Fix: Modell-Manager Rollen-Taxonomie vereinheitlicht (5 Rollen) + echter Vocab-Check
- Rollen ueberall = fast/heavy/coder/vision/scout (eine Quelle der Wahrheit): sources.py CATEGORIES (agent/reasoning raus, fast/heavy rein), llamaswap.ROLE_IDS, maintenance ROLE_MAP entfernt (Discover-Rollen == Serving-Rollen), Discover.tsx ROLE_METADATA, ModelBadges.ROLES, ActiveModelsCard (stale reasoning-Farbe raus). Behebt: Discover zeigte "Reasoning"/"agent"; aus Discover installierte Modelle landeten in keinem Cockpit-Slot. - gguf_meta: Vocab-Check jetzt ECHT - sha256 ueber die vollstaendige Token-Liste statt nur Metadaten. Familienunabhaengig (Qwen/Llama/Mistral/...). Verifiziert: Coder + Qwen3-0.6B byte-identisch (kompatibel), Qwen3.6 abweichend (inkompatibel), 0.11s/Scan. - RolesCard SPEC-Badge -> spec_active (Konsistenz mit Cockpit). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Hitonabi
@@ -11,6 +11,7 @@ Fälle zu unterscheiden (Qwen2.5 vs Qwen3 vs Qwen3.6 etc.). Die llama.cpp-Prüfu
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beim Laden bleibt der letzte Schiedsrichter.
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"""
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import hashlib
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import struct
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from functools import lru_cache
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@@ -79,16 +80,26 @@ def _read_fingerprint(path: str) -> dict | None:
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r.u32() # version
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r.u64() # tensor_count
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kv_count = r.u64()
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fp: dict = {"model": None, "pre": None, "arch": None, "n_vocab": None}
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fp: dict = {"model": None, "pre": None, "arch": None, "n_vocab": None,
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"tokens_sha": None}
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for _ in range(kv_count):
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key = r.gstr()
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vtype = r.u32()
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if key == "tokenizer.ggml.tokens" and vtype == _T_ARRAY:
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etype = r.u32()
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fp["n_vocab"] = r.u64()
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# Wir haben alles (model/pre kommen vor tokens) → abbrechen.
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count = r.u64()
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fp["n_vocab"] = count
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if etype != _T_STRING:
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return None
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# ECHTE Vocab-Identität: sha256 über die tatsächliche Token-Liste
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# (familienunabhängig — funktioniert für Qwen, Llama, Mistral, …).
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h = hashlib.sha256()
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h.update(count.to_bytes(8, "little"))
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for _ in range(count):
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n = r.u64()
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h.update(r.read(n))
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fp["tokens_sha"] = h.hexdigest()
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# model/pre kommen vor tokens → wir haben alles. Abbrechen.
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break
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if key in _WANT_STRINGS and vtype == _T_STRING:
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val = r.gstr()
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@@ -112,12 +123,12 @@ def _cached(path: str, mtime: float, size: int) -> tuple | None:
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fp = _read_fingerprint(path)
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if fp is None:
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return None
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return (fp.get("model"), fp.get("pre"), fp.get("n_vocab"), fp.get("arch"))
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return (fp.get("model"), fp.get("pre"), fp.get("n_vocab"), fp.get("arch"), fp.get("tokens_sha"))
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def fingerprint(path: str) -> dict | None:
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"""Tokenizer-Fingerprint eines GGUF (gecacht nach Pfad+mtime+size).
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Returns dict(model, pre, n_vocab, arch) oder None wenn nicht lesbar."""
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Returns dict(model, pre, n_vocab, arch, tokens_sha) oder None wenn nicht lesbar."""
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import os
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try:
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st = os.stat(path)
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@@ -126,16 +137,18 @@ def fingerprint(path: str) -> dict | None:
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t = _cached(path, st.st_mtime, st.st_size)
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if t is None:
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return None
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return {"model": t[0], "pre": t[1], "n_vocab": t[2], "arch": t[3]}
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return {"model": t[0], "pre": t[1], "n_vocab": t[2], "arch": t[3], "tokens_sha": t[4]}
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def vocab_key(path: str) -> tuple | None:
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"""Vergleichsschlüssel für Vocab-Kompatibilität: (model, pre, n_vocab).
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Genau diese Identität verlangt llama.cpp für Speculative Decoding."""
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"""ECHTER Vergleichsschlüssel für Vocab-Kompatibilität: (model, pre, n_vocab, sha256
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der vollständigen Token-Liste). Vergleicht den TATSÄCHLICHEN Vokabular-Inhalt, nicht
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nur Metadaten — familienunabhängig (Qwen, Llama, Mistral, …). Genau diese Identität
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verlangt llama.cpp für Speculative Decoding."""
