n24q02m/Qwen3-Reranker-0.6B-ONNX

ONNX-optimized version of Qwen/Qwen3-Reranker-0.6B for use with qwen3-embed and fastembed (PR #605).

Available Variants

Variant	File	Size	Description
INT8	onnx/model_quantized.onnx	573 MB	Dynamic INT8 quantization (default)
Q4F16	onnx/model_q4f16.onnx	517 MB	INT4 weights + FP16 activations

Usage

qwen3-embed

pip install qwen3-embed

from qwen3_embed import TextCrossEncoder

# INT8 (default)
reranker = TextCrossEncoder("n24q02m/Qwen3-Reranker-0.6B-ONNX")
scores = list(reranker.rerank("What is AI?", ["AI is...", "Pizza is..."]))

# Custom instruction
scores = list(reranker.rerank(
    "What is AI?",
    ["doc1", "doc2"],
    instruction="Judge document relevance for code search.",
))

# Q4F16 (smaller, slightly less accurate)
reranker_q4 = TextCrossEncoder("n24q02m/Qwen3-Reranker-0.6B-ONNX-Q4F16")

fastembed

pip install fastembed

from fastembed import TextCrossEncoder

# INT8 (default)
reranker = TextCrossEncoder("Qwen/Qwen3-Reranker-0.6B")
scores = list(reranker.rerank("What is AI?", ["AI is...", "Pizza is..."]))

# Q4F16
reranker_q4 = TextCrossEncoder("Qwen/Qwen3-Reranker-0.6B-Q4F16")

Note: fastembed support requires PR #605 or install from fork: pip install git+https://github.com/n24q02m/fastembed.git@feat/qwen3-support

Conversion Details

• Source: Qwen/Qwen3-Reranker-0.6B
• ONNX opset: 21
• INT8: onnxruntime.quantization.quantize_dynamic (QInt8)
• Q4F16: MatMulNBitsQuantizer (block_size=128, symmetric) + FP16 cast

Related

• GGUF variants: n24q02m/Qwen3-Reranker-0.6B-GGUF