feat(intern-decision): stack, DNS and GPU 3 acceptance for the SemIf replacement

stacks/intern-decision: compose (GPU 1, :8033, hard VRAM cap as the single .env knob, healthcheck, Homepage group 'AI - Eval & Retrieval'), .env.example and README. dns: intern-decision.fv.internal -> fv-ml1 (synced to ana/esh/nh3). acceptance on fv-ml1 GPU 3, 3 fresh processes: bit-identical to the Jev bench's native rows (pooled 240/259, Wyrd 79/84, 0/560 flips, Δp 0), negative control 10/122/14, 0 flips across restarts; largest accepted request 200 at a 10,134 MiB card peak under a 9.25 GiB cap; 503 and recovery proven at a tight cap. GPU 1 deploy held: nvidia-smi Free on GPU 1 is 15,442 MiB.
2026-09-30 09:38:00 -07:00
parent 21d16d7ad8
commit a262477a61
59 changed files with 11969 additions and 1 deletions
@@ -127,4 +127,5 @@ aliases:
  - {name: wherethef, site: nh3, target: nh3-dev,    note: WhereTF :8093}
  - {name: homepage,  site: esh, target: esh-docker-vm, note: fleet dashboard :5100}
  - {name: scriberr,  site: fv,  target: fv-ml1,      note: transcription + diarization :8080 (GPU1)}
-  - {name: semif,     site: fv,  target: fv-ml1,      note: SemIf option-logit decisions :8032 (GPU1)}
+  - {name: semif,     site: fv,  target: fv-ml1,      note: SemIf option-logit decisions :8032 (GPU1) — stopped since 2026-09-30, being replaced by intern-decision (Prime); kept as rollback}
  - {name: intern-decision, site: fv, target: fv-ml1, note: Intern-Decision-4B typed decisions :8033 (GPU1), semif-serve API; replaces semif (Prime 2026-09-30)}
@@ -0,0 +1,243 @@
 """intern-decision-serve acceptance: the service-level checks the bench harness does not cover.
 Stdlib only, so it runs on fv-ml1's host python or on nh3-dev against a live instance.
    python3 checks.py --url URL --token-file T --out out.json --checks auth,chunking,queue,maxreq
    python3 checks.py --url URL --token-file T --out oom.json --checks oom
  auth      401 without / with a wrong token on both POSTs; /health open
  chunking  20 decisions in one /decide/shared equal the same decisions asked as 16 + 4
  queue     past MAX_QUEUE requests in progress, the rest get 429 busy (and the admitted all answer)
  maxreq    the largest request the API accepts: MAX_DECISIONS decisions x 16 options, the state
            grown until each 16-question call sits just under MAX_TOKENS (found from the model's own
            422 message). Sent --repeats times; status, tokens and /health's allocator peak recorded.
  fit       under the cap, the largest 16-question call that answers 200 (binary search, in tokens)
  oom       the over-cap request (maxreq's body when it ran) -> 503 out_of_memory, then reserved memory
            back to its resting value and an ordinary request answered 200. Earlier text: a long request against a deliberately tight cap -> 503 out_of_memory, then reserved
            memory back to its resting value and an ordinary request answered 200.
 Every request's status, timing and the fields that prove the point are kept in --out.
 """
 from __future__ import annotations
 import argparse
 import json
 import re
 import threading
 import time
 import urllib.error
 import urllib.request
 from pathlib import Path
 HERE = Path(__file__).resolve().parent
 YESNO = [{"id": "yes", "description": "Yes"}, {"id": "no", "description": "No"}]
 def post(url, body, headers, timeout=600):
    req = urllib.request.Request(url, data=json.dumps(body).encode(),
                                 headers={"Content-Type": "application/json", **headers})
    t = time.perf_counter()
    try:
        with urllib.request.urlopen(req, timeout=timeout) as r:
            status, raw = r.status, r.read()
    except urllib.error.HTTPError as e:
        status, raw = e.code, e.read()
    ms = (time.perf_counter() - t) * 1000
    try:
        return status, json.loads(raw), ms
    except ValueError:
        return status, {"raw": raw[:300].decode(errors="replace")}, ms
 def get(url):
    with urllib.request.urlopen(url, timeout=30) as r:
        return r.status, json.loads(r.read())
 def criteria(n, options=YESNO, prefix="c"):
    return [{"id": f"{prefix}{i}", "question": f"Does the evidence mention item number {i}?", "options": options}
            for i in range(n)]
 def check_auth(url, h):
    out = {}
    body = {"id": "a", "state": "s", "question": "q?", "options": YESNO}
    for path, b in (("/decide", body), ("/decide/shared", {"state": "s", "decisions": [body]})):
        out[path] = {"no_token": post(url + path, b, {})[0],
                     "wrong_token": post(url + path, b, {"Authorization": "Bearer " + "x" * 48})[0],
                     "right_token": post(url + path, b, h)[0]}
    out["health_no_token"] = get(url + "/health")[0]
    out["pass"] = all(v["no_token"] == 401 and v["wrong_token"] == 401 and v["right_token"] == 200
                      for k, v in out.items() if k.startswith("/")) and out["health_no_token"] == 200
    return out
 def check_chunking(url, h, state):
    crits = [{"id": f"d{i}", "question": q, "options": opts} for i, (q, opts) in enumerate([
        (f"Is item {i} a {kind}?", [{"id": "yes", "description": "Yes"}, {"id": "no", "description": "No"},
                                    {"id": "unclear", "description": "Cannot tell from the evidence"}])
        for i, kind in enumerate(["policy", "exception", "deadline", "person", "number"] * 4)])]
    s_all, j_all, ms_all = post(url + "/decide/shared", {"state": state, "decisions": crits}, h)
    s_a, j_a, _ = post(url + "/decide/shared", {"state": state, "decisions": crits[:16]}, h)
    s_b, j_b, _ = post(url + "/decide/shared", {"state": state, "decisions": crits[16:]}, h)
    split = (j_a.get("results", []) + j_b.get("results", [])) if s_a == s_b == 200 else []
    rows = []
    for whole, part in zip(j_all.get("results", []), split):
        rows.append({"id": whole["id"], "top_whole": whole["top"], "top_split": part["top"],
                     "max_dp": max(abs(x - y) for x, y in zip(whole["probabilities"], part["probabilities"])),
                     "call_whole": whole["call"], "sha_equal": whole["prompt_sha256"] == part["prompt_sha256"]})
    t = j_all.get("timing", {})
    return {"status": [s_all, s_a, s_b], "timing": t, "rows": rows, "e2e_ms": round(ms_all, 1),
            "pass": s_all == s_a == s_b == 200 and t.get("calls") == 2 and t.get("questions_per_call") == [16, 4]
            and len(rows) == 20 and all(r["top_whole"] == r["top_split"] and r["max_dp"] == 0 and r["sha_equal"]
                                        for r in rows)}
 def check_queue(url, h, state, max_queue, extra):
    n = max_queue + extra
    body = {"state": state, "decisions": criteria(16)}
    barrier = threading.Barrier(n)
    results = [None] * n
    def one(i):
        barrier.wait()
        s, j, ms = post(url + "/decide/shared", body, h)
        results[i] = (s, (j.get("error") or {}).get("code"), round(ms, 1))
    threads = [threading.Thread(target=one, args=(i,)) for i in range(n)]
    for t in threads:
        t.start()
    for t in threads:
        t.join()
    statuses = [r[0] for r in results]
    return {"sent": n, "max_queue": max_queue, "ok": statuses.count(200), "busy_429": statuses.count(429),
            "codes_429": sorted({r[1] for r in results if r[0] == 429}), "other": [r for r in results if r[0] not in (200, 429)],
            "pass": statuses.count(429) >= 1 and statuses.count(200) + statuses.count(429) == n
            and statuses.count(200) <= max_queue}
 def question_block(i):
    """Zero-padded numbers: Qwen tokenises digits one by one, so every 16-question call of a
    64-decision request has the same token count and each one sits at the limit."""
    return {"id": f"q{i:02d}", "question": f"Which of the listed categories best describes clause {i:02d} of the policy?",
            "options": [{"id": f"k{j:02d}", "description": f"Category {j:02d}: the clause concerns topic number {j:02d}"}
                        for j in range(16)]}
 def find_max_state(url, h, base_state, max_tokens, n_decisions=16):
    """Grow the state (repeating base_state) until the request is refused with 422, using the token
    count the model's own 422 reports; return the longest state the API accepts and its tokens per call.
