Hitonabi
701476179e
- Profi-Suche: HF-Suche mit &full=true (liefert Tags); Multi-Capability-Chips (Code/Bild/Logik/MoE·aktiv/Tools(yes|likely)/Chat) aus Name + HF-Tags. - Facetten-Filter (Mehrfachauswahl: Tools/MoE/Logik/Bild/Code) statt Einzel-Kategorie — clientseitig auf den Treffern (Index für Fit/Best bleibt). - Discover: dieselben Chips; Backend liefert HF-Tags je Modell mit. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
557 lines
22 KiB
Python
557 lines
22 KiB
Python
"""
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Cookbook Router: Verbindet die HuggingFace API mit der Odysseus-Hardware-Berechnung.
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"""
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import httpx
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import json
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import os
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import re
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import time
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel
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import psutil
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from ruamel.yaml.scalarstring import LiteralScalarString
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from auth import auth
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from hw_math import evaluate_fit, max_ctx_for, extract_params_b
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from config import (MODELS_DIR, CMD_TEMPLATE, DEFAULT_TTL, HF_DOWNLOAD_ENV, USER_RECIPES_PATH,
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DISCOVER_CACHE_PATH, DISCOVER_TTL, hf_bin)
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from llamaswap import read_config, write_config, model_id_from_path, set_role_alias
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from jobengine import start_job, JOBS, attach_download_progress
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from recipes import RECIPES, UPGRADES
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from sources import TRUSTED_AUTHORS, CATEGORIES, SKIP_TOKENS
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router = APIRouter(prefix="/api/cookbook", dependencies=[Depends(auth)])
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_FIT_ORDER = {"perfect": 0, "marginal": 1, "too_tight": 2}
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# ---------------------------------------------------------------------------
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# Eigene Setups (vom Nutzer) — persistente JSON-Datei, gleiches Schema wie RECIPES.
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# ---------------------------------------------------------------------------
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def load_user_recipes() -> list:
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try:
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if USER_RECIPES_PATH.exists():
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return json.loads(USER_RECIPES_PATH.read_text(encoding="utf-8")) or []
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except Exception: # noqa: BLE001
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pass
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return []
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def save_user_recipes(items: list) -> None:
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USER_RECIPES_PATH.parent.mkdir(parents=True, exist_ok=True)
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tmp = USER_RECIPES_PATH.with_name(USER_RECIPES_PATH.name + ".tmp")
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tmp.write_text(json.dumps(items, ensure_ascii=False, indent=2), encoding="utf-8")
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os.replace(tmp, USER_RECIPES_PATH)
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def all_recipes() -> list:
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"""Eingebaute + eigene Setups (eigene tragen 'user': True)."""
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return list(RECIPES) + load_user_recipes()
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class AnalyzeRequest(BaseModel):
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repo_id: str
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ctx: int = 8192
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class EvaluateRequest(BaseModel):
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params_b: float
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quant: str
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ctx: int
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class InstallRecipeReq(BaseModel):
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recipe_id: str
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hf_token: str | None = None
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class InstallModelReq(BaseModel):
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repo: str
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role: str
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params_b: float
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quant: str = "Q4_K_M"
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file: str | None = None # konkrete GGUF-Datei; None → _pick_gguf wählt automatisch
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ctx: int | None = None # None → max_ctx_for (Hardware-Optimum)
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hf_token: str | None = None
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class UserRecipeModelReq(BaseModel):
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repo: str
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role: str
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quant: str = "Q4_K_M"
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name: str | None = None
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why: str | None = None
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class UserRecipeReq(BaseModel):
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title: str
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desc: str = ""
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icon: str = "box"
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models: list[UserRecipeModelReq]
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def extract_quant(filename: str) -> str:
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m = re.search(r"(Q\d_[A-Z0-9_]+|IQ\d_[A-Z0-9_]+|FP16|BF16)", filename, re.IGNORECASE)
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return m.group(1).upper() if m else "Q4_K_M"
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@router.post("/analyze")
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async def analyze_repo(req: AnalyzeRequest):
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"""Holt die GGUF Dateien von HuggingFace und berechnet den Hardware-Fit."""
