Add Qwen2.5-32B-BitNet support with per-linear RMSNorm

[vladimirmoushkov]

Summary

Adds support for running QAT-trained Qwen2.5-32B-BitNet models that use
per-linear RMSNorm — a separate RMSNorm layer applied before each quantized
linear projection to stabilize ternary activations.

Changes

Patch file for 3rdparty/llama.cpp/src/llama.cpp:

• 7 new tensor types for per-linear RMSNorm weights:
  attn_q.rms_norm, attn_k.rms_norm, attn_v.rms_norm,
  attn_output.rms_norm, ffn_gate.rms_norm, ffn_down.rms_norm,
  ffn_up.rms_norm
• Qwen2 forward pass: applies RMSNorm before Q/K/V/O attention
  projections and gate/up/down FFN projections when norm weights are present
• 448 optional tensors (7 per layer × 64 layers) loaded from GGUF
• No regression: tensors are loaded with TENSOR_NOT_REQUIRED — when
  absent (standard models), the existing code path runs unchanged

Context

Qwen2.5-32B can be converted to ternary {-1, 0, +1} weights via
Quantization-Aware Training (QAT) with Straight-Through Estimator. The
per-linear RMSNorm stabilizes activations entering each ternary matmul,
which was key to reaching loss 1.93 at 32B scale. Without it, ternary
quantization at this scale produces divergent activations.

Results

• Model size: 62 GB (F16) → 11 GB (TL2) — 5.6× compression
• Inference speed: 2.4 tok/s generation (Xeon Gold 6148, 8 threads)
• Output quality: Coherent, domain-specific responses

Testing

• Tested with Qwen2.5-32B-BitNet QAT model (step 6500, loss 1.93)
• Verified standard Qwen2 models (without RMSNorm weights) still work — no regression
• Tested in both Debug and Release builds

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