    Sized with the WHOLE n_decisions request, so the largest call in it is the one that decides."""
    decisions = [question_block(i) for i in range(n_decisions)]
    text = (base_state + "\n\n") * 4
    def tokens_for(chars):
        s, j, _ = post(url + "/decide/shared", {"state": text[:chars], "decisions": decisions}, h)
        if s == 200:
            return s, j["timing"]["input_tokens"][0]
        if s == 503:
            return s, None
        m = re.search(r"has (\d+) tokens", (j.get("error") or {}).get("message", ""))
        return s, int(m.group(1)) if m else None
    lo, hi = 1000, len(text)
    s, tok = tokens_for(hi)
    assert s == 422 and tok and tok > max_tokens, (s, tok)
    best = None
    while lo < hi:
        mid = (lo + hi + 1) // 2
        s, tok = tokens_for(mid)
        if s in (200, 503):          # accepted by the API (503 = accepted, then refused by the VRAM cap)
            lo, best = mid, tok
        elif s == 422:
            hi = mid - 1
        else:
            raise RuntimeError(f"unexpected {s} while sizing the max request: {tok}")
    return text[:lo], best
 MAX_BODY = {}
 def check_maxreq(url, h, base_state, max_tokens, max_decisions, repeats):
    state, tokens = find_max_state(url, h, base_state, max_tokens, max_decisions)
    decisions = [question_block(i) for i in range(max_decisions)]
    body = {"state": state, "decisions": decisions}
    MAX_BODY.update(body)
    size = len(json.dumps(body).encode())
    runs = []
    for _ in range(repeats):
        s, j, ms = post(url + "/decide/shared", body, h)
        health = get(url + "/health")[1]["model"]
        runs.append({"status": s, "e2e_ms": round(ms, 1), "code": (j.get("error") or {}).get("code"),
                     "calls": (j.get("timing") or {}).get("calls"), "input_tokens": (j.get("timing") or {}).get("input_tokens"),
                     "reserved_gib_after": health.get("reserved_gib"), "max_reserved_gib": health.get("max_reserved_gib"),
                     "t_end": time.time()})
    return {"state_chars": len(state), "tokens_per_call": tokens, "decisions": max_decisions, "options_each": 16,
            "body_bytes": size, "runs": runs}
 def check_fit(url, h, base_state):
    """Under the cap: the largest 16-question call (16 options each) that still answers 200, in tokens."""
    decisions = [question_block(i) for i in range(16)]
    text = (base_state + "\n\n") * 4
    lo, hi, best, probes = 1000, len(text), None, []
    while lo < hi:
        mid = (lo + hi + 1) // 2
        s, j, _ = post(url + "/decide/shared", {"state": text[:mid], "decisions": decisions}, h)
        probes.append([mid, s, j["timing"]["input_tokens"][0] if s == 200 else None])
        if s == 200:
            lo, best = mid, j["timing"]["input_tokens"][0]
        else:
            hi = mid - 1
    return {"largest_ok_state_chars": lo, "largest_ok_tokens": best, "probes": probes}
 def check_oom(url, h, long_state):
    """The over-cap request: the largest one (maxreq) if it ran, else 16 criteria over the long state."""
    before = get(url + "/health")[1]["model"]
    body = dict(MAX_BODY) or {"state": long_state, "decisions": criteria(16)}
    s, j, ms = post(url + "/decide/shared", body, h)
    after = get(url + "/health")[1]["model"]
    s2, j2, _ = post(url + "/decide", {"id": "after", "state": "The deploy passed.", "question": "Did it pass?",
                                       "options": YESNO}, h)
    again = get(url + "/health")[1]["model"]
    return {"oom_status": s, "oom_error": j.get("error"), "oom_ms": round(ms, 1),
            "reserved_gib": {"before": before.get("reserved_gib"), "after_oom": after.get("reserved_gib"),
                             "after_next_request": again.get("reserved_gib")},
            "next_request_status": s2, "next_top": j2.get("top"),
            "pass": s == 503 and (j.get("error") or {}).get("code") == "out_of_memory" and s2 == 200
            and after.get("reserved_gib") is not None and after["reserved_gib"] <= before["reserved_gib"] + 0.5}
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--url", required=True)
    ap.add_argument("--token-file", required=True)
    ap.add_argument("--out", required=True)
    ap.add_argument("--checks", default="auth,chunking,queue,maxreq")
    ap.add_argument("--long-state-file", default=str(HERE / "long_state.txt"))
    ap.add_argument("--max-queue", type=int, default=32)
    ap.add_argument("--queue-extra", type=int, default=16)
    ap.add_argument("--repeats", type=int, default=3)
    args = ap.parse_args()
    url = args.url.rstrip("/")
    h = {"Authorization": "Bearer " + Path(args.token_file).read_text().strip()}
    long_state = Path(args.long_state_file).read_text()
    health = get(url + "/health")[1]
    report = {"url": url, "started_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), "health": health}
    for c in args.checks.split(","):
        t = time.time()
        report[c] = {"auth": lambda: check_auth(url, h),
                     "chunking": lambda: check_chunking(url, h, long_state[:4000]),
                     "queue": lambda: check_queue(url, h, long_state, args.max_queue, args.queue_extra),
                     "maxreq": lambda: check_maxreq(url, h, long_state, health["max_tokens"], health["max_decisions"],
                                                    args.repeats),
                     "fit": lambda: check_fit(url, h, long_state),
                     "oom": lambda: check_oom(url, h, long_state)}[c]()
        report[c]["t_start"], report[c]["t_end"] = t, time.time()
        print(c, json.dumps({k: v for k, v in report[c].items() if k not in ("rows", "runs")})[:600], flush=True)
    report["finished_utc"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
    Path(args.out).write_text(json.dumps(report, indent=1))
 if __name__ == "__main__":
    main()
@@ -0,0 +1,101 @@
 """Score intern-decision-serve acceptance runs of the 2026-09-30 bench harness (bench_sets.py
 --backend semif, i.e. through the service's semif-compatible API) exactly as the bench's analyze.py
 scored them, and compare them row by row with the bench's own native Intern-Decision rows.
    python3 compare.py --ours r1/sets.json r2/sets.json r3/sets.json \
        --bench ../../semif-serve/bench-jev-2026-09-30/raw/out/intern-decision-4b-native > compare.json
 Definitions (from analyze.py): a row counts when ok and labelled; right = top in gold. pooled =
 authored144 + cicada-w1 + wyrd evidence rows (259). The negative control is read against the same
 run's single condition.
 """
 from __future__ import annotations
 import argparse
 import json
 import statistics as st
 from pathlib import Path
 SETS = ("authored144", "perturbations108", "cicada-w1", "cicada-w2", "wyrd")
 POOLED = ("authored144", "cicada-w1", "wyrd")
 NAMES = ("pooled",) + SETS + ("wyrd:place", "wyrd:place2", "wyrd:exit", "wyrd:exit2")
 def rows(run: dict, cond: str) -> dict:
    c = run["conditions"].get(cond)
    return {x["id"]: x for x in c["rows"]} if c else {}
 def acc(rs) -> tuple[int, int]:
    lab = [x for x in rs if x["ok"] and x["gold"]]
    return sum(x["top"] in x["gold"] for x in lab), len(lab)
 def set_scores(run: dict) -> dict:
    out = {}
    for cond in ("single", "rotations", "multifield"):
        rr = list(rows(run, cond).values())
        if not rr:
            continue
        for name in NAMES:
            base, _, dec = name.partition(":")
            wanted = set(POOLED) if base == "pooled" else {base}
            sub = [x for x in rr if x["set"] in wanted and (not dec or x["decision"] == dec) and x["cond"] == "evidence"]
            c, n = acc(sub)
            if n:
                out[f"{cond}/{name}"] = [c, n]
        out[f"{cond}/failures"] = sum(not x["ok"] for x in rr)
    return out
 def negative(run: dict) -> dict:
    s, neg = rows(run, "single"), rows(run, "negative")
    ids = [i for i in neg if i in s and s[i]["ok"] and neg[i]["ok"]]
    return {"n": len(ids), "same_top_as_unrotated": sum(neg[i]["top"] == s[i]["top"] for i in ids),
            "follows_description": sum(neg[i]["top"] == neg[i]["gold_desc_id"] for i in ids),
            "vs_original_gold": sum(neg[i]["top"] in neg[i]["gold"] for i in ids)}
 def pdiff(a: dict, b: dict) -> float:
    return max(abs(x - y) for x, y in zip(a["probs"], b["probs"]))
 def pair(a: dict, b: dict, cond_a: str, cond_b: str) -> dict:
    ra, rb = rows(a, cond_a), rows(b, cond_b)
    ids = [i for i in ra if i in rb and ra[i]["ok"] and rb[i]["ok"]]
    flips = [i for i in ids if ra[i]["top"] != rb[i]["top"]]
    return {"rows": len(ids), "top_differs": len(flips), "flipped_ids": flips[:20],
            "max_dp": max((pdiff(ra[i], rb[i]) for i in ids), default=None)}
 def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--ours", nargs="+", required=True)
    ap.add_argument("--bench", required=True, help="raw/out/intern-decision-4b-native")
    args = ap.parse_args()
    ours = {Path(p).parent.name or p: json.loads(Path(p).read_text()) for p in args.ours}
    bench = {d.name: json.loads((d / "sets.json").read_text()) for d in sorted(Path(args.bench).glob("r*"))
             if (d / "sets.json").exists()}
    report = {"ours": {k: {"scores": set_scores(v), "negative": negative(v),
                           "a_vs_a_in_process_authored144": pair(v, v, "single", "repeat")} for k, v in ours.items()},
              "bench": {k: {"scores": set_scores(v), "negative": negative(v)} for k, v in bench.items()}}
    def bench_with(cond: str) -> str:
        """The first bench repeat that ran `cond` (multifield was added after the bench's r1)."""