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url = f"https://huggingface.co/api/models/{req.repo_id}/tree/main"
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async with httpx.AsyncClient() as client:
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try:
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resp = await client.get(url, timeout=10.0)
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resp.raise_for_status()
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tree = resp.json()
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"HuggingFace Fehler: {str(e)}")
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gguf_files = [f["path"] for f in tree if f.get("path", "").endswith(".gguf")]
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if not gguf_files:
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return {"files": []}
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params_b = extract_params_b(req.repo_id)
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# Ermittle RAM des Systems (da APU = Shared Memory)
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ram_gb = psutil.virtual_memory().total / (1024**3)
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results = []
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for f in gguf_files:
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quant = extract_quant(f)
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fit = evaluate_fit(params_b, quant, req.ctx, ram_gb, name=req.repo_id)
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# Priority-Score, um den besten Fit an oberste Stelle zu setzen.
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# "Q4_K_M" ist oft der Sweetspot.
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priority = 0
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if fit["level"] == "perfect":
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priority += 10
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if quant == "Q4_K_M": priority += 5
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elif quant.startswith("Q4"): priority += 4
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elif quant.startswith("Q5"): priority += 3
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results.append({
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"filename": f,
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"quant": quant,
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"fit": fit,
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"optimal_ctx": max_ctx_for(params_b, quant, ram_gb),
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"priority": priority
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})
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# Sortieren: Highest priority first, dann nach tps (schnellste zuerst)
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results.sort(key=lambda x: (x["priority"], x["fit"]["tps"]), reverse=True)
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return {
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"repo": req.repo_id,
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"params_b": params_b,
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"sys_ram_gb": round(ram_gb, 1),
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"files": results
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}
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@router.post("/evaluate")
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def evaluate_single(req: EvaluateRequest):
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ram_gb = psutil.virtual_memory().total / (1024**3)
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fit = evaluate_fit(req.params_b, req.quant, req.ctx, ram_gb)
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fit["optimal_ctx"] = max_ctx_for(req.params_b, req.quant, ram_gb)
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return fit
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@router.get("/recipes")
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def recipes():
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"""Use-Case-Setups mit Hardware-Fit pro Modell + Stack-Gesamturteil. Da llama-swap nur EIN
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Modell gleichzeitig lädt, ist das Stack-Urteil der schlechteste (= größte) Einzel-Fit."""
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ram_gb = psutil.virtual_memory().total / (1024 ** 3)
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out = []
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for r in all_recipes():
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models, worst = [], "perfect"
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for m in r["models"]:
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fit = evaluate_fit(m["params_b"], m["quant"], 8192, ram_gb, name=m.get("name", ""))
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models.append({**m, "fit": fit, "optimal_ctx": max_ctx_for(m["params_b"], m["quant"], ram_gb)})
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if _FIT_ORDER[fit["level"]] > _FIT_ORDER[worst]:
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worst = fit["level"]
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out.append({**r, "models": models, "fit_level": worst, "user": bool(r.get("user"))})
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# „Beste Wahl": das reichste KURATIERTE Setup, das komplett auf die Hardware passt.
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fitting = [r for r in out if not r["user"] and r["fit_level"] != "too_tight"]
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rec = max(fitting, key=lambda r: len(r["models"]), default=None) if fitting else None
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return {"recipes": out, "sys_ram_gb": round(ram_gb, 1), "recommended_id": rec["id"] if rec else None}
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@router.post("/user-recipe")
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def create_user_recipe(req: UserRecipeReq):
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"""Eigenes Cookbook-Setup anlegen. params_b wird aus dem Repo-Namen abgeleitet, damit der
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Nutzer nur Repo + Rolle + Quant angeben muss; Fit/Kontext kommen wie immer zur Laufzeit."""
