Enable static quantization for Qwen3-0.6B decoder (transformer-only)

[spalne]

Introduces a post-training quantization pipeline for the Qwen3-0.6B decoder in winml-cli. The flow runs after the standard composite build: ad-hoc ONNX surgery strips the embedding Gather and lm_head MatMul from both decoder_prefill and decoder_gen, exposing inputs_embeds as the graph input and output_hidden_states as the graph output. The resulting transformer-only graphs are then statically quantized to w8a16 (uint8 weights, uint16 activations, QDQ format, per-tensor, MinMax calibration) using a PyTorch-backed calibration reader that runs HF embed_tokens over real chat-templated prompts to produce realistic inputs_embeds, attention masks, position IDs, and zero-initialized KV cache feeds.

To run -
set QUANTIZE=1
python "test_qwen 2.py"

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