        return next(k for k in sorted(bench) if cond in bench[k]["conditions"])
    report["vs_bench_row_by_row"] = {k: {cond: {"bench_repeat": bench_with(cond), **pair(v, bench[bench_with(cond)], cond, cond)}
                                         for cond in ("single", "rotations", "negative", "multifield")}
                                     for k, v in ours.items()}
    names = sorted(ours)
    report["across_restarts"] = {f"{a}~{b}": {cond: pair(ours[a], ours[b], cond, cond) for cond in ("single", "rotations")}
                                 for i, a in enumerate(names) for b in names[i + 1:]}
    keys = sorted({k for v in report["ours"].values() for k in v["scores"] if not k.endswith("/failures")})
    report["summary"] = {k: {"ours_median": st.median(v["scores"][k][0] for v in report["ours"].values() if k in v["scores"]),
                             "ours_all": [v["scores"][k][0] for v in report["ours"].values() if k in v["scores"]],
                             "bench_all": [v["scores"][k][0] for v in report["bench"].values() if k in v["scores"]],
                             "n": next(v["scores"][k][1] for v in report["ours"].values() if k in v["scores"])}
                         for k in keys}
    print(json.dumps(report, indent=1))
 if __name__ == "__main__":
    main()
@@ -0,0 +1 @@
 # pid=0 gpu=
@@ -0,0 +1,142 @@
 {
 "url": "http://127.0.0.1:18033",
 "started_utc": "2026-09-30T16:36:04Z",
 "health": {
  "status": "ok",
  "model": {
   "name": "Intern-Decision-4B",
   "source": "internlm/Intern-Decision-4B",
   "revision": "0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd",
   "checkpoint": "/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd",
   "inference_py_sha256": "c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863",
   "temperature": 1.99241824,
   "dtype": "bfloat16",
   "attn_implementation": "sdpa",
   "device": "cuda",
   "max_length": 8192,
   "torch_version": "2.10.0+cu128",
   "transformers_version": "5.17.0",
   "vision_tower": "removed",
   "device_name": "NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition",
   "allocated_gib": 7.937,
   "reserved_gib": 7.969,
   "max_reserved_gib": 8.861
  },
  "vram_cap_gib": 9.0,
  "max_tokens": 8192,
  "max_decisions": 64,
  "max_questions_per_call": 16,
  "chunking": "/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.",
  "workloads": []
 },
 "fit": {
  "largest_ok_state_chars": 4793,
  "largest_ok_tokens": 7168,
  "probes": [
   [
    31950,
    422,
    null
   ],
   [
    16475,
    422,
    null
   ],
   [
    8737,
    503,
    null
   ],
   [
    4868,
    503,
    null
   ],
   [
    2934,
    200,
    6772
   ],
   [
    3901,
    200,
    6964
   ],
   [
    4384,
    200,
    7056
   ],
   [
    4626,
    200,
    7133
   ],
   [
    4747,
    200,
    7156
   ],
   [
    4807,
    503,
    null
   ],
   [
    4777,
    200,
    7163
   ],
   [
    4792,
    200,
    7168
   ],
   [
    4799,
    503,
    null
   ],
   [
    4795,
    503,
    null
   ],
   [
    4793,
    200,
    7168
   ],
   [
    4794,
    503,
    null
   ]
  ],
  "t_start": 1790786164.5822356,
  "t_end": 1790786175.4811816
 },
 "maxreq": {
  "state_chars": 9508,
  "tokens_per_call": null,
  "decisions": 64,
  "options_each": 16,
  "body_bytes": 101019,
  "runs": [
   {
    "status": 503,
    "e2e_ms": 209.5,
    "code": "out_of_memory",
    "calls": null,
    "input_tokens": null,
    "reserved_gib_after": 7.969,
    "max_reserved_gib": 9.0,
    "t_end": 1790786178.99772
   }
  ],
  "t_start": 1790786175.481284,
  "t_end": 1790786178.9977298
 },
 "finished_utc": "2026-09-30T16:36:18Z"
 }
@@ -0,0 +1 @@
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@@ -0,0 +1,294 @@
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@@ -0,0 +1,72 @@
 {
 "url": "http://127.0.0.1:18033",
 "started_utc": "2026-09-30T16:34:04Z",
 "health": {
  "status": "ok",
  "model": {
   "name": "Intern-Decision-4B",
   "source": "internlm/Intern-Decision-4B",
   "revision": "0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd",
   "checkpoint": "/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd",
   "inference_py_sha256": "c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863",
   "temperature": 1.99241824,
   "dtype": "bfloat16",
   "attn_implementation": "sdpa",
   "device": "cuda",
   "max_length": 8192,
   "torch_version": "2.10.0+cu128",
   "transformers_version": "5.17.0",
   "vision_tower": "removed",
   "device_name": "NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition",
   "allocated_gib": 7.937,
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   "max_reserved_gib": 8.861
  },
  "vram_cap_gib": 8.9,
  "max_tokens": 8192,
  "max_decisions": 64,
  "max_questions_per_call": 16,
  "chunking": "/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.",
  "workloads": []
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 "maxreq": {
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  "decisions": 64,
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  "runs": [
   {
    "status": 503,
    "e2e_ms": 193.4,
    "code": "out_of_memory",
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 },
 "oom": {
  "oom_status": 503,
  "oom_error": {
   "code": "out_of_memory",
   "message": "CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 94.97 GiB of which 85.46 GiB is free. Including non-PyTorch memory, this process has 9.50 GiB memory in use. 8.90 GiB allowed; Of the allocated memory 8.80 GiB is allocated by PyTorch, and 57.34 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
  },
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  "next_top": "yes",
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  "t_end": 1790786054.1036348
 },
 "finished_utc": "2026-09-30T16:34:14Z"
 }
@@ -0,0 +1,305 @@
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 {
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 {
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@@ -0,0 +1,37 @@
 [
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 {
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 {
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 {
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 }
 ]
@@ -0,0 +1 @@
 {"label": "probe", "backend": "semif", "runs": 1, "per": 5, "max_questions": 10000, "events": [["start", 1790785217.097767], ["short1:measure", 1790785217.1804233], ["crit21:measure", 1790785217.5361114], ["long1:measure", 1790785224.795359], ["long16:measure", 1790785232.4314742], ["shapes:done", 1790785233.5109885], ["end", 1790785233.5109909]], "shapes": {"short1": {"rows": 1, "runs": [{"e2e_ms": [36.293586948886514, 36.00253281183541, 37.3701888602227, 36.00338310934603, 35.94383201561868], "server_ms": [34.18141300790012, 34.299023915082216, 34.113402012735605, 34.34582380577922, 34.3846851028502], "statuses": [200, 200, 200, 200, 200], "tokens": 181, "median_e2e": 36.00338310934603, "median_server": 34.299023915082216}], "tokens": 181, "median_e2e_ms": 36.0, "run_medians_e2e_ms": [36.0], "median_server_ms": 34.3, "non_200": 0}, "crit21": {"rows": 21, "runs": [{"e2e_ms": [85.47385083511472, 85.30352916568518, 85.49744100309908, 85.56356094777584, 85.31283889897168], "server_ms": [79.13649803958833, 78.94592499360442, 79.16115806438029, 79.16332804597914, 78.98750598542392], "statuses": [200, 200, 200, 200, 200], "tokens": null, "median_e2e": 85.47385083511472, "median_server": 79.13649803958833}], "tokens": null, "median_e2e_ms": 85.5, "run_medians_e2e_ms": [85.5], "median_server_ms": 79.1, "non_200": 0}, "long1": {"rows": 1, "runs": [{"e2e_ms": [193.0149649269879, 186.22795795090497, 187.29469901882112, 211.8130698800087, 182.15654999949038], "server_ms": [189.49031387455761, 182.79000790789723, 183.78369905985892, 208.4096318576485, 178.73777099885046], "statuses": [200, 200, 200, 200, 200], "tokens": 4019, "median_e2e": 187.29469901882112, "median_server": 183.78369905985892}], "tokens": 4019, "median_e2e_ms": 187.3, "run_medians_e2e_ms": [187.3], "median_server_ms": 183.8, "non_200": 0}, "long16": {"rows": 16, "runs": [{"e2e_ms": [212.7648708410561, 229.93756900541484, 210.01248992979527, 212.9045429173857, 211.45822713151574], "server_ms": [207.08801690489054, 224.23489298671484, 204.2714338749647, 207.02347601763904, 205.7694320101291], "statuses": [200, 200, 200, 200, 200], "tokens": null, "median_e2e": 212.7648708410561, "median_server": 207.02347601763904}], "tokens": null, "median_e2e_ms": 212.8, "run_medians_e2e_ms": [212.8], "median_server_ms": 207.0, "non_200": 0}}}
@@ -0,0 +1,877 @@
 # pid=1849348 gpu=GPU-186dacf4-f447-ef03-ffc3-12ab28e1b8ff
 1790785210.381560709,8812,8822
 1790785210.490009044,8812,8822
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 1790785211.051771180,8812,8822
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@@ -0,0 +1,37 @@
 """GPU memory outside torch's allocator (whole card minus 2 MiB idle minus torch reserved), at quiet points
 between loads. GPU 3 carries only this process."""
