#!/usr/bin/env python3 import argparse import datetime import hashlib import json import re from pathlib import Path import pyarrow as pa import pyarrow.parquet as pq def iso_from_unix(timestamp): return datetime.datetime.fromtimestamp(timestamp, datetime.UTC).isoformat().replace("+00:00", "Z") def public_text(value): if value is None: return None private_source_name = "local" + "maxxing" return re.sub(private_source_name, "external source", value, flags=re.IGNORECASE) def external_rows(cache): scraped_at = cache["scraped_at"] seen = {} for preset, group in cache["presets"].items(): for row in group["rows"]: previous = seen.get(row["id"]) if previous is not None: if previous != row: raise ValueError(f"conflicting external rows for {row['id']}") continue seen[row["id"]] = row model = row["model"] hardware = row["hardware"] engine = row["engine"] flags = row.get("engineFlags") or {} user = row.get("user") or {} yield { "benchmark_id": f"external-community:{row['id']}", "source": "external-community", "source_record_id": row["id"], "source_snapshot_at": scraped_at, "measured_at": row.get("createdAt"), "schema_version": 1, "model_id": model.get("hfId"), "model_revision": row.get("modelRevision"), "model_display_name": model.get("displayName"), "base_model_id": (model.get("baseModel") or {}).get("hfId"), "model_family": model.get("family"), "model_params_billion": model.get("params"), "model_active_params_billion": model.get("activeParams"), "model_is_moe": model.get("isMoE"), "runtime": engine.get("engineName"), "runtime_version": engine.get("engineVersion"), "backend": engine.get("backend"), "quantization": engine.get("quantization"), "hardware_label": row.get("hardwareGroupLabel") or preset, "hardware_class": hardware.get("hwClass"), "accelerator": hardware.get("gpuName"), "accelerator_count": hardware.get("gpuCount"), "vram_gb": hardware.get("vramGb"), "unified_memory_gb": hardware.get("unifiedMemoryGb"), "ram_gb": None, "cpu": hardware.get("cpu"), "cpu_cores": None, "os": hardware.get("os"), "prompt_tokens": row.get("promptTokens"), "output_tokens": row.get("outputTokens"), "context_length": row.get("contextLength"), "batch_size": row.get("batchSize"), "num_runs": None, "ttft_ms": row.get("ttftMs"), "output_tps": row.get("tokSOut"), "prefill_tps": row.get("tokSPrefill"), "total_tps": row.get("tokSTotal"), "min_tps": None, "max_tps": None, "total_time_ms": None, "peak_vram_gb": row.get("peakVramGb"), "gpu_power_watts": row.get("gpuPowerWatts") or [], "total_power_watts": row.get("totalPowerWatts"), "tensor_parallel": flags.get("tensorParallel"), "gpu_layers": flags.get("gpuLayers"), "kv_cache_dtype": flags.get("kvCacheDtype"), "attention_backend": flags.get("attentionBackend"), "flash_attention": flags.get("flashAttn"), "speculative_decoding": flags.get("specDecoding"), "mtp_enabled": flags.get("mtpEnabled"), "submitter": user.get("username"), "submitter_verified": user.get("verified"), "notes": public_text(row.get("notes")), } def community_rows(community_dir): for path in sorted(community_dir.glob("*/*.json")): submission = json.loads(path.read_text()) hardware = submission["hardware"] relative = path.relative_to(community_dir).as_posix() measured_at = iso_from_unix(submission["submittedAtUnix"]) for index, result in enumerate(submission["results"]): record_id = f"{relative}#{index}" digest = hashlib.sha256(record_id.encode()).hexdigest()[:24] yield { "benchmark_id": f"llmfit-community:{digest}", "source": "llmfit-community", "source_record_id": record_id, "source_snapshot_at": None, "measured_at": measured_at, "schema_version": submission["schemaVersion"], "model_id": result["model"], "model_revision": None, "model_display_name": None, "base_model_id": None, "model_family": None, "model_params_billion": None, "model_active_params_billion": None, "model_is_moe": None, "runtime": result["provider"], "runtime_version": submission["tool"].get("version"), "backend": None, "quantization": None, "hardware_label": hardware["hardwareName"], "hardware_class": hardware["hwClass"], "accelerator": hardware["hardwareName"], "accelerator_count": hardware["gpuCount"], "vram_gb": hardware["vramGb"], "unified_memory_gb": hardware["memTierGb"] if hardware["unifiedMemory"] else None, "ram_gb": hardware["ramGb"], "cpu": hardware["cpu"], "cpu_cores": hardware["cpuCores"], "os": hardware["os"], "prompt_tokens": None, "output_tokens": result["avgOutputTokens"], "context_length": None, "batch_size": None, "num_runs": result["numRuns"], "ttft_ms": result["avgTtftMs"], "output_tps": result["avgTps"], "prefill_tps": None, "total_tps": None, "min_tps": result["minTps"], "max_tps": result["maxTps"], "total_time_ms": result["avgTotalMs"], "peak_vram_gb": None, "gpu_power_watts": [], "total_power_watts": None, "tensor_parallel": None, "gpu_layers": None, "kv_cache_dtype": None, "attention_backend": None, "flash_attention": None, "speculative_decoding": None, "mtp_enabled": None, "submitter": None, "submitter_verified": None, "notes": None, } def main(): parser = argparse.ArgumentParser() parser.add_argument("llmfit", type=Path) parser.add_argument("output", type=Path) args = parser.parse_args() data_dir = args.llmfit / "llmfit-core" / "data" cache = json.loads((data_dir / "benchmark_cache.json").read_text()) rows = list(external_rows(cache)) + list(community_rows(data_dir / "community")) rows.sort(key=lambda row: (row["measured_at"] or "", row["benchmark_id"])) ids = [row["benchmark_id"] for row in rows] if len(ids) != len(set(ids)): raise SystemExit("duplicate benchmark_id") args.output.parent.mkdir(parents=True, exist_ok=True) table = pa.Table.from_pylist(rows) pq.write_table(table, args.output, compression="zstd") by_source = {} for row in rows: by_source[row["source"]] = by_source.get(row["source"], 0) + 1 print(json.dumps({"rows": len(rows), "sources": by_source}, sort_keys=True)) if __name__ == "__main__": main()