colibrì: pure-C GLM-5.2 (744B MoE) engine with disk-streamed experts

Engine (c/glm.c): MLA attention with compressed KV, sigmoid noaux_tc router,
int8/int4/int2 quant kernels (AVX2), per-layer LRU expert cache + pinned
hot-store, batch-union MoE, native MTP speculative decoding (lossless),
multi-stop + official chat template, RAM auto-budget from MemAvailable.
Tokenizer: byte-level BPE in C. Tooling: coli CLI, disk-safe FP8→int4
converter, tiny-random oracle validation (TF 32/32, greedy 20/20).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

profile_run.py

@@ -0,0 +1,18 @@
+"""Profila dove va il tempo: lettura expert dal disco vs attenzione vs moe vs matmul."""
+import cProfile, pstats, io, glob, json
+from engine import OlmoeStreaming
+
+snap = glob.glob("/home/vincenzo/.cache/huggingface/hub/models--allenai--OLMoE-1B-7B-0924/snapshots/*")[0]
+ref = json.load(open("ref.json"))
+m = OlmoeStreaming(snap, expert_cap=16)
+
+pr = cProfile.Profile()
+pr.enable()
+m.generate(ref["prompt_ids"], 8, greedy=True)   # 8 token bastano per il profilo
+pr.disable()
+
+s = io.StringIO()
+ps = pstats.Stats(pr, stream=s).sort_stats("tottime")
+ps.print_stats(15)
+print(s.getvalue())
+print(f"Hit-rate: {m.cache.hitrate()*100:.1f}%  hit={m.cache.hits} miss={m.cache.miss}")