 import json, subprocess, sys, threading, time, urllib.request
 URL = "http://127.0.0.1:18033"
 TOKEN = open("token").read().strip()
 H = {"Authorization": "Bearer " + TOKEN, "Content-Type": "application/json"}
 def card():
    return int(subprocess.check_output(["nvidia-smi", "-i", "3", "--query-gpu=memory.used", "--format=csv,noheader,nounits"]).strip())
 def health():
    return json.load(urllib.request.urlopen(URL + "/health"))["model"]
 def post(path, body):
    r = urllib.request.Request(URL + path, data=json.dumps(body).encode(), headers=H)
    try:
        return urllib.request.urlopen(r).status
    except urllib.error.HTTPError as e:
        return e.code
 SMALL = {"id": "x", "state": "The deploy passed.", "question": "Did it pass?",
         "options": [{"id": "yes", "description": "Yes"}, {"id": "no", "description": "No"}]}
 out = []
 def snap(tag):
    time.sleep(1.5)
    c, h = card(), health()
    r = h["reserved_gib"] * 1024
    row = {"tag": tag, "card_mib": c, "reserved_mib": round(r), "outside_mib": round(c - 2 - r), "max_reserved_mib": round(h["max_reserved_gib"] * 1024)}
    out.append(row); print(row, flush=True)
 snap("rest")
 for burst in range(2):
    ts = [threading.Thread(target=post, args=("/decide", SMALL)) for _ in range(30)]
    [t.start() for t in ts]; [t.join() for t in ts]
    snap(f"after concurrent burst {burst + 1}")
 subprocess.run([sys.executable, "bench_shape.py", "--backend", "semif", "--url", URL, "--token-file", "token",
                "--long-state-file", "long_state.txt", "--label", "probe", "--out", sys.argv[1] + "/shape.json", "--runs", "1", "--per", "5"], check=True)
 snap("after latency shapes")
 subprocess.run([sys.executable, "checks.py", "--url", URL, "--token-file", "token", "--out", sys.argv[1] + "/checks.json",
                "--checks", "maxreq,oom", "--repeats", "2"], check=True)
 snap("after largest requests")
 json.dump(out, open(sys.argv[1] + "/overhead.json", "w"), indent=1)
@@ -0,0 +1,10 @@
 #!/bin/bash
 # poll.sh <container> <out.csv>: unix time, that container's process GPU memory (MiB, nvidia-smi per process), card total
 pid=$(docker inspect -f '{{.State.Pid}}' "$1")
 gpu=$(nvidia-smi --query-compute-apps=pid,gpu_uuid --format=csv,noheader | awk -F', ' -v p="$pid" '$1==p{print $2}')
 echo "# pid=$pid gpu=$gpu" > "$2"
 while [ -d /proc/$pid ]; do
  procmem=$(nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader,nounits | awk -F', ' -v p="$pid" '$1==p{print $2}')
  card=$(nvidia-smi --id="$gpu" --query-gpu=memory.used --format=csv,noheader,nounits)
  printf '%s,%s,%s\n' "$(date +%s.%N)" "${procmem:-0}" "$card" >> "$2"
 done
@@ -0,0 +1 @@
 1d98f12c909d19f7e157823e29412e4fb44aa22db94c6ee4a96d8453aed29c70
@@ -0,0 +1,3 @@
 1790785010.413756011 sets:start
 1790785010.470560236 sets:done
 gpu3 after: 2 MiB
@@ -0,0 +1 @@
 {"status":"ok","model":{"name":"Intern-Decision-4B","source":"internlm/Intern-Decision-4B","revision":"0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","checkpoint":"/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","inference_py_sha256":"c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863","temperature":1.99241824,"dtype":"bfloat16","attn_implementation":"sdpa","device":"cuda","max_length":8192,"torch_version":"2.10.0+cu128","transformers_version":"5.17.0","vision_tower":"removed","device_name":"NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition","allocated_gib":7.937,"reserved_gib":7.969,"max_reserved_gib":8.861},"vram_cap_gib":9.25,"max_tokens":8192,"max_decisions":64,"max_questions_per_call":16,"chunking":"/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.","workloads":[]}
@@ -0,0 +1 @@
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@@ -0,0 +1 @@
 5068d9162b114ffd3b9773ce3a001634a40605e8e9777778e586bc28475e2c9f
@@ -0,0 +1,5 @@
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 1790785525.645862796 checks:done
 gpu3 after: 2 MiB
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@@ -0,0 +1 @@
 {"status":"ok","model":{"name":"Intern-Decision-4B","source":"internlm/Intern-Decision-4B","revision":"0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","checkpoint":"/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","inference_py_sha256":"c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863","temperature":1.99241824,"dtype":"bfloat16","attn_implementation":"sdpa","device":"cuda","max_length":8192,"torch_version":"2.10.0+cu128","transformers_version":"5.17.0","vision_tower":"removed","device_name":"NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition","allocated_gib":7.937,"reserved_gib":7.969,"max_reserved_gib":8.861},"vram_cap_gib":9.25,"max_tokens":8192,"max_decisions":64,"max_questions_per_call":16,"chunking":"/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.","workloads":[]}
@@ -0,0 +1 @@
 297e8eb1cac5b35462bd8ab9c918791fb84c064372f3280596b1911dfb5c5f92
@@ -0,0 +1,3 @@
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 gpu3 after: 2 MiB
@@ -0,0 +1 @@
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@@ -0,0 +1 @@
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@@ -0,0 +1 @@
 d10e0ec2b1f031f57d6594c10548696bd8352c75b35458cf44538ead4fc2eb0a
@@ -0,0 +1,3 @@
 1790785726.691427913 sets:start
 1790785810.443650874 sets:done
 gpu3 after: 2 MiB
@@ -0,0 +1 @@
 {"status":"ok","model":{"name":"Intern-Decision-4B","source":"internlm/Intern-Decision-4B","revision":"0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","checkpoint":"/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","inference_py_sha256":"c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863","temperature":1.99241824,"dtype":"bfloat16","attn_implementation":"sdpa","device":"cuda","max_length":8192,"torch_version":"2.10.0+cu128","transformers_version":"5.17.0","vision_tower":"removed","device_name":"NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition","allocated_gib":7.937,"reserved_gib":8.082,"max_reserved_gib":8.861},"vram_cap_gib":9.25,"max_tokens":8192,"max_decisions":64,"max_questions_per_call":16,"chunking":"/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.","workloads":[]}
@@ -0,0 +1 @@
 {"status":"ok","model":{"name":"Intern-Decision-4B","source":"internlm/Intern-Decision-4B","revision":"0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","checkpoint":"/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","inference_py_sha256":"c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863","temperature":1.99241824,"dtype":"bfloat16","attn_implementation":"sdpa","device":"cuda","max_length":8192,"torch_version":"2.10.0+cu128","transformers_version":"5.17.0","vision_tower":"removed","device_name":"NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition","allocated_gib":7.937,"reserved_gib":7.969,"max_reserved_gib":8.861},"vram_cap_gib":9.25,"max_tokens":8192,"max_decisions":64,"max_questions_per_call":16,"chunking":"/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.","workloads":[]}
@@ -0,0 +1,836 @@
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@@ -0,0 +1,34 @@
 #!/bin/bash
 # run.sh <label> <cap_gib> <conditions|full>: one process lifetime of intern-decision-serve on GPU 3.
 set -u
 cd /tmp/intern-accept
 label=$1 cap=$2 mode=$3
 O=out/$label; mkdir -p "$O"
 [ "$(nvidia-smi -i 3 --query-gpu=memory.used --format=csv,noheader,nounits)" -gt 64 ] && { echo "GPU 3 not empty"; exit 10; }
 docker run -d --name intern-decision-accept --gpus '"device=3"' -p 18033:8000 --env-file env \
  -e INTERN_DECISION_DEVICE=cuda -e INTERN_DECISION_VRAM_CAP_GIB="$cap" \
  -v /tank/aimodels/huggingface:/hf:ro intern-decision-serve:0.1.0 > "$O/container.id"
 for i in $(seq 1 60); do
  [ "$(curl -s -o /dev/null -w '%{http_code}' localhost:18033/health)" = 200 ] && break
  [ "$(docker inspect -f '{{.State.Running}}' intern-decision-accept)" != true ] && { docker logs intern-decision-accept > "$O/server.log" 2>&1; echo "died"; exit 11; }
  sleep 2
 done
 ./poll.sh intern-decision-accept "$O/vram.csv" & POLL=$!
 sleep 3
 curl -s localhost:18033/health > "$O/health-rest.json"
 echo "$(date +%s.%N) sets:start" >> "$O/events.txt"
 if [ "$mode" = full ]; then conds=single,rotations,repeat,reversed,shuffled,negative,multifield; else conds=$mode; fi
 python3 bench_sets.py --backend semif --url http://127.0.0.1:18033 --token-file token --label "$label" --out "$O/sets.json" --conditions "$conds" 2>&1 | tee "$O/sets.log"
 echo "$(date +%s.%N) sets:done" >> "$O/events.txt"
 if [ "$mode" = full ]; then
  python3 bench_shape.py --backend semif --url http://127.0.0.1:18033 --token-file token --long-state-file long_state.txt --label "$label" --out "$O/shape.json" 2>&1 | tee "$O/shape.log"
  echo "$(date +%s.%N) shape:done" >> "$O/events.txt"
  python3 checks.py --url http://127.0.0.1:18033 --token-file token --out "$O/checks.json" --checks auth,chunking,queue,fit,maxreq,oom --long-state-file long_state.txt 2>&1 | tee "$O/checks.log"
  echo "$(date +%s.%N) checks:done" >> "$O/events.txt"
 fi
 curl -s localhost:18033/health > "$O/health-end.json"
 kill $POLL; wait $POLL 2>/dev/null
 docker logs intern-decision-accept > "$O/server.log" 2>&1
 docker rm -f intern-decision-accept > /dev/null
 sleep 3
 echo "gpu3 after: $(nvidia-smi -i 3 --query-gpu=memory.used --format=csv,noheader)" | tee -a "$O/events.txt"
@@ -0,0 +1,22 @@
 """Does GPU memory outside torch's allocator grow with the number of distinct threads that run a call?"""
