Sentence Similarity
sentence-transformers
ONNX
English
Chinese
qwen3
feature-extraction
text-embeddings
embeddings
retrieval
mteb
onnxruntime
cpu
fp32
custom_code
text-embeddings-inference
Instructions to use magiccodingman/Jasper-Token-Compression-600M-ONNX-FP32 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-FP32 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("magiccodingman/Jasper-Token-Compression-600M-ONNX-FP32", 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
| 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 | |
| - fp32 | |
| # Jasper Token Compression 600M — ONNX FP32 | |
| ONNX export of [infgrad/Jasper-Token-Compression-600M](https://huggingface.co/infgrad/Jasper-Token-Compression-600M). | |
| **Precision:** FP32 | |
| **Quantization:** None (FP32) | |
| **Model size:** 2.26 GiB | |
| Full-precision FP32 ONNX export. This is the reference model used for the included quantization fidelity comparisons. | |
| ## Benchmarks | |
| | Tokens | Median latency | Tokens/s | | |
| |---:|---:|---:| | |
| | 32 | 139.282 ms | 229.7 | | |
| | 128 | 155.466 ms | 823.3 | | |
| | 512 | 211.763 ms | 2,417.8 | | |
| | 1024 | 288.388 ms | 3,550.8 | | |
| ## Attribution | |
| Original model: `infgrad/Jasper-Token-Compression-600M` | |