Sentence Similarity
sentence-transformers
ONNX
English
Chinese
qwen3
feature-extraction
text-embeddings
embeddings
retrieval
mteb
onnxruntime
cpu
int-8
custom_code
text-embeddings-inference
Instructions to use magiccodingman/Jasper-Token-Compression-600M-ONNX-INT8 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-INT8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("magiccodingman/Jasper-Token-Compression-600M-ONNX-INT8", 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
File size: 1,018 Bytes
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license: apache-2.0
language:
- en
- zh
library_name: sentence-transformers
pipeline_tag: sentence-similarity
base_model: infgrad/Jasper-Token-Compression-600M
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- text-embeddings
- embeddings
- retrieval
- mteb
- qwen3
- onnx
- onnxruntime
- cpu
- int-8
---
# Jasper Token Compression 600M — ONNX INT-8
ONNX export of [infgrad/Jasper-Token-Compression-600M](https://huggingface.co/infgrad/Jasper-Token-Compression-600M).
**Precision:** INT8 (Dynamic)
**Quantization:** Dynamic INT8
**Model size:** 583.56 MiB
Dynamic INT8 ONNX export optimized for fast CPU inference. This is a text embedding model.
## Benchmarks
| Tokens | Median latency | Tokens/s |
|---:|---:|---:|
| 32 | 44.362 ms | 721.3 |
| 128 | 48.609 ms | 2,633.3 |
| 512 | 63.180 ms | 8,103.8 |
| 1024 | 84.619 ms | 12,101.3 |
## Fidelity
Median cosine similarity versus FP32: ~0.988–0.992.
## Attribution
Original model: `infgrad/Jasper-Token-Compression-600M`
|