 import json, subprocess, sys, threading, time, urllib.request
 URL = "http://127.0.0.1:18033"
 TOKEN = open("token").read().strip()
 BODY = json.dumps({"id": "x", "state": "The deploy passed.", "question": "Did it pass?",
                   "options": [{"id": "yes", "description": "Yes"}, {"id": "no", "description": "No"}]}).encode()
 def card():
    return int(subprocess.check_output(["nvidia-smi", "-i", "3", "--query-gpu=memory.used", "--format=csv,noheader,nounits"]).strip())
 def reserved():
    return json.load(urllib.request.urlopen(URL + "/health"))["model"]["reserved_gib"] * 1024
 def post():
    r = urllib.request.Request(URL + "/decide", data=BODY, headers={"Authorization": "Bearer " + TOKEN, "Content-Type": "application/json"})
    urllib.request.urlopen(r).read()
 def snap(tag):
    time.sleep(1.5); c, r = card(), reserved(); print(f"{tag}: card {c} MiB, reserved {r:.0f} MiB, outside allocator {c - 2 - r:.0f} MiB", flush=True)
 snap("start")
 for i in range(20): post()
 snap("after 20 sequential requests")
 for burst in range(3):
    ts = [threading.Thread(target=post) for _ in range(40)]
    [t.start() for t in ts]; [t.join() for t in ts]
    snap(f"after concurrent burst {burst + 1} (40 requests)")
@@ -0,0 +1,357 @@
 {
 "url": "http://127.0.0.1:18033",
 "started_utc": "2026-09-30T16:09:34Z",
 "health": {
  "status": "ok",
  "model": {
   "name": "Intern-Decision-4B",
   "source": "internlm/Intern-Decision-4B",
   "revision": "0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd",
   "checkpoint": "/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd",
   "inference_py_sha256": "c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863",
   "temperature": 1.99241824,
   "dtype": "bfloat16",
   "attn_implementation": "sdpa",
   "device": "cuda",
   "max_length": 8192,
   "torch_version": "2.10.0+cu128",
   "transformers_version": "5.17.0",
   "vision_tower": "removed",
   "device_name": "NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition",
   "allocated_gib": 7.937,
   "reserved_gib": 7.969,
   "max_reserved_gib": 8.861
  },
  "vram_cap_gib": null,
  "max_tokens": 8192,
  "max_decisions": 64,
  "max_questions_per_call": 16,
  "chunking": "/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.",
  "workloads": []
 },
 "auth": {
  "/decide": {
   "no_token": 401,
   "wrong_token": 401,
   "right_token": 200
  },
  "/decide/shared": {
   "no_token": 401,
   "wrong_token": 401,
   "right_token": 200
  },
  "health_no_token": 200,
  "pass": true,
  "t_start": 1790784574.0391982,
  "t_end": 1790784574.1316206
 },
 "chunking": {
  "status": [
   200,
   200,
   200
  ],
  "timing": {
   "total_seconds": 0.14785360009409487,
   "batch_size": 20,
   "calls": 2,
   "questions_per_call": [
    16,
    4
   ],
   "input_tokens": [
    1884,
    1328
   ],
   "inference_seconds": 0.13485
  },
  "rows": [
   {
    "id": "d0",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q1",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d1",
    "top_whole": "no",
    "top_split": "no",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q2",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d2",
    "top_whole": "no",
    "top_split": "no",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q3",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d3",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q4",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d4",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q5",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d5",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q6",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d6",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q7",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d7",
    "top_whole": "no",
    "top_split": "no",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q8",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d8",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q9",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d9",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q10",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d10",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q11",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d11",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q12",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d12",
    "top_whole": "no",
    "top_split": "no",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q13",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d13",
    "top_whole": "no",
    "top_split": "no",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q14",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d14",
    "top_whole": "no",
    "top_split": "no",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q15",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d15",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 0,
     "field": "q16",
     "questions": 16
    },
    "sha_equal": true
   },
   {
    "id": "d16",
    "top_whole": "unclear",
    "top_split": "unclear",
    "max_dp": 0.0,
    "call_whole": {
     "index": 1,
     "field": "q1",
     "questions": 4
    },
    "sha_equal": true
   },
   {
    "id": "d17",
    "top_whole": "unclear",
    "top_split": "unclear",
    "max_dp": 0.0,
    "call_whole": {
     "index": 1,
     "field": "q2",
     "questions": 4
    },
    "sha_equal": true
   },
   {
    "id": "d18",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 1,
     "field": "q3",
     "questions": 4
    },
    "sha_equal": true
   },
   {
    "id": "d19",
    "top_whole": "yes",
    "top_split": "yes",
    "max_dp": 0.0,
    "call_whole": {
     "index": 1,
     "field": "q4",
     "questions": 4
    },
    "sha_equal": true
   }
  ],
  "e2e_ms": 151.3,
  "pass": true,
  "t_start": 1790784574.1316838,
  "t_end": 1790784574.4364655
 },
 "maxreq": {
  "state_chars": 11337,
  "tokens_per_call": 8192,
  "decisions": 64,
  "options_each": 16,
  "body_bytes": 100953,
  "runs": [
   {
    "status": 422,
    "e2e_ms": 415.8,
    "code": "invalid_request",
    "calls": null,
    "input_tokens": null,
    "reserved_gib_after": 7.969,
    "max_reserved_gib": 9.557,
    "t_end": 1790784583.8459144
   },
   {
    "status": 422,
    "e2e_ms": 405.2,
    "code": "invalid_request",
    "calls": null,
    "input_tokens": null,
    "reserved_gib_after": 7.969,
    "max_reserved_gib": 9.557,
    "t_end": 1790784584.2551143
   },
   {
    "status": 422,
    "e2e_ms": 406.5,
    "code": "invalid_request",
    "calls": null,
    "input_tokens": null,
    "reserved_gib_after": 7.969,
    "max_reserved_gib": 9.557,
    "t_end": 1790784584.6652017
   }
  ],
  "t_start": 1790784574.4365265,
  "t_end": 1790784584.6653428
 },
 "finished_utc": "2026-09-30T16:09:44Z"
 }
@@ -0,0 +1 @@
 {"label": "uncapped", "backend": "semif", "runs": 1, "per": 10, "max_questions": 10000, "events": [["start", 1790784611.4584568], ["short1:measure", 1790784611.5543973], ["crit21:measure", 1790784612.0878277], ["long1:measure", 1790784619.396992], ["long16:measure", 1790784627.8208334], ["shapes:done", 1790784629.9486265], ["end", 1790784629.9486306]], "shapes": {"short1": {"rows": 1, "runs": [{"e2e_ms": [35.92049889266491, 35.901617957279086, 35.708046052604914, 35.906028002500534, 36.10570100136101, 36.089200992137194, 35.76449700631201, 36.227591102942824, 35.77947709709406, 35.997220082208514], "server_ms": [34.09949690103531, 34.24146887846291, 34.126657992601395, 34.240649081766605, 34.342060098424554, 34.322259947657585, 34.07772700302303, 34.49505195021629, 34.051917027682066, 34.29409000091255], "statuses": [200, 200, 200, 200, 200, 200, 200, 200, 200, 200], "tokens": 181, "median_e2e": 35.91326344758272, "median_server": 34.24105898011476}], "tokens": 181, "median_e2e_ms": 35.9, "run_medians_e2e_ms": [35.9], "median_server_ms": 34.2, "non_200": 0}, "crit21": {"rows": 21, "runs": [{"e2e_ms": [85.90914588421583, 85.42978088371456, 85.99224709905684, 85.41093999519944, 85.5076820589602, 85.47824202105403, 85.63234284520149, 85.61140298843384, 85.56949300691485, 85.53116186521947], "server_ms": [79.33550095185637, 78.80261586979032, 79.46493290364742, 78.93323712050915, 78.8946459069848, 78.88813596218824, 79.00104694999754, 79.05412814579904, 79.03093798086047, 79.03374801389873], "statuses": [200, 200, 200, 200, 200, 200, 200, 200, 200, 200], "tokens": null, "median_e2e": 85.55032743606716, "median_server": 79.01599246542901}], "tokens": null, "median_e2e_ms": 85.6, "run_medians_e2e_ms": [85.6], "median_server_ms": 79.0, "non_200": 0}, "long1": {"rows": 1, "runs": [{"e2e_ms": [183.08503087610006, 183.89351991936564, 184.35147497802973, 185.54342817515135, 191.41989503987134, 183.4883950650692, 185.06936286576092, 185.85462193004787, 187.36803904175758, 184.89661114290357], "server_ms": [179.3815779965371, 180.2897888701409, 180.69604295305908, 181.9530779030174, 187.8542739432305, 179.9399449955672, 181.47349287755787, 182.3184520471841, 183.79759904928505, 181.30912980996072], "statuses": [200, 200, 200, 200, 200, 200, 200, 200, 200, 200], "tokens": 4019, "median_e2e": 184.98298700433224, "median_server": 181.3913113437593}], "tokens": 4019, "median_e2e_ms": 185.0, "run_medians_e2e_ms": [185.0], "median_server_ms": 181.4, "non_200": 0}, "long16": {"rows": 16, "runs": [{"e2e_ms": [210.3101098909974, 210.82147606648505, 212.6600358169526, 211.06627909466624, 214.81614094227552, 210.01119702123106, 215.28325602412224, 212.87801885046065, 211.02875797078013, 213.75816804356873], "server_ms": [203.99634796194732, 204.5704941265285, 206.43203612416983, 204.81025706976652, 208.57751998119056, 203.7482550367713, 208.42881803400815, 205.99671080708504, 204.55457409843802, 207.47225801460445], "statuses": [200, 200, 200, 200, 200, 200, 200, 200, 200, 200], "tokens": null, "median_e2e": 211.86315745580941, "median_server": 205.40348393842578}], "tokens": null, "median_e2e_ms": 211.9, "run_medians_e2e_ms": [211.9], "median_server_ms": 205.4, "non_200": 0}}}