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if not req.title.strip():
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raise HTTPException(400, "Bitte einen Titel angeben.")
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models = []
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for m in req.models:
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if not m.repo.strip():
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continue
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models.append({
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"role": (m.role or "modell").strip(),
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"name": (m.name or m.repo.split("/")[-1]).strip(),
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"repo": m.repo.strip(),
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"params_b": extract_params_b(m.repo),
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"quant": (m.quant or "Q4_K_M").strip(),
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"why": (m.why or "").strip(),
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})
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if not models:
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raise HTTPException(400, "Mindestens ein Modell (Repo) angeben.")
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items = load_user_recipes()
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base = "user-" + (re.sub(r"[^a-z0-9]+", "-", req.title.lower()).strip("-") or "setup")
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rid, n = base, 2
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existing = {r.get("id") for r in items} | {r["id"] for r in RECIPES}
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while rid in existing:
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rid, n = f"{base}-{n}", n + 1
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items.append({"id": rid, "title": req.title.strip(), "icon": (req.icon or "box").strip(),
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"desc": req.desc.strip(), "models": models, "user": True})
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save_user_recipes(items)
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return {"ok": True, "id": rid}
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@router.put("/user-recipe/{recipe_id}")
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def update_user_recipe(recipe_id: str, req: UserRecipeReq):
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"""Bestehendes eigenes Setup aktualisieren (Titel, Beschreibung, Modelle)."""
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items = load_user_recipes()
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idx = next((i for i, r in enumerate(items) if r.get("id") == recipe_id), None)
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if idx is None:
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raise HTTPException(404, "Eigenes Setup nicht gefunden.")
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if not req.title.strip():
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raise HTTPException(400, "Bitte einen Titel angeben.")
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models = []
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for m in req.models:
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if not m.repo.strip():
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continue
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models.append({
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"role": (m.role or "modell").strip(),
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"name": (m.name or m.repo.split("/")[-1]).strip(),
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"repo": m.repo.strip(),
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"params_b": extract_params_b(m.repo),
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"quant": (m.quant or "Q4_K_M").strip(),
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"why": (m.why or "").strip(),
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})
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if not models:
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raise HTTPException(400, "Mindestens ein Modell (Repo) angeben.")
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items[idx] = {**items[idx], "title": req.title.strip(), "icon": (req.icon or "box").strip(),
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"desc": req.desc.strip(), "models": models}
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save_user_recipes(items)
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return {"ok": True, "id": recipe_id}
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@router.delete("/user-recipe/{recipe_id}")
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def delete_user_recipe(recipe_id: str):
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items = load_user_recipes()
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kept = [r for r in items if r.get("id") != recipe_id]
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if len(kept) == len(items):
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raise HTTPException(404, "Eigenes Setup nicht gefunden.")
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save_user_recipes(kept)
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return {"ok": True}
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@router.post("/install-recipe")
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def install_recipe(req: InstallRecipeReq):
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"""Komplettes Setup installieren: jedes Modell als Download-Job starten UND sofort mit
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optimalem (gedeckeltem) Kontext in die config.yaml eintragen. llama-swap (-watch-config)
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übernimmt es, sobald die Datei da ist."""
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recipe = next((r for r in all_recipes() if r["id"] == req.recipe_id), None)
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if not recipe:
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raise HTTPException(404, "Setup nicht gefunden.")
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ram_gb = psutil.virtual_memory().total / (1024 ** 3)
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env = dict(HF_DOWNLOAD_ENV)
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if req.hf_token:
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env["HF_TOKEN"] = req.hf_token
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cfg = read_config()
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job_ids = []
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for m in recipe["models"]:
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file = _pick_gguf(m["repo"], m.get("quant", "Q4_K_M"))
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if not file:
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continue # kein GGUF im Repo gefunden -> Modell ueberspringen (Rest installiert trotzdem)
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# Vision-Modelle: mmproj-Projektor mitholen (nötig für --mmproj in llama.cpp).
