1 MB
- SHA256:
- 425a292ab98dcc4731590fa00c2f177e450476e147aeab40db4061b4e38ba25a
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