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>

quant_test.py

@@ -0,0 +1,22 @@
+"""La domanda che conta: a quanti bit l'output degli expert REGGE ancora?
+Quantizzo solo gli expert (la parte densa resta bf16) e confronto col riferimento."""
+import json, glob
+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"))
+exp = ref["full_ids"][len(ref["prompt_ids"]):]
+n_new = len(exp)
+
+print(f"{'bit':>4} {'MB/expert':>10} {'match':>7}  testo")
+for bits in (16, 8, 4, 3, 2):
+    m = OlmoeStreaming(snap, expert_cap=64, quant_bits=bits)  # cap64: isola l'effetto quant
+    out = m.generate(ref["prompt_ids"], n_new, greedy=True)
+    gen = out[len(ref["prompt_ids"]):]
+    match = sum(a == b for a, b in zip(gen, exp))
+    mb = 6.29 * bits / 8 / 1.0  # ~6.29M param/expert * bit / 8 -> MB
+    # decode testo per vedere se e' ancora sensato
+    from transformers import AutoTokenizer
+    tok = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
+    txt = tok.decode(gen)
+    print(f"{bits:>4} {mb:>9.1f}M {match:>4}/{n_new:<2}  {txt!r}")