Fix: Rollen-Scorer prinzipien-konform (fast!=groesstes, Hirn=Hermes/moderat)
Erster Wurf empfahl fast->122B und hermes->122B: ein globaler Wissen=Groesse-Term ueberstimmte die Rollen-Absicht. Jetzt getrennt: - _capability_suit: harte Gates (Vision braucht Vision; Coder-Modell Pflicht; Hirn bevorzugt Hermes-Familie/Tools; dedizierte VL > Omni). - _pref: rollengerechte Groessen-/Tempo-Praeferenz (fast=Tempo&klein, heavy=gross, hermes=7-24B, vision=klein, scout=moderat). - _catalog_role_match: Cookbook-Eintrag fuer die Rolle = starker Anker-Bonus. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Hitonabi
+56
-22
@@ -9,7 +9,7 @@ und fit.evaluate_fit — dieselbe Mathematik wie Install-Automatik und Auto-ctx-
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import psutil
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from services import budget, llamaswap
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from services import budget, catalog, llamaswap
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from services.fit import evaluate_fit
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@@ -17,53 +17,86 @@ def _ram_gb() -> float:
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return psutil.virtual_memory().total / (1024 ** 3)
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def _suitability(role: str, caps: dict, params: float, name: str) -> float:
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"""0..1 — wie gut passt die Capability eines Modells zur Rolle. Harte Anforderungen
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(Vision braucht Vision) geben 0 bei Nichterfüllung; weiche Präferenzen skalieren."""
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def _capability_suit(role: str, caps: dict, name: str) -> float:
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"""0..1 — Capability-Eignung (HARTE Gates). 0 = grundsätzlich falsch für die Rolle.
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Größe/Tempo bewertet getrennt _pref(), damit z.B. 'fast' nicht das größte Modell zieht."""
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role = (role or "").lower()
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low = (name or "").lower()
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vision = bool(caps.get("vision"))
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coder = bool(caps.get("coder"))
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tools = caps.get("tools") != "no"
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moe = bool(caps.get("moe"))
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if role == "vision":
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return 1.0 if vision else 0.0 # harte Anforderung
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if not vision:
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return 0.0 # harte Anforderung
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return 1.0 if ("vl" in low or "llava" in low or "pixtral" in low) else 0.7 # dediziert > omni
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if role == "coder":
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return 1.0 if coder else 0.45 # Coder bevorzugt, andere notfalls
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return 1.0 if coder else 0.4 # Coder-Modell Pflicht für Empfehlung
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if role == "hermes":
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# Agent-Hirn: natives Tool-Calling Pflicht; Hermes-Familie am robustesten.
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# Agent-Hirn: Hermes-Familie am robustesten; sonst natives Tool-Calling Pflicht.
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if "hermes" in low:
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return 1.0
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return 0.85 if tools else 0.15
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return 0.6 if tools else 0.1
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if role == "fast":
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# schnelles Alltags-Hirn: klein/MoE bevorzugt (niedrige aktive Params = Tempo).
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return 1.0 if (moe or params <= 40) else 0.5
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# Alltags-Hirn braucht zuverlässige Tools; Größe/Tempo macht _pref.
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return 1.0 if tools else 0.6
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if role == "heavy":
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# schweres Reasoning: Wissen = Gesamt-Params (groß bevorzugt).
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return min(params / 70.0, 1.0)
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return 1.0
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if role == "scout":
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# Multimodal-Allrounder: Vision ein Plus, sonst solide Basis.
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return 0.9 if vision else 0.7
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return 0.5
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def _reason(role: str, caps: dict, fit: dict, suit: float, fits: bool, incomplete: bool) -> str:
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def _pref(role: str, params: float, tps: float) -> float:
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"""0..1 — rollengerechte GRÖSSEN-/TEMPO-Präferenz. 'fast' belohnt Tempo & Kleinheit,
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'heavy' Größe (Wissen), 'hermes' moderate Größe (muss warm + ko-resident bleiben)."""