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fp = fingerprint(path)
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if not fp or fp["n_vocab"] is None:
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if not fp or fp["n_vocab"] is None or not fp.get("tokens_sha"):
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return None
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return (fp["model"], fp["pre"], fp["n_vocab"])
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return (fp["model"], fp["pre"], fp["n_vocab"], fp["tokens_sha"])
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def compatible(target_path: str, draft_path: str) -> bool | None:
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@@ -20,8 +20,9 @@ from config import (
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log = logging.getLogger(__name__)
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# Kanonische Rollen (vereinheitlicht ggü. v1: kein manager/reviewer mehr).
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ROLE_IDS = {"vision", "coder", "agent", "scout"}
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# Kanonische Serving-Rollen — EINE Quelle der Wahrheit (identisch zu sources.ROLE_IDS,
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# maintenance, frontend ModelBadges.ROLES). Kein agent/reasoning mehr.
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ROLE_IDS = {"fast", "heavy", "coder", "vision", "scout"}
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_CTX_RE = re.compile(r"-(?:c|-ctx-size)\s+(\d+)")
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_PATH_RE = re.compile(r"-(?:m|-model)\s+([^\s]+)")
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@@ -169,18 +169,10 @@ def model_upgrades() -> list[dict]:
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for s in (llamaswap.read_config().get("models") or {}).values())
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out = []
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ROLE_MAP = {
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"agent": "fast",
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"scout": "fast",
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"coder": "coder",
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"vision": "vision"
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}
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# Discover-Rollen == Serving-Rollen (fast/heavy/coder/vision/scout) → kein Mapping mehr.
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for c in disc.get("categories", []):
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disc_role = c["role"]
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mapped_role = ROLE_MAP.get(disc_role, disc_role)
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if mapped_role not in active_roles:
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role = c["role"]
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if role not in active_roles: # nur Rollen, die bereits ein Modell haben
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continue
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rec = c.get("recommended")
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@@ -190,7 +182,7 @@ def model_upgrades() -> list[dict]:
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stem = base[:-5] if base.endswith("-gguf") else base
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if base in cmds or (stem and stem in cmds):
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continue
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out.append({"role": mapped_role, "title": c["title"], "repo": rec})
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out.append({"role": role, "title": c["title"], "repo": rec})
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return out
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@@ -1,24 +1,31 @@
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"""
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Vertrauenswürdige Quellen + Kategorien für die automatische Modell-Entdeckung.
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Portiert aus Mission Control v1 (sources.py). Rollen sind die EINE Quelle der
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Wahrheit (vereinheitlicht): vision · coder · agent · scout.
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Rollen = die EINE Quelle der Wahrheit, identisch zu den llama-swap-Serving-Rollen
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und der UI: fast · heavy · coder · vision · scout.
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"""
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# HF-Orgs, die zuverlässig aktuelle, hochwertige GGUF-Quants veröffentlichen.
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TRUSTED_AUTHORS = ["unsloth", "bartowski", "ggml-org", "lmstudio-community"]
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# Kanonische Rollen — eine Quelle der Wahrheit (deckt sich mit llamaswap.ROLE_IDS,
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# maintenance.ROLE_MAP, frontend ModelBadges.ROLES + Discover.ROLE_METADATA).
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ROLE_IDS = ["fast", "heavy", "coder", "vision", "scout"]
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# Kategorien (Reihenfolge = Anzeige + Zuordnungs-Priorität). Ein Modell wird der
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# ERSTEN Kategorie zugeordnet, deren Stichwort im Repo-Namen vorkommt; sonst „scout".
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# Die `role` ist zugleich der Alias-Vorschlag und gehört zu ROLE_IDS.
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# Die `role` ist zugleich der Alias-Vorschlag und EINE der 5 kanonischen Rollen.
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CATEGORIES = [
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{"role": "vision", "title": "Bilder verstehen", "icon": "eye",
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"kw": ["-vl-", "-vl", "vision", "llava", "multimodal", "-mm-", "pixtral"]},
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{"role": "coder", "title": "Coden & Programmieren", "icon": "code",
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"kw": ["coder", "-code-", "code-", "codestral", "starcoder"]},
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{"role": "agent", "title": "Agenten & Tool-Use", "icon": "layers",
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"kw": ["hermes", "-tool", "command-r", "watt", "-fc-", "function"]},
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{"role": "scout", "title": "Allrounder & Chat", "icon": "compass",
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"kw": []}, # Fallback: instruct/chat-Modelle
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{"role": "heavy", "title": "Schweres Reasoning", "icon": "brain",
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"kw": ["reasoning", "-think", "thinking", "gpt-oss", "deepseek-r", "-r1", "qwq",
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"-70b", "-72b", "-120b", "-123b", "-235b", "-405b", "-a10b", "-a22b"]},
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{"role": "fast", "title": "Schnelles Alltags-Hirn", "icon": "zap",
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"kw": ["-a3b", "-a1", "-a2", "-30b", "-32b", "-14b", "-8b", "-7b", "-4b", "-moe"]},
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{"role": "scout", "title": "Multimodal-Allrounder", "icon": "compass",
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"kw": []}, # Fallback: instruct/chat-Modelle, die in keine Spezialrolle fallen
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]
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# Repo-Namensteile, die bei der Entdeckung übersprungen werden (Roh-/Spezialformate).
