Instructions to use medkar/promptuino-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use medkar/promptuino-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("medkar/promptuino-embeddings") 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
Promptuino embeddings (ONNX, fp32)
ONNX export of
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,
used by Promptuino to retrieve Arduino
libraries locally, with no network call at runtime.
Exported with scripts/export_onnx_model.py (optimum / ONNX Runtime), fp32,
no quantisation.
Why a separate repository
Promptuino's retrieval thresholds are measured against this exact export,
and the corpus embedding matrix shipped with the application is aligned with
it. A different export โ another optimum version, another opset, or a
quantised variant โ shifts the similarity scores and silently degrades
retrieval. Pinning a revision here guarantees the installer downloads the file
the measurements were made on.
| File | model.onnx |
| Size | 470216988 bytes |
| SHA-256 | 005d51beaafb6721f3a8a2227749f2c518356fa087d9c2983da7aabaab8f84cd |
License
The original model is distributed by its authors under Apache-2.0; this export inherits it. Promptuino itself is GPL-3.0.