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role = (role or "").lower()
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if role == "fast":
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speed = min(tps / 25.0, 1.0)
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size_ok = 1.0 if params <= 50 else 50.0 / params
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return speed * size_ok
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if role == "heavy":
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return min(params / 120.0, 1.0)
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if role == "hermes":
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return 1.0 if params <= 24 else max(0.15, 24.0 / params) # 7–24B ideal als Hirn
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if role == "vision":
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return 1.0 if params <= 12 else 0.7 # klein/günstig bevorzugt
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if role == "coder":
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return 0.5 + 0.5 * min(params / 80.0, 1.0)
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if role == "scout":
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return 1.0 if params <= 40 else 0.5
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return 0.5
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def _catalog_role_match(role: str, name: str) -> bool:
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"""Ist dieses Modell im kuratierten Katalog (Cookbook) genau für DIESE Rolle gelistet?
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Dann ist es der prinzipien-konforme Pick → starker Bonus."""
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meta = catalog.meta_for_name(name)
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return bool(meta and (meta.get("role") or "").lower() == (role or "").lower())
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def _reason(role: str, caps: dict, name: str, fit: dict, fits: bool,
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incomplete: bool, cat_match: bool) -> str:
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if incomplete:
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return "Download unvollständig"
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if role == "vision" and not caps.get("vision"):
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return "keine Vision-Fähigkeit"
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if role == "hermes" and caps.get("tools") == "no":
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if role == "coder" and not caps.get("coder"):
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return "kein Coder-Modell"
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if role == "hermes" and "hermes" not in (name or "").lower() and caps.get("tools") == "no":
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return "kein natives Tool-Calling"
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if not fits:
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return "passt nicht ins Budget (OOM)"
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bits = []
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if cat_match:
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bits.append("Katalog-Pick ✓")
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if role == "vision":
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bits.append("Vision ✓")
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if role == "coder" and caps.get("coder"):
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bits.append("Coder ✓")
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if role == "hermes":
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bits.append("Tools ✓" if caps.get("tools") != "no" else "ohne Tools")
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bits.append("Hermes" if "hermes" in (name or "").lower()
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else ("Tools ✓" if caps.get("tools") != "no" else "ohne Tools"))
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if caps.get("moe"):
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bits.append("MoE")
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bits.append(f"{fit['text']}, ~{fit['tps']:.0f} t/s")
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@@ -84,13 +117,14 @@ def recommend_for_role(role: str) -> dict:
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fit = evaluate_fit(params, quant, ctx, ram, name=m["name"])
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incomplete = bool(m.get("incomplete"))
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fits = (fit["level"] != "too_tight") and not incomplete
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suit = _suitability(role, caps, params, m["name"])
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tps = fit["tps"] or 0
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suit = _capability_suit(role, caps, m["name"])
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cat_match = _catalog_role_match(role, m["name"])
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suitable = suit >= 0.5 and fits
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fit_term = {"perfect": 1.0, "marginal": 0.3}.get(fit["level"], -2.0)
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score = (2.0 * suit) + fit_term \
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+ min((fit["tps"] or 0) / 80.0, 1.0) * 0.5 \
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+ min(params / 120.0, 1.0) * 0.5
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# Eignung dominiert (×2), rollengerechte Größe/Tempo (_pref), Katalog-Anker, dann Fit.
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score = (2.0 * suit) + _pref(role, params, tps) + (0.6 if cat_match else 0.0) + fit_term
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if not fits:
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score -= 5.0
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@@ -99,7 +133,7 @@ def recommend_for_role(role: str) -> dict:
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"params_b": round(params, 1), "quant": quant,
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"fit": fit, "suitable": suitable, "incomplete": incomplete,
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"score": round(score, 3),
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"reason": _reason(role, caps, fit, suit, fits, incomplete),
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"reason": _reason(role, caps, m["name"], fit, fits, incomplete, cat_match),
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})
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out.sort(key=lambda x: -x["score"])
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