43e67d872f
Run models locally as a first-class provider. The CLI grows a managed llama.cpp runtime (engine install, model download, server supervision); the desktop app grows the full setup and management story on top of it. GUI surfaces ship behind the desktop --local launch flag (hermes desktop --local, or the flag on the packaged app); backend routes and the CLI are always live. Runtime (hermes_cli/local_runtime/): - curated GGUF catalog with per-machine variant selection: hardware probe (VRAM/RAM/UMA), fit planning with spill accounting, quant choice by context window - derived recommendation: quality-ranked picks gated by a predicted decode-speed floor, bandwidth-aware on unified memory; the decision table is pinned as a test (pick AND reason per memory class), and the Recommended badge explains its pick in a tooltip fed by the resolver's actual branch - engine install + model download with resumable split parts, cumulative plan-level progress, and staged-model integrity (a split GGUF counts only when every part is present) - server supervision: spawn/adopt/stop, router mode with per-model load progress relayed over SSE, abandoned-request cleanup Desktop: - Settings -> Providers -> Local models: one-click quickstart (install engine, download the recommended model, boot) plus per-model download/ activate/eject, fit-ranked catalog with context pills - model pickers (composer dropdown + Cmd+K) show staged local models, in-flight downloads as live progress rows, and load-into-memory bars - local-setup campaign tip for eligible hardware; System resources statusbar widget (GPU/VRAM/RAM); in-chat load progress during sends - friendly dead-server errors, and failed agent builds retry on the next send instead of wedging the session Co-developed with NVIDIA field feedback on RTX 5090 and DGX Spark.
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
"""Propose catalog quality updates from Artificial Analysis.
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Authoring-time helper — NEVER called at runtime (their terms forbid
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client-side keys, the fleet would burn the rate limit, and a
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recommendation must not change because a third-party endpoint
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hiccuped). Run it when adding a model or refreshing the ordering;
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review the printed diff and edit catalog.json yourself. The script
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proposes, the commit decides.
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The catalog's `quality` stays OUR field: AA-informed where they cover a
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model, editorially set where they don't (day-0 releases lag their evals;
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some entries never appear). AA's Intelligence Index grades the
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full-precision cloud model, not our Q4 build — fine for ordering, never
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for display.
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Usage:
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export AA_API_KEY=... # from https://artificialanalysis.ai (free tier)
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python scripts/aa_quality_sync.py
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Attribution: scores by Artificial Analysis (https://artificialanalysis.ai).
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"""
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from __future__ import annotations
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import json
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import os
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import sys
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import urllib.request
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parent.parent
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CATALOG_PATH = REPO_ROOT / "hermes_cli" / "local_runtime" / "catalog.json"
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AA_URL = "https://artificialanalysis.ai/api/v2/data/llms/models"
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# Catalog entry id -> AA slug. Hand-maintained: AA's naming rarely matches
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# HF repo names, and a wrong match silently mis-ranks a model. An entry
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# absent here (or mapped to None) is editorial-only and never overwritten.
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AA_SLUG_BY_ENTRY = {
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"qwen3.8-27b": "qwen3-8-27b",
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"qwen3.8-flash-next": "qwen3-8-flash-next",
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"qwen3.6-35b-a3b": "qwen3-6-35b-a3b",
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"deepseek-v4-flash": "deepseek-v4-flash",
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}
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def fetch_aa_models(api_key: str) -> dict[str, dict]:
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req = urllib.request.Request(AA_URL, headers={"x-api-key": api_key})
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with urllib.request.urlopen(req, timeout=30) as r:
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doc = json.load(r)
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return {m["slug"]: m for m in doc.get("data", [])}
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def main() -> int:
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api_key = os.environ.get("AA_API_KEY", "").strip()
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if not api_key:
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print("AA_API_KEY not set — create a free key at "
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"https://artificialanalysis.ai and export it.", file=sys.stderr)
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return 2
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catalog = json.loads(CATALOG_PATH.read_text(encoding="utf-8"))
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aa = fetch_aa_models(api_key)
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print(f"{'entry':24s} {'catalog q':>9s} {'AA index':>9s} note")
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print("-" * 70)
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for model in catalog["models"]:
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entry_id = model["id"]
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current = model.get("quality", 0)
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slug = AA_SLUG_BY_ENTRY.get(entry_id)
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if not slug:
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print(f"{entry_id:24s} {current:>9d} {'—':>9s} editorial only (no AA mapping)")
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continue
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hit = aa.get(slug)
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if hit is None:
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print(f"{entry_id:24s} {current:>9d} {'—':>9s} not in AA data (slug {slug!r})")
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continue
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index = (hit.get("evaluations") or {}).get(
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"artificial_analysis_intelligence_index")
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if index is None:
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print(f"{entry_id:24s} {current:>9d} {'—':>9s} AA row lacks the index")
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continue
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proposed = round(float(index))
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marker = "" if proposed == current else " <-- proposes change"
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print(f"{entry_id:24s} {current:>9d} {proposed:>9d}{marker}")
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print("\nReview against the decision table before editing: a quality "
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"change that flips cells in tests/hermes_cli/"
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"test_local_recommendation.py is the actual decision being made.")
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print("Attribution: scores by Artificial Analysis "
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"(https://artificialanalysis.ai).")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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