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
File size: 1,041 Bytes
879aca3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ---
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-4
---
# Jasper Token Compression 600M — ONNX INT-4
ONNX export of [infgrad/Jasper-Token-Compression-600M](https://huggingface.co/infgrad/Jasper-Token-Compression-600M).
**Precision:** INT4
**Quantization:** INT4
**Model size:** 317.15 MiB
INT4 ONNX export intended for highly compact CPU/local deployment while retaining the original model's 2048-dimensional embeddings.
## Benchmarks
| Tokens | Median latency | Tokens/s |
|---:|---:|---:|
| 32 | 57.347 ms | 558.0 |
| 128 | 65.343 ms | 1,958.9 |
| 512 | 96.749 ms | 5,292.0 |
| 1024 | 137.314 ms | 7,457.4 |
## Fidelity
Median cosine similarity versus FP32: ~0.981–0.986.
## Attribution
Original model: `infgrad/Jasper-Token-Compression-600M`
|