broadfield-dev/Qwen3-0.6B-1767563653-onnx

ONNX Export: Qwen/Qwen3-0.6B

This is a version of Qwen/Qwen3-0.6B that has been converted to ONNX and optimized.

Model Details

• Base Model: Qwen/Qwen3-0.6B
• Task: text-generation
• Opset Version: 17
• Optimization: FP32 (No Quantization)

Usage

Installation

For a lightweight mobile/serverless setup, you only need onnxruntime and tokenizers.

pip install onnxruntime tokenizers optimum

Python Example

from tokenizers import Tokenizer
import onnxruntime as ort
import numpy as np

# 1. Load the lightweight tokenizer (No Transformers dependency needed)
tokenizer = Tokenizer.from_pretrained("broadfield-dev/Qwen3-0.6B-onnx")

# 2. Load the ONNX model
# For Generative/Chat models, use: optimum.onnxruntime.ORTModelForCausalLM
session = ort.InferenceSession("model.onnx")

# 3. Preprocess (Simple text encoding)
text = "Run inference on mobile!"
encoding = tokenizer.encode(text)

# Prepare inputs (Exact names vary by model, usually input_ids + attention_mask)
inputs = {
    "input_ids": np.array([encoding.ids], dtype=np.int64),
    "attention_mask": np.array([encoding.attention_mask], dtype=np.int64)
}

# 4. Run Inference
outputs = session.run(None, inputs)
print("Output logits shape:", outputs[0].shape)

About this Export

This model was exported using Optimum. It includes the FP32 (No Quantization) quantization settings and a pre-compiled tokenizer.json for fast loading.