feat(openchat): Mistral-7B inference engine (GQA + RoPE + RMSNorm + SiLU) OpenChat 3.5 / Mistral-7B architecture, fully distinct from GPT-2: - GQA: 32 query heads share 8 KV heads (4:1 ratio, 75% KV cache savings) - RoPE: rotary positional embedding (no learned positions) - RMSNorm: simpler norm without mean subtraction (both in models::layers) - SiLU: gated MLP (gate * up → down) with F32x16 element-wise SIMD - GGUF weight loading via hpc::gguf (Q4_K_M + Q4_0 dequantization added) - CausalEdge64 emission from attention patterns - OpenChat chat template (GPT4 Correct User/Assistant markers) - /v1/chat/completions API types All ops through crate::simd::F32x16 via models::layers. No weights stored — loaded at runtime from user-provided GGUF. 15 tests passing. 77 total across new modules. https://claude.ai/code/session_01Y69Vnw751w75iVSBRws7o7 #45