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mmproj = _pick_mmproj(m["repo"])
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target = MODELS_DIR / m["repo"].split("/")[-1]
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target.mkdir(parents=True, exist_ok=True)
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args = [hf_bin(), "download", m["repo"], file]
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if mmproj:
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args.append(mmproj) # hf-cli unterstützt mehrere Dateien in einem Aufruf
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args += ["--local-dir", str(target)]
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jid = start_job(args, f"download {m['name']}", env=env)
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JOBS[jid]["result_path"] = str(target / file)
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attach_download_progress(jid, str(target), hf_file_size(m["repo"], file))
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job_ids.append(jid)
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# Eintrag jetzt schon schreiben — optimaler Kontext für die Hardware.
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ctx = max_ctx_for(m["params_b"], m["quant"], ram_gb)
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path = str(target / file)
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cmd = CMD_TEMPLATE.replace("{model}", path).replace("{ctx}", str(ctx))
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if mmproj:
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cmd = cmd.rstrip() + f" --mmproj {target / mmproj} --jinja"
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# Schluessel = sprechender Modellname; Rolle (aus dem Rezept) als Alias.
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mid = model_id_from_path(path)
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cfg["models"][mid] = {"cmd": LiteralScalarString(cmd + "\n"), "ttl": DEFAULT_TTL}
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set_role_alias(cfg, mid, m["role"])
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write_config(cfg)
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return {"job_ids": job_ids, "count": len(job_ids)}
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def hf_file_size(repo: str, file: str) -> int:
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"""Groesse einer Datei im HF-Repo (Bytes) fuer die Fortschrittsanzeige. 0 wenn unbekannt.
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GGUFs sind LFS -> ggf. unter 'lfs.size'."""
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try:
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with httpx.Client(timeout=10.0) as c:
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tree = c.get(f"https://huggingface.co/api/models/{repo}/tree/main").json()
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for f in tree:
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if isinstance(f, dict) and f.get("path") == file:
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return int(f.get("size") or (f.get("lfs") or {}).get("size") or 0)
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except Exception: # noqa: BLE001
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return 0
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return 0
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def _pick_gguf(repo: str, quant: str = "Q4_K_M") -> str | None:
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"""Beste GGUF-Datei eines Repos auflösen: bevorzugt gewünschten Quant, keine Split-Teile."""
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try:
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with httpx.Client(timeout=10.0) as c:
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tree = c.get(f"https://huggingface.co/api/models/{repo}/tree/main").json()
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except Exception: # noqa: BLE001
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return None
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ggufs = [f["path"] for f in tree if isinstance(f, dict) and str(f.get("path", "")).endswith(".gguf")]
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if not ggufs:
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return None
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pref = [g for g in ggufs if quant.lower() in g.lower() and "-of-" not in g]
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nosplit = [g for g in ggufs if "-of-" not in g]
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return (pref or nosplit or ggufs)[0]
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def _pick_mmproj(repo: str) -> str | None:
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"""Sucht den mmproj-Projektor (Vision-Modelle) im Repo — F16/BF16 bevorzugt.
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Gibt None zurueck, wenn kein Projektor vorhanden (= kein Vision-Modell)."""
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try:
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with httpx.Client(timeout=10.0) as c:
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tree = c.get(f"https://huggingface.co/api/models/{repo}/tree/main").json()
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except Exception: # noqa: BLE001
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return None
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candidates = [
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f["path"] for f in tree
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if isinstance(f, dict) and "mmproj" in str(f.get("path", "")).lower()
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and str(f.get("path", "")).endswith(".gguf")
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]
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if not candidates:
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return None
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# F16/BF16 hat die beste Projektor-Qualität; nimm das erste wenn keins passt.
