cff3f0b1a8
Backend-Services: fit/caps/sources (portiert), discover (live HF + Fit + Caps + ranked recommendation), llama-swap write/register + groups (Ko- Residenz swap:false), LiteLLM-Gateway-Config + gateway-Service (model:auto + Fallbacks). Router: discover/fit/register/groups/routing; health zeigt gateway_reachable. Frontend: Modelle&Routing mit Caps-Chips, Fit-Badges, Discover-Tab (live), Routing-View. Lokal verifiziert: Backend-Smoke (alle Endpunkte) + Frontend-Build + Browser (Shell, Discover, Caps/Fit). Box-Verifikation offen. Docs: README + docs/STATUS.md (Phasen-Tracker + Resume-Guide). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
132 lines
5.6 KiB
Python
132 lines
5.6 KiB
Python
"""
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Modell-Capabilities — EINE Quelle der Wahrheit für Modell-Eigenschaften
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(MoE / Tools / Vision / Coder / Reasoning / Embedding / Kontext).
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Portiert aus Mission Control v1 (model_caps.py).
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Quellen, geschichtet: GGUF-Header (offline, authoritativ) → cmd-Flags
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(--jinja/--mmproj) → HF-Block (tags + chat_template) → Familien-Fallback.
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Tool-Fähigkeit dreistufig: yes (bestätigt) | likely (Familie) | no.
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"""
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import re
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import struct
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from services.fit import extract_active_params_b, extract_params_b
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_GGUF_FIXED = {0: 1, 1: 1, 2: 2, 3: 2, 4: 4, 5: 4, 6: 4, 7: 1, 10: 8, 11: 8, 12: 8}
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def _read_gguf_meta(path: str) -> dict:
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"""Liest nur den GGUF-Metadaten-Header (architecture/context_length/expert_count/
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parameter_count). Bricht vor dem Tokenizer-Array ab → schnell, lädt NICHT das Modell."""
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out: dict = {}
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try:
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with open(path, "rb") as f:
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if f.read(4) != b"GGUF":
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return {}
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struct.unpack("<I", f.read(4))[0]
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f.read(8)
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kv = struct.unpack("<Q", f.read(8))[0]
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def ru32() -> int: return struct.unpack("<I", f.read(4))[0]
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def ru64() -> int: return struct.unpack("<Q", f.read(8))[0]
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def rstr() -> str: return f.read(ru64()).decode("utf-8", "replace")
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def rval(t: int):
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if t == 8: return rstr()
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if t == 0: return struct.unpack("<B", f.read(1))[0]
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if t == 1: return struct.unpack("<b", f.read(1))[0]
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if t == 2: return struct.unpack("<H", f.read(2))[0]
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if t == 3: return struct.unpack("<h", f.read(2))[0]
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if t == 4: return struct.unpack("<I", f.read(4))[0]
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if t == 5: return struct.unpack("<i", f.read(4))[0]
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if t == 6: return struct.unpack("<f", f.read(4))[0]
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if t == 7: return f.read(1) != b"\x00"
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if t == 10: return struct.unpack("<Q", f.read(8))[0]
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if t == 11: return struct.unpack("<q", f.read(8))[0]
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if t == 12: return struct.unpack("<d", f.read(8))[0]
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if t == 9:
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et = ru32(); cnt = ru64()
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if et == 8:
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for _ in range(cnt):
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f.seek(ru64(), 1)
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elif et == 9:
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for _ in range(cnt):
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rval(9)
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else:
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f.seek(cnt * _GGUF_FIXED.get(et, 0), 1)
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return None
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raise ValueError(f"unbekannter GGUF-Typ {t}")
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want = {"architecture", "context_length", "expert_count", "parameter_count"}
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for _ in range(kv):
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key = rstr()
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t = ru32()
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if key == "tokenizer.ggml.tokens":
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break
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v = rval(t)
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short = key.split(".")[-1]
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if short in want and short not in out:
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out[short] = v
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except Exception:
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return out
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return out
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_TOOL_FAMILIES = (
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"qwen2.5", "qwen3", "qwen2", "hermes", "mistral", "mixtral", "devstral",
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"command-r", "command_r", "llama-3.1", "llama3.1", "llama-3.3", "llama-4", "llama4",
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"functionary", "watt", "firefunction", "granite", "glm-4", "glm-5", "ministral",
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)
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_REASON_KW = (
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"-r1", "deepseek-r1", "qwq", "magistral", "-think", "thinking", "-o1",
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"gpt-oss", "reasoning", "exaone-deep", "phi-4-reasoning", "phi-4-mini-reasoning",
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)
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_CODE_KW = ("coder", "-code", "code-", "codestral", "starcoder", "deepseek-coder")
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_VISION_KW = ("-vl", "vision", "llava", "pixtral", "multimodal", "-mm-", "qwen3vl", "qwen2-vl")
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_EMBED_KW = ("bge", "e5-", "gte-", "nomic-embed", "embed")
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_MOE_ARCH = ("moe", "mixtral", "deepseek2", "deepseek3", "llama4", "qwen3moe", "grok")
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def capabilities(name: str = "", cmd: str = "", gguf_path: str = "", hf: dict | None = None) -> dict:
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"""Capability-Tag-Set für ein Modell. Alle Quellen optional — nutzt, was da ist."""
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low = (name or "").lower()
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cmdl = (cmd or "").lower()
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hf = hf or {}
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meta = _read_gguf_meta(gguf_path) if gguf_path else {}
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arch = str(meta.get("architecture") or hf.get("architecture") or "").lower()
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tags = [str(t).lower() for t in (hf.get("tags") or [])]
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chat_tpl = str(hf.get("chat_template") or "")
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expert_count = int(meta.get("expert_count") or 0)
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moe = (
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expert_count > 1
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or any(a in arch for a in _MOE_ARCH)
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or bool(re.search(r"\d+x\d+\.?\d*b", low))
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or bool(re.search(r"a\d+\.?\d*b", low))
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)
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active_b = extract_active_params_b(name)
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pcount = int(meta.get("parameter_count") or 0)
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params_b = round(pcount / 1e9, 1) if pcount else extract_params_b(name)
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ctx = meta.get("context_length")
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if not ctx:
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m = re.search(r"-(?:c|-ctx-size)\s+(\d+)", cmdl)
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ctx = int(m.group(1)) if m else None
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tool_confirmed = "--jinja" in cmdl or "tool_call" in chat_tpl or "<tools>" in chat_tpl
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tool_family = any(fam in low for fam in _TOOL_FAMILIES) or "function-calling" in tags
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tools = "yes" if tool_confirmed else ("likely" if tool_family else "no")
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vision = "--mmproj" in cmdl or "vl" in arch or "clip" in arch or any(k in low for k in _VISION_KW)
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coder = any(k in low for k in _CODE_KW)
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reasoning = any(k in low for k in _REASON_KW) or "reasoning" in tags
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embedding = "bert" in arch or any(k in low for k in _EMBED_KW)
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return {
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"moe": moe, "active_b": active_b, "tools": tools, "vision": vision,
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"coder": coder, "reasoning": reasoning, "embedding": embedding,
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"ctx": ctx, "params_b": params_b or None, "arch": arch or None,
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}
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