@@ -0,0 +1,15 @@
 # intern-decision — copy to /opt/docker/compose/intern-decision/.env on fv-ml1 (mode 0600).
 # Built on fv-ml1 from services/intern-decision-serve (see README "Building").
 IMAGE=intern-decision-serve:0.1.0
 PORT=8033
 HOST_IP=10.251.50.54
 # fv-ml1 GPU 1 = the utility card (vllm-coder, erp, meromero, scriberr).
 GPU_ID=1
 # HARD torch-allocator cap: the single knob that holds the container's WHOLE nvidia-smi footprint
 # (CUDA context included) <= 10,300 MiB, the GPU 1 budget next to scriberr (2026-09-30).
 #   footprint <= cap + non-allocator overhead = 9,472 MiB + 662 MiB = 10,134 MiB
 #   (overhead measured flat at 660-662 MiB with the single inference thread; README "VRAM").
 # 9.25 still answers the largest request the API accepts (4 calls x 8,191 tokens) with a 200.
 VRAM_CAP_GIB=9.25
 # >= 32 characters; source of truth: secret get intern-decision/api-token
 INTERN_DECISION_API_TOKEN=
@@ -0,0 +1,215 @@
 # intern-decision
 > ⚠ **STATUS 2026-09-30 0937 PT: built and accepted on GPU 3; the GPU 1 deploy is HELD.** GPU 1's
 > usable free memory is 15,442 MiB (nvidia-smi `Free`: 97,887 total − 640 driver-reserved −
 > 81,806 used), below the 15,800 MiB pre-deploy floor. The 10,300 MiB budget was derived from
 > total − used (16,081). The decision is with infra-ops/Prime; see "VRAM". Nothing is running
 > on port 8033 yet.
 **Intern-Decision-4B typed decisions** on **fv-ml1 GPU 1**, the utility card beside `vllm-coder`,
 the erp/meromero seats and scriberr. It **replaced `semif`** on Prime's ruling of 2026-09-30,
 ~0510 PT: "replace semif with intern-decision now". The pick came from the previous night's bench,
 `docs/pfi/jev-candidates-bench-2026-09-30.md`: the only candidate at least as accurate as SemIf on
 our sets, faster at our shape, and inside the memory budget.
 [Intern-Decision-4B](https://huggingface.co/internlm/Intern-Decision-4B) (Shanghai AI Lab,
 Apache-2.0) is a Qwen3.5-4B finetune. It takes a state plus up to 16 named questions and scores
 every question's options in **one forward pass with no decoding**. It reads each answer from the
 logits just before a `<decision>` marker. `services/intern-decision-serve/` loads it once and
 scores through the checkpoint's **own** `inference.py` (`DecisionEngine.predict`). It keeps
 **semif-serve's HTTP surface**, so a semif caller changes only the URL and the token. The contract
 is `services/intern-decision-serve/intern-decision-serve.contract.md`.
 | | |
 |---|---|
 | **URL** | `http://10.251.50.54:8033` = `http://intern-decision.fv.internal:8033`. `/health` is open; POSTs need `Authorization: Bearer $(secret get intern-decision/api-token)` |
 | **Model** | `internlm/Intern-Decision-4B` @ `0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd`, BF16, from `/tank/aimodels/huggingface`, mounted read-only and offline |
 | **Scoring code** | the snapshot's `inference.py`, sha256 `c904e2c6…43b29863`, checked before it is imported. Startup refuses any other file |
 | **Stack** | torch `2.10.0+cu128`, transformers `5.17.0`, fla `0.5.2`, causal-conv1d `1.7.0`: the bench's stack |
 | **Image** | `intern-decision-serve:<version>`, built on fv-ml1 from `services/intern-decision-serve/` |
 | **State** | None. The service never downloads (`HF_HUB_OFFLINE=1`, read-only mount). If the weights are ever lost, re-pull the pinned revision by hand. |
 | **Rollback** | `stacks/semif` is stopped, not removed. See **Rollback** below. |
 ## API (semif-serve's)
 ```bash
 T=$(secret get intern-decision/api-token)
 curl -s -H "Authorization: Bearer $T" http://intern-decision.fv.internal:8033/decide -d '{
  "id": "q1", "state": "Health checks passed in all three zones.",
  "question": "Did the deployment succeed?",
  "options": [{"id":"yes","description":"It succeeded."},{"id":"no","description":"It failed."}]}'
 ```
 - `POST /decide` takes one decision, `{id, state, question, options[2..16]}`.
 - `POST /decide/shared` takes `{state, decisions: [{id, question, options}]}`.
 - `GET /health` reports the pins, the limits and the chunking rule.
 - **Order averaging:** `"orderings": "rotations"`, or `"all"` for ≤ 4 options, is still accepted,
  with semif's `combined` block. **It is barely needed:** the bench found Intern-Decision reorders
  8 of 144 labels when the options are reversed, against SemIf's 30, and at one ordering it matched
  SemIf-with-rotations.
 - Each result carries semif's fields where they mean something: `option_ids`, `probabilities`,
  `input_tokens`, `prompt_sha256`, `prompt_version`, `model`, `readout`, `probability_status`, and
  on `/decide` also `total_seconds` and `forward_seconds`. New fields:
  - `top`;
  - `confidence`, the model's own;
  - `calibration`, the model's own temperature (T = 1.99241824);
  - `native`, the model's answer object, unchanged;
  - `call` (`index`, `field`, `questions`): which prompt the decision was asked in.
 - Past `MAX_QUEUE` (32) requests in progress, new POSTs get `429 busy` before their body is read.
 ### What a semif caller must know (every deliberate difference is in the contract, "Deltas")
 1. **Option ids are shown to the model.** The prompt prints `A = <id>: <description>`, so an
   id is part of the question. Use meaningful or neutral ids, not misleading ones.
 2. **Decisions in one `/decide/shared` call are asked together, in one prompt.** An answer can
   depend on the other questions in its call.
   - The bench measured this on Wyrd: 79/84 asked one decision at a time, 77/84 with a turn's
     4 decisions in one prompt.
   - A call holds **at most 16 questions**. More are split greedily in request order: 1–16,
     17–32, … . `/health` says so.
 3. **`probabilities` are temperature-scaled** by the model's shipped calibration. SemIf's were
   raw. The argmax is the same either way. This calibration is the vendor's, fitted on its own
   data, not ours.
 4. **Gone:**
   - `option_logits` (the model's runtime does not expose logits);
   - the prefix-cache timing fields;
   - `/health.semif_commit`;
   - per-workload calibration. Any `workload` is a 422, exactly as the deployed semif behaved
     with its empty table.
 5. **`MAX_TOKENS` (8192) is per call:** the state plus all its questions. A longer call is a 422
   and is never truncated. Every call up to that limit fits the VRAM cap (measured). See VRAM
   below.
 ## VRAM: the whole container ≤ 10,300 MiB
 **Budget (infra-ops, 2026-09-30):** the container's **whole** nvidia-smi footprint, CUDA context
 included, must never exceed **10,300 MiB**, whatever the request. That leaves scriberr (peak
 5,496 MiB with its 120 s slices) 295 MiB of spare even when both hit their peaks at once.
 `torch.cuda.set_per_process_memory_fraction` caps only torch's allocator, so the cap is the budget
 minus the measured non-allocator overhead:
 ```
 footprint  <=  VRAM_CAP_GIB  +  overhead      = 9,472 MiB (9.25 GiB)  +  662 MiB  =  10,134 MiB  (166 MiB under budget)
 ```
 `VRAM_CAP_GIB` in `.env` is **the single knob**. It is applied before the weights load.
 ⚠ **The budget assumed 16,081 MiB free on GPU 1. That is total − used.** nvidia-smi's own `Free` is
 **15,442 MiB**, because the driver reserves 640 MiB on every card (GPU 3 shows the same 640).
 Against the true free memory, a footprint that never collides with scriberr's 5,496 MiB peak must
 stay at or below **9,946 MiB**:
 | cap | footprint peak (measured, GPU 3) | with scriberr at its peak, against 15,442 free | calls that fit (16 questions × 16 options) |
 |---|---|---|---|
 | **9.25 GiB** (`.env.example` now) | 10,134 MiB | **188 MiB over** | every call the API accepts (8,191 tok) |
 | **9.0 GiB** | 9,876 MiB | 70 MiB spare | up to 7,168 tok; longer calls get 503. Every bench shape fits, at the same latency |
 | measured on fv-ml1 GPU 3 (alone on the card), image 0.1.0 | MiB |
 |---|---|
 | **at rest** (nvidia-smi, whole card less 2 MiB idle) | **8,820** (torch reserved 8,160 + outside 660) |
 | outside the allocator (CUDA context + loaded kernels), at rest / after 2×30 concurrent requests / after the latency shapes / after 4 × 8,191-token calls | 660 / 660 / 662 / 662 |
 | **peak, largest request the API accepts, capped at 9.25 GiB** (card, 0.1 s sampling) | **10,134**; torch `max_reserved` = 9.25 GiB exactly, so the cap is what held it |
 | the same request **uncapped**, for comparison | 10,422 (allocator peak 9,760): **over budget, so the cap is required** |
 | startup peak (weights + vision tower until the swap + warm-up) | allocator 8.86 GiB. **A cap below ~8.9 GiB cannot start** (it fails closed; 8.5 was refused) |
 - **The largest request the API accepts** is 64 decisions × 16 options, with each of its 4 calls
  at 8,191 tokens. Under the cap it answers **200** (1.67 s), not 503. Near the limit the
  allocator frees cached blocks before it fails, so the uncapped 9,760 MiB peak was partly cache.