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pref = [c for c in candidates if "f16" in c.lower() or "bf16" in c.lower()]
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return (pref or candidates)[0]
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# ---------------------------------------------------------------------------
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# Automatische Modell-Entdeckung ("aktuell beste Modelle", live von HF + Cache).
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# ---------------------------------------------------------------------------
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def _categorize(repo_id: str) -> str:
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low = repo_id.lower()
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for cat in CATEGORIES:
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if any(k in low for k in cat["kw"]):
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return cat["role"]
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return "scout" # Fallback: Allrounder
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def _installed_index() -> dict:
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"""alias -> kleingeschriebene cmd-Zeile der installierten Modelle (fuer Bestandsabgleich)."""
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return {alias: str(spec.get("cmd", "")).lower()
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for alias, spec in (read_config().get("models") or {}).items()}
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def _model_installed(repo: str, idx: dict) -> str | None:
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"""Liefert den Alias, unter dem dieses Repo schon installiert ist — sonst None."""
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base = repo.split("/")[-1].lower()
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# GGUF-Repos heissen meist '<modell>-GGUF' -> auch ohne Suffix vergleichen
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stem = base[:-5] if base.endswith("-gguf") else base
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for alias, cmd in idx.items():
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if base in cmd or (stem and stem in cmd):
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return alias
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return None
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def _model_requirements(role: str, quant: str, fit: dict) -> list[str]:
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"""Ehrliche Voraussetzungen/Hinweise pro Modell (Klartext fuer Anfaenger)."""
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reqs = []
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if role == "vision":
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reqs.append("Bild-Modell: Projektor (mmproj-F16) wird automatisch mitgeladen und --jinja gesetzt.")
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if quant.upper() in ("FP16", "BF16", "F16", "F32"):
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reqs.append("Unquantisiert — sehr groß. Eine Q4/Q5-Variante ist meist die bessere Wahl.")
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if fit["level"] == "too_tight":
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reqs.append("Größer als dein Speicher — nur mit kleinerem Quant realistisch.")
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elif fit["level"] == "marginal":
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reqs.append("Läuft, wird aber knapp — andere Programme währenddessen besser schließen.")
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return reqs
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def _fetch_author_models(author: str) -> list:
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url = (f"https://huggingface.co/api/models?author={author}"
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f"&filter=gguf&sort=downloads&direction=-1&limit=40")
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try:
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with httpx.Client(timeout=12.0) as c:
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data = c.get(url).json()
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return data if isinstance(data, list) else []
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except Exception: # noqa: BLE001
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return []
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def refresh_discover(ram_gb: float) -> dict:
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"""Quellen live abfragen, kategorisieren, nach Hardware-Fit + Beliebtheit ranken
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und cachen. Wirft nur, wenn KEINE einzige Quelle erreichbar war."""
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raw, seen, ok = [], set(), 0
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for author in TRUSTED_AUTHORS:
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models = _fetch_author_models(author)
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if models:
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ok += 1
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for m in models:
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rid = m.get("id")
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if not rid or rid in seen:
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continue
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seen.add(rid)
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raw.append(m)
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if ok == 0:
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raise RuntimeError("Keine Quelle erreichbar.")
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by_cat = {c["role"]: [] for c in CATEGORIES}
|
|
for m in raw:
|
|
rid = m["id"]
|
|
low = rid.lower()
|
|
if any(tok in low for tok in SKIP_TOKENS):
|
|
continue
|
|
role = _categorize(rid)
|
|
params_b = extract_params_b(rid)
|
|
quant = "Q4_K_M" # Referenz-Quant fuer die Fit-Einschaetzung
|
|
fit = evaluate_fit(params_b, quant, 8192, ram_gb, name=rid)
|
|
by_cat[role].append({
|
|
"name": rid.split("/")[-1], "author": rid.split("/")[0], "repo": rid,
|
|
"role": role, "params_b": params_b, "quant": quant,
|
|
"tags": [str(t) for t in (m.get("tags") or [])], # HF-Tags für Capability-Erkennung (Phase 10)
|
|
"downloads": int(m.get("downloads") or 0), "likes": int(m.get("likes") or 0),
|
|
"lastModified": m.get("lastModified"),
|
|
"fit": fit, "optimal_ctx": max_ctx_for(params_b, quant, ram_gb),
|
|
})
|
|
|
|
cats = []
|
|
for c in CATEGORIES:
|
|
items = by_cat[c["role"]]
