#!/usr/bin/env python3 """Build the static Smoke24 dashboard from public aggregate data.""" from __future__ import annotations import argparse import csv import json import shutil from dataclasses import dataclass from pathlib import Path from typing import Any ROOT = Path(__file__).resolve().parents[1] DEFAULT_DERIVED_DIR = ROOT / "data/public/derived" DEFAULT_REPORTS_DIR = ROOT / "reports/public" DEFAULT_OUTPUT_DIR = ROOT / "site" TEMPLATE_PATH = ROOT / "dashboard/templates/smoke24_dashboard.html" REPORT_DATE = "2026-06-29" CORPUS_ID = "tb20-coder-smoke24-fast-success-failure-20260616" REFERENCE_MODEL_ID = "residency/vllm/qwen3.6-27b-gptq-pro-4bit" LOCAL_3090_MODEL_IDS = { REFERENCE_MODEL_ID, "residency/vllm/qwopus3.6-27b-v2-gptq-pro-foem-4bit-g128-ns256-v2", "residency/vllm/qwopus3.6-35b-a3b-v1-gptq-pro-foem-4bit-g128-ns256", "residency/vllm/nex-n2-mini-gptq-pro", "residency/vllm/qwopus3.6-27b-coder-gptq-pro-foem-4bit-g128-ns256", "residency/vllm/qwopus3.6-35b-a3b-v1-gptq-pro-foem-4bit-g128-ns256-ctx262k", "residency/vllm/nex-n2-mini-gptq-pro-ctx262k", "residency/vllm/ornith-1.0-35b-gptq-pro-foem-4bit-g128-ns256-ctx262k", "residency/vllm/qwythos-9b-claude-mythos-5-1m-awq-ctx512k", } MODEL_URLS = { "residency/vllm/qwopus3.6-27b-v2-gptq-pro-foem-4bit-g128-ns256-v2": "https://huggingface.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", "residency/vllm/qwopus3.6-35b-a3b-v1-gptq-pro-foem-4bit-g128-ns256": "https://huggingface.co/XReyRobert/Qwopus3.6-35B-A3B-v1-GPTQ-Pro", "residency/vllm/qwopus3.6-35b-a3b-v1-gptq-pro-foem-4bit-g128-ns256-ctx262k": "https://huggingface.co/XReyRobert/Qwopus3.6-35B-A3B-v1-GPTQ-Pro", "residency/vllm/nex-n2-mini-gptq-pro": "https://huggingface.co/XReyRobert/Nex-N2-mini-GPTQ-Pro", "residency/vllm/nex-n2-mini-gptq-pro-ctx262k": "https://huggingface.co/XReyRobert/Nex-N2-mini-GPTQ-Pro", "residency/vllm/qwopus3.6-27b-coder-gptq-pro-foem-4bit-g128-ns256": "https://huggingface.co/XReyRobert/Qwopus3.6-27B-Coder-GPTQ-Pro", "residency/vllm/ornith-1.0-35b-gptq-pro-foem-4bit-g128-ns256-ctx262k": "https://huggingface.co/XReyRobert/Ornith-1.0-35B-GPTQ-Pro-FOEM-4bit-g128-ns256", "residency/vllm/qwythos-9b-claude-mythos-5-1m-awq-ctx512k": "https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M", "residency/vllm/qwen3.6-27b-gptq-pro-4bit": "https://huggingface.co/groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit", } @dataclass(frozen=True) class AssetCopy: source_name: str target_name: str title: str IMAGE_ASSETS = [ AssetCopy("model-lineage.png", "model-lineage.png", "Model lineage"), AssetCopy("uncertainty-landscape.png", "uncertainty-landscape.png", "Uncertainty landscape"), AssetCopy("score-token-efficiency.png", "score-token-efficiency.png", "Score vs generated tokens per success"), AssetCopy("task-stability.png", "task-stability.png", "Task stability heatmap"), AssetCopy("metric-variance.png", "metric-variance.png", "Metric variance bands"), AssetCopy("external-public-success.png", "external-public-success.png", "External public success comparison"), ] def read_csv(path: Path) -> list[dict[str, str]]: with path.open(newline="", encoding="utf-8") as handle: return list(csv.DictReader(handle)) def to_float(value: Any) -> float | None: if value in (None, ""): return None try: return float(value) except (TypeError, ValueError): return None def to_int(value: Any) -> int: number = to_float(value) return int(number) if number is not None else 0 def short_label(model_id: str) -> str: mapping = { REFERENCE_MODEL_ID: "Qwen3.6 27B