Lumen v0.1.0 — end-to-end LLM inference compiler in Rust

First public milestone of Lumen — a from-scratch LLM inference compiler + runtime in Rust.

What's in this release

• Self-built x86_64 instruction encoder (REX/VEX, ModR/M, SIB, AVX2+F16C+FMA)
• Register-tile (4×8) matmul: 57 GFLOPS / 19× scalar in standalone bench
• IR-level auto-synthesis of dequant×matmul fused kernels
• GGUF v3 reader + GPT-2 byte-level BPE tokenizer
• Full Qwen2.5-0.5B (24 layers, GQA 14Q/2KV, RoPE, SiLU FFN, KV cache) forward pass
• Shape-keyed JIT cache (MatmulJitCache) feeding every weight matmul
• Bit-identical tokens between naive Rust path and JIT path

Production runtime dependencies: thiserror — that's the entire list.

Benchmark (Qwen2.5-0.5B-Instruct-Q8_0, single thread, Windows AVX2)

Path	tg32 tok/s	vs llama.cpp
Lumen naive Rust matmul	2.91	14.2× slower
Lumen JIT (4×8 tile / 1×8 AVX2)	4.43	9.3× slower
llama.cpp (ggml, b9174 AVX2)	41.32	1.0×

Honest gap breakdown and next-10× roadmap in BENCHMARK.md.

What's next (toward v0.2)

• Cache-blocked matmul (outer block loop around the 4×8 tile)
• Q8×F32 fused matmul on the model forward path (no dequant pass)
• Flash-style attention
• Multi-thread prefill

Read the story

• End-to-end retrospective (KR): docs/blog/phase7-v0.1.0-end-to-end-ko.md
• Architecture: docs/ARCHITECTURE.md
• Plan: PLAN.md