|
|
# Passendes zuerst (perfect/marginal vor too_tight), dann nach Downloads.
|
|
items.sort(key=lambda x: (x["fit"]["level"] != "too_tight", x["downloads"]), reverse=True)
|
|
top = items[:4]
|
|
if top:
|
|
cats.append({"role": c["role"], "title": c["title"], "icon": c["icon"], "models": top})
|
|
|
|
data = {"updated": time.time(), "categories": cats}
|
|
try:
|
|
DISCOVER_CACHE_PATH.parent.mkdir(parents=True, exist_ok=True)
|
|
tmp = DISCOVER_CACHE_PATH.with_name(DISCOVER_CACHE_PATH.name + ".tmp")
|
|
tmp.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
|
|
os.replace(tmp, DISCOVER_CACHE_PATH)
|
|
except Exception: # noqa: BLE001
|
|
pass # Cache ist nur Beschleunigung — Entdeckung funktioniert auch ohne Schreibrecht
|
|
return data
|
|
|
|
|
|
def _load_discover() -> dict | None:
|
|
try:
|
|
if DISCOVER_CACHE_PATH.exists():
|
|
return json.loads(DISCOVER_CACHE_PATH.read_text(encoding="utf-8"))
|
|
except Exception: # noqa: BLE001
|
|
pass
|
|
return None
|
|
|
|
|
|
@router.get("/discover")
|
|
def discover(force: bool = False):
|
|
"""Aktuell beste Modelle je Kategorie — live aus den Quellen (gecacht, TTL).
|
|
Markiert pro Modell, ob es schon installiert ist, und nennt Voraussetzungen."""
|
|
ram_gb = psutil.virtual_memory().total / (1024 ** 3)
|
|
cached = _load_discover()
|
|
fresh = bool(cached) and (time.time() - cached.get("updated", 0) < DISCOVER_TTL)
|
|
if fresh and not force:
|
|
data = cached
|
|
else:
|
|
try:
|
|
data = refresh_discover(ram_gb)
|
|
except Exception: # noqa: BLE001
|
|
if cached:
|
|
data = cached # Quellen down -> alter Cache ist besser als nichts
|
|
else:
|
|
raise HTTPException(502, "Modell-Quellen gerade nicht erreichbar — später erneut versuchen.")
|
|
# Bestand + Voraussetzungen frisch dazurechnen (aendern sich unabhaengig vom Cache).
|
|
idx = _installed_index()
|
|
out = []
|
|
for c in data["categories"]:
|
|
ms = [{**m, "installed": _model_installed(m["repo"], idx),
|
|
"requirements": _model_requirements(m["role"], m.get("quant", "Q4_K_M"), m["fit"])}
|
|
for m in c["models"]]
|
|
# „Beste Wahl" je Kategorie: das lauffähige Modell mit dem besten Fit (perfect vor
|
|
# marginal), bei Gleichstand das meistgeladene. Zu große Modelle kommen nie in Frage.
|
|
ranked = sorted([m for m in ms if m["fit"]["level"] != "too_tight"],
|
|
key=lambda m: (_FIT_ORDER[m["fit"]["level"]], -int(m.get("downloads") or 0)))
|
|
out.append({**c, "models": ms, "recommended": ranked[0]["repo"] if ranked else None})
|
|
return {"updated": data["updated"], "categories": out,
|
|
"sys_ram_gb": round(ram_gb, 1), "stale": not fresh}
|
|
|
|
|
|
def compute_upgrades(ram_gb):
|
|
"""Liste relevanter Modell-Upgrades für die installierten Modelle (UPGRADES-Map)."""
