Sentence Similarity
sentence-transformers
ONNX
English
Chinese
qwen3
feature-extraction
text-embeddings
embeddings
retrieval
mteb
onnxruntime
cpu
int-4
custom_code
text-embeddings-inference
Instructions to use magiccodingman/Jasper-Token-Compression-600M-ONNX-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use magiccodingman/Jasper-Token-Compression-600M-ONNX-INT4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("magiccodingman/Jasper-Token-Compression-600M-ONNX-INT4", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download configuration.json from magiccodingman/Jasper-Token-Compression-600M-ONNX-INT4: direct link, hf CLI and curl.
- Browser
- Download file 85 Bytes
-
https://huggingface.co/magiccodingman/Jasper-Token-Compression-600M-ONNX-INT4/resolve/main/configuration.json
- Command line
-
hf download hf://magiccodingman/Jasper-Token-Compression-600M-ONNX-INT4/configuration.json
-
curl -L -o configuration.json https://huggingface.co/magiccodingman/Jasper-Token-Compression-600M-ONNX-INT4/resolve/main/configuration.json
85 Bytes
| { | |
| "framework": "pytorch", | |
| "task": "text-generation", | |
| "allow_remote": true | |
| } |