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+11
-11
File diff suppressed because one or more lines are too long
Vendored
+1
-1
@@ -7,7 +7,7 @@
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<link rel="manifest" href="/manifest.webmanifest" />
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<link rel="icon" type="image/svg+xml" href="/favicon.svg" />
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<title>Mission Control 2.0</title>
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<script type="module" crossorigin src="/assets/index-D9tRIUA4.js"></script>
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<script type="module" crossorigin src="/assets/index-CrPS-3Fn.js"></script>
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<link rel="stylesheet" crossorigin href="/assets/index-CjZVMvhL.css">
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</head>
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<body>
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@@ -8,7 +8,6 @@ const ROLE_TONE: Record<string, string> = {
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fast: "bg-cyan-500/15 text-cyan-400 border-cyan-500/25",
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heavy: "bg-amber-500/15 text-amber-400 border-amber-500/25",
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coder: "bg-violet-500/15 text-violet-400 border-violet-500/25",
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reasoning: "bg-emerald-500/15 text-emerald-400 border-emerald-500/25",
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vision: "bg-pink-500/15 text-pink-400 border-pink-500/25",
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scout: "bg-teal-500/15 text-teal-400 border-teal-500/25",
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hermes: "bg-indigo-500/15 text-indigo-400 border-indigo-500/25",
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@@ -57,7 +57,7 @@ export function RolesCard() {
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PC
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</span>
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)}
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{m.spec_draft_model && (
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{m.spec_active && (
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<span className="text-[7px] leading-none font-mono font-bold bg-emerald-500/10 text-emerald-400 border border-emerald-500/20 px-1 py-0.5 rounded" title={`Speculative Decoding aktiv (Draft: ${m.spec_draft_model})`}>
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SPEC
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</span>
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@@ -1,7 +1,8 @@
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import { type Fit } from "@/lib/api"
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import { cn } from "@/lib/utils"
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export const ROLES = ["fast", "heavy", "coder", "agent", "vision", "scout"]
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// Die 5 kanonischen Serving-Rollen (identisch zu Backend sources.py/llamaswap.ROLE_IDS).
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export const ROLES = ["fast", "heavy", "coder", "vision", "scout"]
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export function FitBadge({ fit }: { fit: Fit }) {
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const tone = {
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@@ -1,5 +1,5 @@
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import { useState } from "react"
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import { Download, Search, Star, Layers, Check, Bot, Eye, Code, Brain, Compass, ChevronDown, ChevronUp } from "lucide-react"
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import { Download, Search, Star, Layers, Check, Zap, Eye, Code, Brain, Compass, ChevronDown, ChevronUp } from "lucide-react"
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import { api } from "@/lib/api"
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import { useModels, useUpdates, useDiscover } from "@/lib/queries"
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import { cn } from "@/lib/utils"
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@@ -7,30 +7,31 @@ import { fmtBytes } from "@/lib/format"
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import { FitBadge } from "@/components/models/ModelBadges"
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import { AddModel } from "./AddModel"
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// Die 5 kanonischen Rollen (identisch zu sources.py / ModelBadges.ROLES).
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const ROLE_METADATA: Record<string, { title: string; desc: string; icon: any }> = {
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vision: {
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title: "Bilder & Vision",
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desc: "Verarbeitet Bilder, Screenshots und visuelle Diagramme in multimodalen Chats.",
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icon: Eye
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fast: {
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title: "Schnelles Alltags-Hirn",
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desc: "Schnelle MoE-Antworten für Chat, Zusammenfassungen und alltägliche Aufgaben.",
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icon: Zap
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},
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heavy: {
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title: "Schweres Reasoning",
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desc: "Große Modelle für komplexe Logik, Mathematik und tiefgründiges Planen.",
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icon: Brain
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},
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coder: {
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title: "Coden & Entwicklung",
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desc: "Autovervollständigung, Refactoring und Codegenerierung direkt in deiner IDE.",
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icon: Code
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},
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reasoning: {
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title: "Logik & Nachdenken",
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desc: "Komplexe logische Gedankengänge, Mathematik und tiefgründiges Planen (Reasoning).",
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icon: Brain
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},
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agent: {
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title: "Autonomer Agent (Hermes)",
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desc: "Führt selbstständig Terminalbefehle aus und interagiert mit deinem Homelab.",
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icon: Bot
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vision: {
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title: "Bilder & Vision",
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desc: "Verarbeitet Bilder, Screenshots und visuelle Diagramme in multimodalen Chats.",
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icon: Eye
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},
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scout: {
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title: "Allrounder & Chat",
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desc: "Schnelle Antworten, Zusammenfassungen, Übersetzungen und alltägliche Fragen.",
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title: "Multimodal-Allrounder",
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desc: "Multimodaler Allrounder für schnelle Antworten, Übersetzungen und Alltag.",
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icon: Compass
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}
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}
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