ThinTensor Improvements

• Universal semantic-IR runtime for mixed attention, dense and MoE architectures.
• Per-tensor precision, kernel and residency planning instead of layer-wide quantization.
• Production FP8, INT4, MXFP4, Q2 and Q1 quantizers with calibration-aware scoring.
• Hard ≥0.995 cosine certification with exact routing, greedy-token and KV checks.
• Qwen3.5 hybrid linear-attention, shared-expert and packed rank-3 MoE support.
• Durable grouped-INT4 expert sidecars with bounded-RAM, mmap-based construction.
• Fixed four-matrix Triton projection, cuda/cuda:0, layer-zero and converter-role bugs.
• Improved fused kernels, KV caching, expert routing and decode hot paths.
• Strict cache identity across model, tokenizer, GPU, CUDA, kernels and calibration.
• Remaining goal: prove ≥30 tok/s on Qwen3.5-35B-A3B at certified ≥0.995 cosine.
• Getting way better speed on 27B dense models at a cosine that is also way better