Heuremen LLC: YO v4 card, adapter, audit tool, license

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- quantum-computing
+- ibm-quantum
+- calibration
+- audit
+- lora
+- mlx
+---
+
+# YO
+
+YO is a small open model that audits IBM Quantum calibration records. It is Qwen3-1.7B with a LoRA adapter, trained by Heuremen LLC on IBM's own published calibration history. This repository holds the merged model (root), the adapter alone (`adapter/`), and a tool that runs it on a whole record (`yo_audit.py`).
+
+## What it does
+
+Shown a record's last-calibration stamp and eight of its qubits, YO flags:
+
+- **stale**: qubits whose T1 or T2 was measured more than 24 hours before the stamp
+- **zero**: error values of exactly 0
+
+It also gives a four-outcome verdict (ABOVE, BELOW, CONSISTENT, CANNOT_TELL) on a measured value against a stated reference.
+
+These rules can be computed exactly by code, and `yo_audit.py` prints the code's answer beside YO's on every run. Where they differ, the code is right.
+
+## Run it
+
+Save a backend's calibration record from Qiskit (`backend.properties().to_dict()`, written with `json.dump(..., default=str)`), then:
+
+```
+pip install mlx-lm                       # Apple silicon
+python yo_audit.py record.json --model indahl/YO
+
+pip install transformers torch           # anywhere
+python yo_audit.py record.json --fused indahl/YO
+```
+
+Every qubit with a T1 and a T2 value and date is audited, eight per prompt, in the same format YO was trained on.
+
+## How it scored
+
+On IBM records held out from training (426 records from 2 to 31 October 2026 that no earlier test used):
+
+- The everyday mix of records: 193 of 215 stale qubits found (YO v2, the earlier model: 119).
+- Records where all 8 qubits were stale: the stale list exactly right on 130 of 136 (YO v2: 53). YO still flags nothing on 3 of them and one qubit on 3.
+- Of the qubits YO flags as stale, 1,127 of 1,128 are stale.
+- Zero values: 115 of 119 found, with 4 false flags.
+- Four-outcome verdicts: 390 of 400.
+- Weaker on some processors: of the stale qubits in records near the 24 hour line, 120 of 135 found on ibm_boston and 72 of 80 on ibm_kingston, all of them on the other four.
+
+One training run, one seed, scored once. The plan was committed before any data was built. Plan, code and every reading: https://github.com/Heuremen-LLC/indahl.ai/tree/main/code/yo . The results table: https://indahl.ai/iris/lab
+
+## Live
+
+YO reads each new IBM calibration record as it is published, beside the code's answer, at https://indahl.ai/qia
+
+## License
+
+Apache-2.0, as the base model Qwen/Qwen3-1.7B. Trained on IBM Quantum calibration records as IBM publishes them.

adapter/adapter_config.json

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+{
+  "adapter_path": "adapter",
+  "batch_size": 4,
+  "clear_cache_threshold": 0,
+  "config": "yo4.yaml",
+  "data": "built by code from IBM calibration records (yo4_data.py)",
+  "fine_tune_type": "lora",
+  "grad_accumulation_steps": 1,
+  "grad_checkpoint": true,
+  "iters": 1200,
+  "learning_rate": 5e-05,
+  "lora_parameters": {
+    "rank": 8,
+    "dropout": 0.0,
+    "scale": 20.0
+  },
+  "lr_schedule": null,
+  "mask_prompt": true,
+  "max_seq_length": 2048,
+  "model": "Qwen/Qwen3-1.7B",
+  "num_layers": 16,
+  "optimizer": "adam",
+  "optimizer_config": {
+    "adam": {},
+    "adamw": {},
+    "muon": {},
+    "sgd": {},
+    "adafactor": {}
+  },
+  "project_name": null,
+  "report_to": null,
+  "resume_adapter_file": "the YO v2 adapter",
+  "save_every": 200,
+  "seed": 44,
+  "steps_per_eval": 200,
+  "steps_per_report": 20,
+  "test": false,
+  "test_batches": 500,
+  "train": true,
+  "val_batches": 50
+}

adapter/adapters.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4737100d6e008afab40bfe5c6552bc5a61fda02b164c5954c7a4e85bc19b7e18
+size 19947012

