Jasper Token Compression 600M — ONNX INT-8
ONNX export of 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
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Model tree for magiccodingman/Jasper-Token-Compression-600M-ONNX-INT8
Base model
Qwen/Qwen3-0.6B-Base Finetuned
Qwen/Qwen3-Embedding-0.6B
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]