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>

validate_ref.py

@@ -0,0 +1,17 @@
+"""Genera il riferimento con transformers (greedy) e lo salva. Va lanciato da solo (usa ~13GB)."""
+import json, torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+PROMPT = "The capital of France is"
+N = 12
+tok = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
+ids = tok(PROMPT, return_tensors="pt").input_ids
+model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924",
+                                             torch_dtype=torch.bfloat16, low_cpu_mem_usage=True).eval()
+with torch.no_grad():
+    out = model.generate(ids, max_new_tokens=N, do_sample=False)
+full = out[0].tolist()
+json.dump({"prompt": PROMPT, "prompt_ids": ids[0].tolist(), "full_ids": full,
+           "text": tok.decode(full)}, open("ref.json", "w"))
+print("RIFERIMENTO salvato:", repr(tok.decode(full)))
+print("full_ids:", full)