yo_audit.py

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+"""yo_audit.py -- audit an IBM Quantum calibration record with YO, beside the exact answer computed by code.
+
+  python yo_audit.py record.json --model indahl/YO          (Apple silicon, mlx-lm)
+  python yo_audit.py record.json --fused indahl/YO          (anywhere, transformers)
+  python yo_audit.py record.json --adapter PATH            (the adapter on top of Qwen/Qwen3-1.7B)
+
+record.json is a backend's calibration record in Qiskit's BackendProperties format (backend.properties().to_dict(), saved with
+json.dump(..., default=str)); .json.gz also works. Every qubit with a T1 and a T2 value and date is audited, eight per prompt, as YO was
+trained. YO flags (1) stale: qubits whose T1 or T2 was measured more than 24 hours before the record's last_update_date, and (2) zero: error
+values of exactly 0. The code computes the same two lists exactly; both are printed, with every difference.
+
+Apple silicon: pip install mlx-lm. Elsewhere: pip install transformers torch. Both read the merged model from huggingface.co/indahl/YO."""
+import json, gzip, sys, argparse, datetime as dt
+UTC = dt.timezone.utc
+FIELDS = ['T1', 'T2', 'readout_error', 'prob_meas1_prep0', 'prob_meas0_prep1']; ERRS = ['readout_error', 'prob_meas1_prep0', 'prob_meas0_prep1']
+RULE_A = ('You audit IBM quantum calibration records. Flag (1) "stale": qubits whose T1 or T2 measurement date is more than 24 hours '
+          'before the record\'s last-calibration stamp; (2) "zero": qubit:field pairs whose error value is exactly 0. '
+          'Answer only JSON: {"stale": [qubit numbers], "zero": ["q:field", ...]}, lists sorted.')
+def when(s):
+    d = dt.datetime.fromisoformat(str(s).replace('Z', '+00:00')); return (d if d.tzinfo else d.replace(tzinfo=UTC)).astimezone(UTC)
+def hm(d): return d.strftime('%Y-%m-%d %H:%M')
+def g4(v): return f'{v:.4g}'
+def load_record(path):
+    d = json.load(gzip.open(path) if path.endswith('.gz') else open(path)); S = when(d['last_update_date']); Q = {}
+    for i, qs in enumerate(d['qubits']):
+        e = {p['name']: (p.get('value'), p.get('date')) for p in qs if p.get('name') in FIELDS}
+        if all(f in e and e[f][0] is not None and e[f][1] for f in ('T1', 'T2')): Q[i] = e
+    return d.get('backend_name', 'backend'), S, Q
+def prompt_and_answer(b, S, Q, qs):   # the training format, unchanged
+    lines, stale, zero = [], [], []
+    for q in sorted(qs):
+        e = Q[q]; parts = []
+        for f in FIELDS:
+            if f not in e or e[f][0] is None: continue
+            v, t = e[f]; parts.append(f'{f}={g4(v)} ({hm(when(t))})' if t else f'{f}={g4(v)}')
+            if f in ('T1', 'T2') and (S - when(t)).total_seconds() > 86400 and q not in stale: stale.append(q)
+            if f in ERRS and v == 0: zero.append(f'{q}:{f}')
+        lines.append(f'q{q}: ' + ', '.join(parts))
+    return f'{RULE_A}\nBackend {b}, last-calibration stamp {hm(S)} UTC.\n' + '\n'.join(lines), {'stale': sorted(stale), 'zero': sorted(zero)}
+def groups(qs):   # eight per prompt; a short last group takes the last eight qubits so every prompt holds eight, as in training
+    qs = sorted(qs); out = [qs[i:i + 8] for i in range(0, len(qs), 8)]
+    if len(out) > 1 and len(out[-1]) < 8: out[-1] = qs[-8:]
+    return out
+def parse(out):
+    try: p = json.loads(out[out.index('{'):out.rindex('}') + 1]); return set(int(x) for x in p.get('stale', [])), set(str(x) for x in p.get('zero', []))
+    except Exception: return None
+if __name__ == '__main__':
+    ap = argparse.ArgumentParser(description=__doc__.split('\n\n')[0]); ap.add_argument('record'); ap.add_argument('--adapter'); ap.add_argument('--model', default='Qwen/Qwen3-1.7B')
+    ap.add_argument('--fused'); a = ap.parse_args()
+    b, S, Q = load_record(a.record)
+    if len(Q) < 8: sys.exit(f'{b}: fewer than 8 qubits with T1 and T2 values and dates, nothing to audit')
+    if a.fused:
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        tok = AutoTokenizer.from_pretrained(a.fused); model = AutoModelForCausalLM.from_pretrained(a.fused)
+        def ask(p):
+            t = tok.apply_chat_template([{'role': 'user', 'content': p}], tokenize=False, add_generation_prompt=True, enable_thinking=False)
+            ids = tok(t, return_tensors='pt'); o = model.generate(**ids, max_new_tokens=200, do_sample=False)
+            return tok.decode(o[0][ids['input_ids'].shape[1]:], skip_special_tokens=True)
+    else:
+        if not a.adapter and a.model == 'Qwen/Qwen3-1.7B': sys.exit('pass --model indahl/YO (the merged YO), or --adapter with the base model, or --fused for transformers')
+        from mlx_lm import load, generate
+        model, tok = load(a.model, adapter_path=a.adapter)
+        def ask(p): return generate(model, tok, prompt=tok.apply_chat_template([{'role': 'user', 'content': p}], tokenize=False, add_generation_prompt=True, enable_thinking=False), max_tokens=200, verbose=False)
+    ys, yz, cs, cz, unread = set(), set(), set(), set(), 0
+    for g in groups(Q):
+        p, ans = prompt_and_answer(b, S, Q, g); r = parse(ask(p))
+        cs |= set(ans['stale']); cz |= set(ans['zero'])
+        if r is None: unread += 1; continue
+        ys |= r[0] & set(g); yz |= r[1]
+    print(f'{b}, record stamp {hm(S)} UTC, {len(Q)} qubits audited in {len(groups(Q))} prompts' + (f', {unread} replies not readable as JSON' if unread else ''))
+    print('stale  YO:', sorted(ys)); print('stale  code:', sorted(cs)); print('zero   YO:', sorted(yz)); print('zero   code:', sorted(cz))
+    for name, y, c in (('stale', ys, cs), ('zero', yz, cz)):
+        if y - c: print(f'{name}: YO flagged, code did not:', sorted(y - c))
+        if c - y: print(f'{name}: code flagged, YO did not:', sorted(c - y))
+    print('YO and the code agree on every flag.' if (ys == cs and yz == cz and not unread) else 'The code\'s lists are exact. Where YO differs, the code is right.')