swift15: env -> candidate payload, RAD floor 110000 (measured 116,416 MiB)
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#!/usr/bin/env python3
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"""deep-probe.py — one chat completion at ~43.6k prompt tokens, temp 0, 512
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generated, streamed; decode = completion_tokens / (total - ttft)."""
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import json, time, urllib.request
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TARGET_BYTES = int(43600 * 3.8)
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def build():
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parts, n = [], 0
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with open("/home/dank/corpora/out/calib.jsonl") as f:
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for line in f:
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p = json.loads(line)["prompt"]
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parts.append(p)
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n += len(p.encode())
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if n >= TARGET_BYTES:
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break
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return "".join(parts)
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def main(salt):
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body = json.dumps({
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"model": "frank-primary",
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"messages": [{"role": "user", "content": f"[probe {salt}] Continue and summarize this material in English, 400 words.\n\n" + build()}],
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"temperature": 0.0, "max_tokens": 512,
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"stream": True, "stream_options": {"include_usage": True},
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}).encode()
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req = urllib.request.Request("http://localhost:7770/v1/chat/completions",
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data=body, headers={"Content-Type": "application/json"})
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t0 = time.time(); ttft = None; ctok = 0; usage = {}
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with urllib.request.urlopen(req, timeout=2400) as r:
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for raw in r:
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line = raw.decode().strip()
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if not line.startswith("data:"):
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continue
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d = line[5:].strip()
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if d == "[DONE]":
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break
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j = json.loads(d)
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if j.get("usage"):
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usage = j["usage"]
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for ch in j.get("choices", []):
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delta = (ch.get("delta") or {})
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piece = delta.get("content") or delta.get("reasoning_content") or ""
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if piece and ttft is None:
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ttft = time.time() - t0
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ctok += 1
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total = time.time() - t0
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gen = usage.get("completion_tokens") or ctok
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dec = gen / (total - (ttft or 0)) if total > (ttft or 0) else 0
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print(json.dumps({"salt": salt, "prompt_tokens": usage.get("prompt_tokens"),
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"completion_tokens": gen, "ttft_s": round(ttft or 0, 2),
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"total_s": round(total, 1), "decode_tps": round(dec, 2)}))
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if __name__ == "__main__":
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import sys
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main(sys.argv[1] if len(sys.argv) > 1 else "x")
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