Inference fails on Windows with non-AVX CPU (Intel N6000)

Hello,

I am attempting to run the BitNet model on a Windows 11 machine with an Intel N6000 CPU, which does not have AVX/AVX2 support. The installation completes, but inference results in a repeating character output (e.g., "GGGGGG...").

Key Findings:

• This behavior is reproducible on my Intel N6000 machine.
• I can successfully compile and run the same model on a Raspberry Pi 4 B, which proves that AVX is not a fundamental requirement for the model's logic. This suggests the bug is specific to the Windows x86 non-AVX build.

Steps to Reproduce:

1. On a Windows machine with a non-AVX CPU (e.g., Intel N6000), follow the standard installation instructions.
2. During the build process, a compilation error occurs in 3rdparty/llama.cpp/common/common.cpp due to a missing header. Adding #include <chrono> fixes this initial error.
3. The project then compiles successfully.
4. Running inference with a command like python run_inference.py -m models/BitNet-b1.58-2B-4T/ggml-model-i2_s.gguf -p "Once upon a time" results in a repeating character output.

What I've Tried:

• Compiling with the default settings.
• Forcing a build with -DLLAMA_SSE4_2=ON.
• Forcing a generic build with no flags.

All of these configurations compile successfully but produce the same incorrect inference output. The system_info log confirms that AVX is disabled.

This seems to be a bug in the x86 fallback code path when compiled with the Windows toolchain.

Start with 3rdparty/llama.cpp/common/common.cpp and the x86 fallback path, then reproduce through run_inference.py using the reported BitNet model and prompt on a non-AVX Windows CPU. Compare the system_info output and inference behavior with the Raspberry Pi 4 build; done means the Windows N6000 produces normal text instead of repeating characters without regressing supported builds.