  Every call up to `MAX_TOKENS` fits the cap.
 - **Over the cap the answer is 503 `out_of_memory`.** Memory goes back to the resting baseline
  and the service keeps answering. This was proven with a deliberately tight cap (acceptance).
 - **One inference thread is load-bearing** (contract INV-2). torch keeps CUDA state per host
  thread (cuBLAS handles and workspaces). The first build scored on anyio's 40-thread pool: a
  concurrent burst added 252 MiB outside the cap and 326 MiB inside it, and the peak reached
  10,392 MiB. Load, warm-up and every call now run on one thread.
 - **Released after bursts:** when a call leaves reserved memory more than 512 MiB over the resting
  baseline, the engine calls `empty_cache()`. Measured: back to 7.97 GiB after every large
  request.
 - The rest footprint is 0.9 GB below the bench's 9,736 MiB. The service takes no images, so the
  vision tower (0.62 GiB) is swapped for a stub after the first warm-up, and the warm-up cache is
  released. Startup proves the swap changes nothing: the warm-up answer must be bit-identical
  (INV-7).
 ## Latency
 Loopback on fv-ml1 (GPU 3 alone on the card, image 0.1.0, cap 9.25 GiB). `bench_shape.py`
 from the Jev bench ran 3 runs × 20 requests, and the medians are shown with the run-median
 range. The bench's native Intern-Decision numbers are the reference.
 | shape | end to end | server | bench (native, loopback) | SemIf 0.1.4 (bench) |
 |---|---|---|---|---|
 | 1 decision, short (~180 tok) | 36.4 ms (36.4–36.5) | 34.7 | 39 | 37 |
 | **21 binary criteria**, one `/decide/shared` (2 calls: 16 + 5) | **82.3 ms** (82.3–82.4) | 78.9 | 88 | 131 |
 | 1 decision over the ~3,900-token state | 191.4 ms (191.4–191.5) | 187.9 | 190 | 186 |
 | **16 criteria over the ~3,900-token state** (1 call, 4,579 tok) | **216.8 ms** (216.5–217.0) | 211.1 | 215 | 505 |
 | largest accepted request (4 calls × 8,191 tok) | 1,672 ms (1,672–1,675, N=3) | | | |
 *From nh3-dev and on GPU 1, next to the vLLM seats: pending the GPU 1 deploy (held, see the status banner).*
 ## Acceptance (2026-09-30)
 Raw results: `services/intern-decision-serve/acceptance/gpu3-2026-09-30/` (`compare.json`, one
 directory per process lifetime). **The harness is the bench's own.** `bench_sets.py
 --backend semif` speaks semif-serve's API, so it drove this service unchanged. `compare.py`
 scores it as the bench's `analyze.py` did.
 | check (GPU 3, cap 9.25 GiB, N = 3 fresh processes unless noted) | result | bench / floor |
 |---|---|---|
 | **positive control**, pooled 259, single ordering | **240, 240, 240** | bench 240 (4 repeats); the pooled set resolves ±4 pts |
 | positive control, Wyrd /84, single | **79, 79, 79** | bench 79 |
 | row by row against the bench's native rows (560 rows, single / rotations / negative) | **0 top changes, max Δp 0.000** in every repeat: bit-identical | bench floor: 0 labels moved across 4 restarts |
 | rotations, pooled / Wyrd | 236 / 77 (×3) | bench 236 / 77 |
 | **negative control** (descriptions rotated, 144): same top / follows the description / right vs the original gold | **10 / 122 / 14** (×3) | bench 10 / 122 / 14 |
 | A-vs-A in the process (authored144 twice) | 0/144 flips, Δp 0.0 (×3) | |
 | A-vs-A across restarts (r1~r2, r1~r3, r2~r3; 560 rows, single and rotations) | 0 flips, Δp 0.0 | |
 | Wyrd, one `/decide/shared` per turn (4 decisions in one prompt) | 78/84 (×3); 1/84 row differs from the bench's 77 | expected: field names are positional here, decision names in the bench (contract) |
 | more than 16 questions: 20 decisions in one request, against the same decisions asked as 16 + 4 | 2 calls `[16, 4]`, all 20 tops equal, Δp 0, same prompt hashes | |
 | 401: no token / wrong token, both POSTs; `/health` open | 401 / 401; 200 | |
 | 429: 48 concurrent requests, `MAX_QUEUE` 32 | 32 answered, 16 × 429 `busy` | |
 | largest request the API accepts (64 × 16 options, 4 × 8,191 tok) at cap 9.25 GiB | **200** ×3; card peak 10,134 MiB | budget 10,300 |
 | **over the cap** (the same request at a deliberately tight cap of 8.9 GiB) | **503 `out_of_memory`**; reserved 7.969 GiB before, 7.969 after, next request 200; then all 560 single rows answered, bit-identical to the bench | |
 | fit under the cap: the largest 16-question call that answers 200 | 8,191 tok (the whole API) at 9.25 GiB; 7,168 tok at 9.0 GiB | |
 | cap below startup | 8.5 GiB: startup refused (OOM loading), fails closed | |
 *On the deployed service (GPU 1): pending the deploy (held, see the status banner).*
 ## Building and deploying
 ```bash
 # from nh3-dev. /opt/docker/src is root-owned, so create the version dir with sudo first.
 ssh infra-ops@10.251.50.54 'sudo -n install -d -o infra-ops -g infra-ops /opt/docker/src/intern-decision-serve-X.Y.Z | cat'
 tar -C services/intern-decision-serve -cf - --exclude=.venv --exclude=.pytest_cache --exclude=__pycache__ \
    --exclude=acceptance --exclude='*.egg-info' . \
  | ssh infra-ops@10.251.50.54 'tar -x -C /opt/docker/src/intern-decision-serve-X.Y.Z'
 # on fv-ml1
 cd /opt/docker/src/intern-decision-serve-X.Y.Z && docker build -t intern-decision-serve:X.Y.Z .
 # from nh3-dev: compose.yaml, .env.example and this README (never .env)
 scripts/deploy-stack.sh fv-ml1 intern-decision
 # on fv-ml1: .env is infra-ops's, mode 0600. Rewrite it IN PLACE to keep the owner and mode.
 cd /opt/docker/compose/intern-decision && new=$(sed 's/^IMAGE=.*/IMAGE=intern-decision-serve:X.Y.Z/' .env) \
  && printf '%s\n' "$new" > .env && docker compose config -q && docker compose up -d
 ```
 **Before any deploy onto GPU 1:** GPU 1 must have at least 15,800 MiB free
 (`nvidia-smi -i 1 --query-gpu=memory.free --format=csv`). If it has less, stop. Do not squeeze
 scriberr.
 Startup fails closed. A container that never reaches healthy did not pass its own checks: the
 `inference.py` hash, the pinned snapshot, the warm-up, the text-only swap and the prompt hash.
 Read `docker logs intern-decision`. Record the deploy with `scripts/ops-log`.
 **To move the model forward**, change `REVISION` and `INFERENCE_PY_SHA256` in `config.py`.
 Re-run the bench's sets through the service (`services/intern-decision-serve/acceptance/`), and
 re-measure the VRAM before changing the cap.
 ## Rollback
 `semif` is **stopped, not removed**, and is kept as the rollback. Only on Prime's word:
 ```bash
 cd /opt/docker/compose/intern-decision && docker compose stop     # first: the two do not both fit GPU 1
 cd /opt/docker/compose/semif && docker compose start
 ```
 Then check GPU 1's free memory against scriberr's peak. Callers go back to `:8032` and
 `secret get semif/api-token`.
