#!/usr/bin/env python3 """Expert Atlas (#175): measure per-expert topic affinity by diffing .coli_usage across themed probe batches, served through a running colibri API server. Protocol per category: snapshot .coli_usage -> send probes -> snapshot again; the delta is that category's expert-activation spectrum. One engine load total. Output: experts.json — for every (layer, expert): counts per category, normalized affinity, entropy, and a "specialist" label when one topic dominates. Usage (server already running with the model): python3 tools/expert_atlas.py --api http://127.0.0.1:8000 \ --usage /path/to/model/.coli_usage --out experts.json --ngen 64 """ import argparse, json, math, time, urllib.request PROBES = { "code": [ "Write a Python function that parses a CSV file and returns a dict keyed by the first column.", "Explain the difference between a mutex and a semaphore, with a C example.", "Refactor this into idiomatic Rust: for i in range(len(xs)): total += xs[i] * 2", ], "math": [ "Prove that the square root of 2 is irrational.", "Compute the derivative of x^3 * ln(x) and explain each step.", "A fair die is rolled 4 times. What is the probability of at least one six?", ], "chinese": [ "请用中文解释一下什么是光合作用,以及它对地球生态系统的重要性。", "把这句话翻译成中文并解释语法:The early bird catches the worm.", "写一段关于秋天的短文,一百字左右。", ], "english_prose": [ "Write a vivid paragraph describing an old lighthouse keeper watching a storm arrive.", "Summarize the plot of Romeo and Juliet in three sentences.", "Continue this story: The last train left the station, and Maria realized her mistake.", ], "science": [ "Explain how mRNA vaccines work at the cellular level.", "Why is the sky blue during the day but red at sunset?", "Describe the life cycle of a massive star, from formation to supernova.", ], "law": [ "Explain the difference between a patent, a trademark, and a copyright.", "What are the key elements required to form a legally binding contract?", "Summarize what 'due process' means in constitutional law.", ], "poetry": [ "Write a short poem about a hummingbird in the style of Emily Dickinson.", "Compose a haiku about winter rain, then explain its imagery.", "Write four rhyming lines about the sea at night.", ], "structured": [ 'Convert to JSON: name Alice, age 30, hobbies reading and chess, address 5 Oak St.', "Write a SQL query returning the top 5 customers by total order value, with the schema you assume.", "Write a regex that matches ISO-8601 dates and explain each part.", ], "translation": [ "Translate into French, German and Spanish: 'Knowledge is the only treasure that grows when shared.'", "Translate this Italian sentence to English and comment on nuance: 'In bocca al lupo per domani.'", "Translate into Japanese: 'The meeting has been moved to next Tuesday afternoon.'", ], "casual": [ "Hey! Any tips for staying awake during boring afternoon meetings?", "What should I cook tonight? I have eggs, rice, tomatoes and some cheese.", "My friend is always late. How do I tell them it bothers me without being rude?", ], } def read_usage(path): counts = {} try: with open(path) as f: for line in f: p = line.split() if len(p) == 3: counts[(int(p[0]), int(p[1]))] = int(p[2]) except FileNotFoundError: pass return counts def diff(after, before): return {k: v - before.get(k, 0) for k, v in after.items() if v - before.get(k, 0) > 0} def chat(api, prompt, ngen): body = json.dumps({"model": "glm-5.2-colibri", "stream": False, "max_tokens": ngen, "messages": [{"role": "user", "content": prompt}]}).encode() req = urllib.request.Request(f"{api}/v1/chat/completions", data=body, headers={"Content-Type": "application/json"}) with urllib.request.urlopen(req, timeout=600) as r: json.load(r) def main(): ap = argparse.ArgumentParser() ap.add_argument("--api", default="http://127.0.0.1:8000") ap.add_argument("--usage", required=True, help="path to the model's .coli_usage") ap.add_argument("--out", default="experts.json") ap.add_argument("--ngen", type=int, default=64) a = ap.parse_args() spectra = {} for cat, prompts in PROBES.items(): before = read_usage(a.usage) t0 = time.time() for p in prompts: chat(a.api, p, a.ngen) time.sleep(2) # let the engine flush .coli_usage spectra[cat] = diff(read_usage(a.usage), before) total = sum(spectra[cat].values()) print(f"[{cat}] {len(spectra[cat])} experts touched, {total} selections, {time.time()-t0:.0f}s", flush=True) cats = list(PROBES.keys()) experts = {} for cat, spec in spectra.items(): for k, v in spec.items(): experts.setdefault(k, {c: 0 for c in cats})[cat] = v atlas = {} for (layer, eid), counts in experts.items(): total = sum(counts.values()) if total < 8: continue # too few observations to characterise aff = {c: v / total for c, v in counts.items() if v} ent = -sum(p * math.log2(p) for p in aff.values()) top = max(aff, key=aff.get) label = f"specialist: {top}" if aff[top] >= 0.45 and ent < 2.2 else "generalist" atlas[f"{layer}:{eid}"] = {"counts": counts, "affinity": {c: round(p, 3) for c, p in aff.items()}, "entropy": round(ent, 2), "top": top, "label": label} spec_n = sum(1 for v in atlas.values() if v["label"].startswith("specialist")) with open(a.out, "w") as f: json.dump({"categories": cats, "ngen": a.ngen, "experts": atlas}, f) print(f"\natlas: {len(atlas)} experts characterised, {spec_n} specialists -> {a.out}") if __name__ == "__main__": main()