GPTQ", "residency/vllm/qwopus3.6-27b-v2-gptq-pro-foem-4bit-g128-ns256-v2": "Qwopus 27B v2", "residency/vllm/qwopus3.6-35b-a3b-v1-gptq-pro-foem-4bit-g128-ns256": "Qwopus 35B A3B", "residency/vllm/nex-n2-mini-gptq-pro": "Nex-N2 mini", "residency/vllm/qwopus3.6-27b-coder-gptq-pro-foem-4bit-g128-ns256": "Qwopus 27B coder", "residency/vllm/qwopus3.6-35b-a3b-v1-gptq-pro-foem-4bit-g128-ns256-ctx262k": "Qwopus 35B A3B 262K", "residency/vllm/nex-n2-mini-gptq-pro-ctx262k": "Nex-N2 mini 262K", "residency/vllm/ornith-1.0-35b-gptq-pro-foem-4bit-g128-ns256-ctx262k": "Ornith 35B GPTQ-Pro", "residency/vllm/qwythos-9b-claude-mythos-5-1m-awq-ctx512k": "Qwythos 9B AWQ", "external/tbench/terminus-2/claude-sonnet-4.5": "Claude Sonnet 4.5", "external/tbench/terminus-2/claude-opus-4.6": "Claude Opus 4.6", "external/tbench/terminus-2/gpt-5": "GPT-5", "external/tbench/terminus-2/gpt-5.3-codex": "GPT-5.3-Codex", } return mapping.get(model_id, model_id.removeprefix("residency/vllm/")) def family(model_id: str) -> str: stripped = model_id.removeprefix("residency/vllm/").removeprefix("external/tbench/terminus-2/") for suffix in ("-ctx512k", "-ctx262k", "-ctx96k"): if stripped.endswith(suffix): stripped = stripped[: -len(suffix)] if stripped.startswith("qwopus3.6-27b-v2"): return "qwopus27" if stripped.startswith("qwopus3.6-27b-coder"): return "coder" if stripped.startswith("qwopus3.6-35b"): return "qwopus35" if stripped.startswith("nex-n2-mini"): return "nex" if stripped.startswith("ornith"): return "ornith" if stripped.startswith("qwythos"): return "qwythos" if stripped.startswith("qwen3.6"): return "qwen" if "claude" in stripped: return "claude" if "gpt" in stripped: return "openai" return "other" def scope(model_id: str) -> str: if model_id.startswith("external/"): return "external-public" if model_id == REFERENCE_MODEL_ID: return "reference" if model_id.endswith("-ctx512k"): return "experimental" if model_id.endswith("-ctx262k"): return "long-context" return "local-131k" def model_rows(derived_dir: Path, reports_dir: Path) -> list[dict[str, Any]]: combined = read_csv(derived_dir / "model_summary.csv") speed_by_label = {row["label"]: row for row in read_csv(reports_dir / "speed_normalized_metrics.csv")} efficiency_by_label = {row["label"]: row for row in read_csv(reports_dir / "local_efficiency_metrics.csv")} hardware_by_label = {row["label"]: row for row in read_csv(reports_dir / "hardware_neutral_metrics.csv")} out: list[dict[str, Any]] = [] seen: set[str] = set() for row in combined: model_id = row["model_id"] if model_id not in LOCAL_3090_MODEL_IDS and not model_id.startswith("external/"): continue if model_id in seen: continue seen.add(model_id) speed = speed_by_label.get(model_id, {}) efficiency = efficiency_by_label.get(model_id, {}) hardware = hardware_by_label.get(model_id, {}) trials = to_int(row.get("trials")) score = to_float(row.get("score")) success_rate = to_float(row.get("success_rate")) equivalent_score = score / (trials / 24.0) if score is not None and trials else None output_tokens = to_float(row.get("output_tokens")) output_per_success = to_float(hardware.get("output_tokens_per_success")) if output_per_success is None and output_tokens is not None and score: output_per_success = output_tokens / score out.append( { "model_id": model_id, "label": short_label(model_id), "display_order": to_int(row.get("display_order")), "family": family(model_id), "scope": scope(model_id), "url": MODEL_URLS.get(model_id, row.get("detail_url") or ""), "trials": trials, "score": score, "success_rate": success_rate, "equivalent_score": equivalent_score, "wall_min": to_float(row.get("wall_min")), "input_tokens": to_float(row.get("input_tokens")), "output_tokens": output_tokens, "output_tokens_per_success": output_per_success, "parser_warning_count": to_int(row.get("parser_warning_count")), "parser_warning_trials": to_int(row.get("parser_warning_trials")), "llm_api_min": to_float(speed.get("llm_api_min") or row.get("llm_api_min")), "observed_tps": to_float(speed.get("observed_tps") or row.get("observed_tps")), "llm_share_wall": to_float(speed.get("llm_share_wall") or row.get("llm_share_wall")), "reasoning_share": to_float(speed.get("reasoning_share") or row.get("reasoning_share")), "llm_min_per_success": to_float(efficiency.get("llm_min_per_success")), "wall_min_per_success": to_float(efficiency.get("wall_min_per_success")), "baseline_equiv_tokens_per_success": to_float(efficiency.get("baseline_equiv_tokens_per_success")), "successes_per_llm_hour": to_float(efficiency.get("successes_per_llm_hour")), "llm_efficiency_index": to_float(efficiency.get("llm_efficiency_index")), "global_tb20_accuracy": to_float(row.get("global_tb20_accuracy")), "global_tb20_stderr": to_float(row.get("global_tb20_stderr")), "token_source": row.get("token_source") or "", "run_ids": row.get("run_ids") or "", "shape_id": row.get("shape_id") or "", } ) return sorted(out, key=lambda item: item["display_order"]) def task_rows(derived_dir: Path) -> list[dict[str, Any]]: rows = read_csv(derived_dir / "task_outcomes.csv") out = [] for row in rows: model_id = row["model_id"] if model_id not in LOCAL_3090_MODEL_IDS and not model_id.startswith("external/"): continue out.append( { "model_id": model_id, "label": short_label(model_id), "scope": scope(model_id), "task": row["task"], "task_order": to_int(row.get("task_order")), "attempts": to_int(row.get("attempts")), "successes": to_int(row.get("successes")), "success_rate": to_float(row.get("success_rate")), "status": row.get("status") or "", "wall_min": to_float(row.get("mean_wall_min")), "input_tokens": to_float(row.get("input_tokens")), "output_tokens": to_float(row.get("output_tokens")), "parser_warning_count": to_int(row.get("parser_warning_count")), } ) return sorted(out, key=lambda item: (item["task_order"], item["label"])) def variance_rows(derived_dir: Path) -> list[dict[str, Any]]: rows = read_csv(derived_dir / "model_variance.csv") out = [] for row in rows: model_id = row["canonical_model_id"] out.append( { "canonical_model_id": model_id, "label": short_label("residency/vllm/" + model_id), "context": row.get("context"), "passes": to_int(row.get("smoke24_passes")), "score_values": row.get("score_values"), "score_mean": to_float(row.get("score_mean")), "score_min": to_float(row.get("score_min")), "score_max": to_float(row.get("score_max")), "score_sample_std": to_float(row.get("score_sample_std")), "output_tokens_min": to_float(row.get("output_tokens_min")), "output_tokens_max": to_float(row.get("output_tokens_max")), "llm_api_min_min": to_float(row.get("llm_api_min_min")), "llm_api_min_max": to_float(row.get("llm_api_min_max")), "parser_warning_count_min": to_float(row.get("parser_warning_count_min")), "parser_warning_count_max": to_float(row.get("parser_warning_count_max")), } ) return out def image_assets(reports_dir: Path, output_dir: Path) -> list[dict[str, str]]: target_dir = output_dir / "assets/img" target_dir.mkdir(parents=True, exist_ok=True) copied = [] for asset in IMAGE_ASSETS: source = reports_dir / "images" / asset.source_name if not source.exists(): continue target = target_dir / asset.target_name shutil.copy2(source, target) copied.append({"path": f"assets/img/{asset.target_name}", "title": asset.title}) return copied def build_data(derived_dir: Path, reports_dir: Path, output_dir: Path) -> dict[str, Any]: models = model_rows(derived_dir, reports_dir) tasks = task_rows(derived_dir) local_models = [row for row in models if row["scope"] in {"reference", "local-131k", "long-context"}] best_local = max(local_models, key=lambda row: (row["success_rate"] or 0, -(row["output_tokens_per_success"] or 10**12))) best_eff = min( [row for row in local_models if row.get("llm_min_per_success") is not None], key=lambda row: row["llm_min_per_success"], ) task_count = len({row["task"] for row in tasks}) return { "meta": { "title": "GPTQ-Pro Smoke24 Agentic 3090 Dashboard", "report_date": REPORT_DATE, "corpus_id": CORPUS_ID, "task_count": task_count, "hardware_context": "Local vLLM / LiteLLM residency runs in the RTX 3090 deployment context", "harness": "Terminal-Bench 2.0 Smoke24, Terminus-2, 30m task timeout, 32 CPU / 48 GiB sandbox", "reference_model_id": REFERENCE_MODEL_ID, "reference_label": short_label(REFERENCE_MODEL_ID), "best_local_label": best_local["label"], "best_local_score": best_local["score"], "best_local_success_rate": best_local["success_rate"], "best_efficiency_label": best_eff["label"], "best_efficiency_llm_min_per_success": best_eff["llm_min_per_success"], }, "models": models, "tasks": tasks, "variance": variance_rows(derived_dir), "assets": image_assets(reports_dir, output_dir), "caveats": [ "Default view focuses on local RTX 3090-class serving context and real agentic workload behavior.", "External public Terminal-Bench rows are success-rate references only; timing and throughput are not hardware-comparable.", "Long-context 262K rows are serving validation on the same Smoke24 corpus, not separate 131K leaderboard entries.", "Qwythos AWQ is marked experimental. Ornith NVFP4 probe has been removed from active report views.", "Smoke24 is intentionally small; variance views should be used when reading one-task deltas.", ], } def write_json(output_dir: Path, data: dict[str, Any]) -> None: data_dir = output_dir / "assets/data" data_dir.mkdir(parents=True, exist_ok=True) (data_dir / "report-data.json").write_text(json.dumps(data, indent=2, sort_keys=True) + "\n", encoding="utf-8") def write_readme(output_dir: Path) -> None: readme = """# GPTQ-Pro Smoke24 Agentic 3090 Dashboard Generated static dashboard. Rebuild from repository root with `make build`. """ (output_dir / "README.md").write_text(readme, encoding="utf-8") def write_index(output_dir: Path, data: dict[str, Any]) -> None: template = TEMPLATE_PATH.read_text(encoding="utf-8") embedded = json.dumps(data, separators=(",", ":")).replace(" None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--derived-dir", type=Path, default=DEFAULT_DERIVED_DIR) parser.add_argument("--reports-dir", type=Path, default=DEFAULT_REPORTS_DIR) parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR) args = parser.parse_args() if args.output_dir.exists(): shutil.rmtree(args.output_dir) args.output_dir.mkdir(parents=True) data = build_data(args.derived_dir, args.reports_dir, args.output_dir) write_json(args.output_dir, data) write_readme(args.output_dir) write_index(args.output_dir, data) print(f"site_dir={args.output_dir} models={len(data['models'])} tasks={data['meta']['task_count']}") if __name__ == "__main__": main()