@@ -0,0 +1,64 @@
 # intern-decision: Intern-Decision-4B (internlm, Apache-2.0) behind intern-decision-serve, on
 # fv-ml1 GPU 1 (the utility card, beside vllm-coder, the erp/meromero seats and scriberr).
 # Replaces semif (Prime, 2026-09-30: "replace semif with intern-decision now").
 #
 # One forward pass per call, scored by the checkpoint's OWN inference.py (sha256-pinned); the
 # service keeps semif-serve's HTTP surface (/decide, /decide/shared, /health). Service code +
 # contract: services/intern-decision-serve/ (intern-decision-serve.contract.md). Image built on
 # fv-ml1 from that dir.
 #
 # ⚠ VRAM_CAP_GIB is a HARD cap on torch's allocator (per-process memory fraction), set so the
 #   container's WHOLE nvidia-smi footprint, CUDA context included, stays <= 10,300 MiB whatever
 #   the request (Prime/infra-ops budget with scriberr, 2026-09-30). A request that needs more
 #   gets 503 out_of_memory and the service stays up. See the README before changing it.
 #
 # .env (tunables): IMAGE, PORT, GPU_ID, VRAM_CAP_GIB, HOST_IP, INTERN_DECISION_API_TOKEN
 #   (vault intern-decision/api-token, >= 32 chars).
 name: intern-decision
 services:
  intern-decision:
    image: ${IMAGE:?set IMAGE}
    container_name: intern-decision
    restart: unless-stopped
    ports:
      - "${PORT:-8033}:8000"
    environment:
      INTERN_DECISION_API_TOKEN: ${INTERN_DECISION_API_TOKEN:?set INTERN_DECISION_API_TOKEN}
      INTERN_DECISION_DEVICE: cuda
      INTERN_DECISION_VRAM_CAP_GIB: ${VRAM_CAP_GIB:?set VRAM_CAP_GIB}
      INTERN_DECISION_MAX_TOKENS: ${MAX_TOKENS:-8192}
      INTERN_DECISION_MAX_DECISIONS: ${MAX_DECISIONS:-64}
      # POSTs in progress (queued + scoring) before new ones get 429 busy.
      INTERN_DECISION_MAX_QUEUE: ${MAX_QUEUE:-32}
    volumes:
      # Pinned weights AND the checkpoint's inference.py, read offline. Never downloads.
      - /tank/aimodels/huggingface:/hf:ro
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ["${GPU_ID:-1}"]
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=5)"]
      interval: 30s
      timeout: 10s
      retries: 3
      # Startup loads ~9 GB of weights and scores the warm-up three times before it serves.
      start_period: 300s
    networks:
      - tnet
    labels:
      - homepage.group=AI - Eval & Retrieval
      - homepage.name=Intern-Decision — typed decisions
      - homepage.icon=mdi-scale-balance
      - homepage.description=Typed decisions from one forward pass (Intern-Decision-4B, fv-ml1 GPU1)
      - homepage.href=http://${HOST_IP:-10.251.50.54}:${PORT:-8033}/health
 networks:
  tnet:
    name: traefik-net
    external: true
		`@@ -0,0 +1 @@`
							{"label": "cap9.0", "backend": "semif", "runs": 1, "per": 5, "max_questions": 10000, "events": [["start", 1790786179.0584767], ["short1:measure", 1790786179.1493392], ["crit21:measure", 1790786179.5161796], ["long1:measure", 1790786186.5989966], ["long16:measure", 1790786194.1815286], ["shapes:done", 1790786195.251973], ["end", 1790786195.251976]], "shapes": {"short1": {"rows": 1, "runs": [{"e2e_ms": [37.941707065328956, 38.23350998573005, 37.37540193833411, 38.53617515414953, 38.11007016338408], "server_ms": [35.29082587920129, 35.39266809821129, 34.68621987849474, 35.76885210350156, 35.205855034291744], "statuses": [200, 200, 200, 200, 200], "tokens": 181, "median_e2e": 38.11007016338408, "median_server": 35.29082587920129}], "tokens": 181, "median_e2e_ms": 38.1, "run_medians_e2e_ms": [38.1], "median_server_ms": 35.3, "non_200": 0}, "crit21": {"rows": 21, "runs": [{"e2e_ms": [86.08735189773142, 85.76549892313778, 85.83429013378918, 85.84657986648381, 85.68961801938713], "server_ms": [79.76265903562307, 79.48806718923151, 79.50853602960706, 79.57211695611477, 79.62968898937106], "statuses": [200, 200, 200, 200, 200], "tokens": null, "median_e2e": 85.83429013378918, "median_server": 79.57211695611477}], "tokens": null, "median_e2e_ms": 85.8, "run_medians_e2e_ms": [85.8], "median_server_ms": 79.6, "non_200": 0}, "long1": {"rows": 1, "runs": [{"e2e_ms": [184.56048821099102, 191.5509181562811, 187.36126995645463, 186.49141117930412, 187.67126300372183], "server_ms": [181.11044797115028, 187.99379700794816, 183.76171891577542, 183.00863006152213, 184.1394929215312], "statuses": [200, 200, 200, 200, 200], "tokens": 4019, "median_e2e": 187.36126995645463, "median_server": 183.76171891577542}], "tokens": 4019, "median_e2e_ms": 187.4, "run_medians_e2e_ms": [187.4], "median_server_ms": 183.8, "non_200": 0}, "long16": {"rows": 16, "runs": [{"e2e_ms": [211.36997686699033, 217.9243720602244, 213.36712990887463, 213.6664930731058, 211.49204880930483], "server_ms": [205.53427003324032, 212.03652513213456, 207.65956514514983, 207.81763619743288, 205.71005111560225], "statuses": [200, 200, 200, 200, 200], "tokens": null, "median_e2e": 213.36712990887463, "median_server": 207.65956514514983}], "tokens": null, "median_e2e_ms": 213.4, "run_medians_e2e_ms": [213.4], "median_server_ms": 207.7, "non_200": 0}}}
		`@@ -0,0 +1 @@`
							{"label": "probe", "backend": "semif", "runs": 1, "per": 5, "max_questions": 10000, "events": [["start", 1790785217.097767], ["short1:measure", 1790785217.1804233], ["crit21:measure", 1790785217.5361114], ["long1:measure", 1790785224.795359], ["long16:measure", 1790785232.4314742], ["shapes:done", 1790785233.5109885], ["end", 1790785233.5109909]], "shapes": {"short1": {"rows": 1, "runs": [{"e2e_ms": [36.293586948886514, 36.00253281183541, 37.3701888602227, 36.00338310934603, 35.94383201561868], "server_ms": [34.18141300790012, 34.299023915082216, 34.113402012735605, 34.34582380577922, 34.3846851028502], "statuses": [200, 200, 200, 200, 200], "tokens": 181, "median_e2e": 36.00338310934603, "median_server": 34.299023915082216}], "tokens": 181, "median_e2e_ms": 36.0, "run_medians_e2e_ms": [36.0], "median_server_ms": 34.3, "non_200": 0}, "crit21": {"rows": 21, "runs": [{"e2e_ms": [85.47385083511472, 85.30352916568518, 85.49744100309908, 85.56356094777584, 85.31283889897168], "server_ms": [79.13649803958833, 78.94592499360442, 79.16115806438029, 79.16332804597914, 78.98750598542392], "statuses": [200, 200, 200, 200, 200], "tokens": null, "median_e2e": 85.47385083511472, "median_server": 79.13649803958833}], "tokens": null, "median_e2e_ms": 85.5, "run_medians_e2e_ms": [85.5], "median_server_ms": 79.1, "non_200": 0}, "long1": {"rows": 1, "runs": [{"e2e_ms": [193.0149649269879, 186.22795795090497, 187.29469901882112, 211.8130698800087, 182.15654999949038], "server_ms": [189.49031387455761, 182.79000790789723, 183.78369905985892, 208.4096318576485, 178.73777099885046], "statuses": [200, 200, 200, 200, 200], "tokens": 4019, "median_e2e": 187.29469901882112, "median_server": 183.78369905985892}], "tokens": 4019, "median_e2e_ms": 187.3, "run_medians_e2e_ms": [187.3], "median_server_ms": 183.8, "non_200": 0}, "long16": {"rows": 16, "runs": [{"e2e_ms": [212.7648708410561, 229.93756900541484, 210.01248992979527, 212.9045429173857, 211.45822713151574], "server_ms": [207.08801690489054, 224.23489298671484, 204.2714338749647, 207.02347601763904, 205.7694320101291], "statuses": [200, 200, 200, 200, 200], "tokens": null, "median_e2e": 212.7648708410561, "median_server": 207.02347601763904}], "tokens": null, "median_e2e_ms": 212.8, "run_medians_e2e_ms": [212.8], "median_server_ms": 207.0, "non_200": 0}}}
		`@@ -0,0 +1 @@`
							`1d98f12c909d19f7e157823e29412e4fb44aa22db94c6ee4a96d8453aed29c70`
		`@@ -0,0 +1 @@`
							{"status":"ok","model":{"name":"Intern-Decision-4B","source":"internlm/Intern-Decision-4B","revision":"0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","checkpoint":"/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","inference_py_sha256":"c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863","temperature":1.99241824,"dtype":"bfloat16","attn_implementation":"sdpa","device":"cuda","max_length":8192,"torch_version":"2.10.0+cu128","transformers_version":"5.17.0","vision_tower":"removed","device_name":"NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition","allocated_gib":7.937,"reserved_gib":7.969,"max_reserved_gib":8.861},"vram_cap_gib":9.25,"max_tokens":8192,"max_decisions":64,"max_questions_per_call":16,"chunking":"/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.","workloads":[]}
		`@@ -0,0 +1 @@`
							`35bbfe41a4b1b3227c24caa36199780398e09966ceb273fbe9402fb1631d6782`
		`@@ -0,0 +1 @@`
							`5068d9162b114ffd3b9773ce3a001634a40605e8e9777778e586bc28475e2c9f`
		`@@ -0,0 +1 @@`
							`297e8eb1cac5b35462bd8ab9c918791fb84c064372f3280596b1911dfb5c5f92`
		`@@ -0,0 +1 @@`
							`d10e0ec2b1f031f57d6594c10548696bd8352c75b35458cf44538ead4fc2eb0a`
		`@@ -0,0 +1 @@`
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