|
|
installed = (read_config().get("models") or {})
|
|
out, seen = [], set()
|
|
for up in UPGRADES:
|
|
new_base = up["repo"].split("/")[-1].lower()
|
|
if any(new_base in str(s.get("cmd", "")).lower() for s in installed.values()):
|
|
continue # neueres Modell schon installiert
|
|
old = None
|
|
for alias, spec in installed.items():
|
|
hay = (alias + " " + str(spec.get("cmd", ""))).lower()
|
|
if any(k in hay for k in up["match"]):
|
|
old = alias
|
|
break
|
|
if not old or up["repo"] in seen:
|
|
continue
|
|
seen.add(up["repo"])
|
|
out.append({
|
|
"name": up["name"], "repo": up["repo"], "params_b": up["params_b"], "quant": up["quant"],
|
|
"why": up["why"], "old": old, "role": old,
|
|
"fit": evaluate_fit(up["params_b"], up["quant"], 8192, ram_gb, name=up.get("name", "")),
|
|
"optimal_ctx": max_ctx_for(up["params_b"], up["quant"], ram_gb),
|
|
})
|
|
return out
|
|
|
|
|
|
@router.get("/upgrades")
|
|
def upgrades():
|
|
return {"upgrades": compute_upgrades(psutil.virtual_memory().total / (1024 ** 3))}
|
|
|
|
|
|
@router.post("/install-model")
|
|
def install_model(req: InstallModelReq):
|
|
"""Ein einzelnes Modell installieren (Download + Einpflegen unter 'role', optimaler ctx).
|
|
Wenn 'file' übergeben wird, wird genau diese GGUF-Datei geladen (Profi-Suche-Auswahl);
|
|
andernfalls wählt _pick_gguf automatisch die beste Variante für den gewünschten Quant."""
|
|
file = req.file or _pick_gguf(req.repo, req.quant)
|
|
if not file:
|
|
raise HTTPException(404, "Keine GGUF-Datei im Repo gefunden.")
|
|
mmproj = _pick_mmproj(req.repo)
|
|
ram_gb = psutil.virtual_memory().total / (1024 ** 3)
|
|
env = dict(HF_DOWNLOAD_ENV)
|
|
if req.hf_token:
|
|
env["HF_TOKEN"] = req.hf_token
|
|
target = MODELS_DIR / req.repo.split("/")[-1]
|
|
target.mkdir(parents=True, exist_ok=True)
|
|
args = [hf_bin(), "download", req.repo, file]
|
|
if mmproj:
|
|
args.append(mmproj)
|
|
args += ["--local-dir", str(target)]
|
|
jid = start_job(args, f"download {req.repo.split('/')[-1]}", env=env)
|
|
JOBS[jid]["result_path"] = str(target / file)
|
|
attach_download_progress(jid, str(target), hf_file_size(req.repo, file))
|
|
cfg = read_config()
|
|
ctx = req.ctx if req.ctx else max_ctx_for(req.params_b, req.quant, ram_gb)
|
|
path = str(target / file)
|
|
cmd = CMD_TEMPLATE.replace("{model}", path).replace("{ctx}", str(ctx))
|
|
if mmproj:
|
|
cmd = cmd.rstrip() + f" --mmproj {target / mmproj} --jinja"
|
|
mid = model_id_from_path(path)
|
|
cfg["models"][mid] = {"cmd": LiteralScalarString(cmd + "\n"), "ttl": DEFAULT_TTL}
|
|
set_role_alias(cfg, mid, req.role)
|
|
write_config(cfg)
|
|
return {"job_id": jid, "model_id": mid}
|
|
|