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9f7b0e1 cb64216 9f7b0e1 cb64216 9f7b0e1 cb64216 9f7b0e1 cb64216 9f7b0e1 cb64216 9f7b0e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import os
import time
import httpx
HF_API_TOKEN = (os.environ.get("HF_API_TOKEN") or os.environ.get("HUGGINGFACE_API_TOKEN") or "").strip()
ROUTER_MODELS_URL = "https://router.huggingface.co/v1/models"
PREFERRED_MODELS = [
{"id": "meta-llama/Llama-3.1-8B-Instruct", "name": "Llama 3.1 8B Instruct", "size": "medium", "description": "Fast general-purpose instruct model"},
{"id": "Qwen/Qwen2.5-7B-Instruct", "name": "Qwen 2.5 7B Instruct", "size": "medium", "description": "Reliable JSON-following instruction model"},
{"id": "meta-llama/Meta-Llama-3-8B-Instruct", "name": "Meta Llama 3 8B Instruct", "size": "medium", "description": "Strong general chat behavior"},
{"id": "google/gemma-3n-E4B-it", "name": "Gemma 3n E4B", "size": "small", "description": "Lightweight instruction-tuned Gemma model"},
{"id": "Sao10K/L3-8B-Stheno-v3.2", "name": "L3 8B Stheno v3.2", "size": "medium", "description": "Creative 8B chat model"},
{"id": "XiaomiMiMo/MiMo-V2-Flash", "name": "MiMo V2 Flash", "size": "medium", "description": "Fast flash-tier chat model"},
{"id": "google/gemma-4-26B-A4B-it", "name": "Gemma 4 26B A4B", "size": "large", "description": "Higher-capacity Gemma instruct model"},
{"id": "google/gemma-4-31B-it", "name": "Gemma 4 31B", "size": "large", "description": "Large Gemma chat model"},
{"id": "Qwen/Qwen3.5-35B-A3B", "name": "Qwen 3.5 35B A3B", "size": "large", "description": "Large Qwen instruction model"},
{"id": "google/gemma-3-27b-it", "name": "Gemma 3 27B", "size": "large", "description": "Large Gemma 3 instruct model"},
{"id": "moonshotai/Kimi-K2.5", "name": "Kimi K2.5", "size": "large", "description": "Large reasoning-oriented chat model"},
{"id": "Qwen/Qwen3-Coder-30B-A3B-Instruct", "name": "Qwen 3 Coder 30B", "size": "large", "description": "Coder-tuned instruction model"},
{"id": "meta-llama/Llama-3.3-70B-Instruct", "name": "Llama 3.3 70B Instruct", "size": "xl", "description": "Large instruction-following flagship model"},
]
_CACHE = {"expires_at": 0.0, "ids": None}
def _headers() -> dict[str, str]:
if not HF_API_TOKEN:
return {}
return {"Authorization": f"Bearer {HF_API_TOKEN}"}
def _extract_router_models(payload) -> list[dict]:
if isinstance(payload, list):
return [item for item in payload if isinstance(item, dict)]
if isinstance(payload, dict):
data = payload.get("data")
if isinstance(data, list):
return [item for item in data if isinstance(item, dict)]
return []
async def _fetch_router_model_ids() -> set[str] | None:
now = time.monotonic()
cached_ids = _CACHE["ids"]
if isinstance(cached_ids, set) and _CACHE["expires_at"] > now:
return cached_ids
if not HF_API_TOKEN:
return None
try:
async with httpx.AsyncClient(timeout=15.0) as client:
response = await client.get(ROUTER_MODELS_URL, headers=_headers())
response.raise_for_status()
payload = response.json()
except Exception:
return None
models = _extract_router_models(payload)
ids = {item["id"] for item in models if isinstance(item.get("id"), str)}
_CACHE["ids"] = ids
_CACHE["expires_at"] = now + 300
return ids
def get_supported_model_ids() -> set[str]:
return {model["id"] for model in PREFERRED_MODELS}
def is_supported_model(model_id: str) -> bool:
return model_id in get_supported_model_ids()
def get_default_model_id() -> str:
return PREFERRED_MODELS[0]["id"]
async def get_available_models() -> dict:
live_ids = await _fetch_router_model_ids()
if live_ids:
models = [model for model in PREFERRED_MODELS if model["id"] in live_ids]
else:
models = list(PREFERRED_MODELS)
return {"models": models, "total": len(models)}
def get_model_display_name(model_id: str) -> str:
for model in PREFERRED_MODELS:
if model["id"] == model_id:
return model["name"]
return model_id.split("/")[-1].split("-")[0].capitalize()
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