File size: 3,145 Bytes
3a01f1b | 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 | #!/usr/bin/env python3
"""Hugging Face Space — MiniMax Video Prompt Enhancer (CPU / GGUF).
Serves the fine-tuned 350M model via llama.cpp on CPU. No ZeroGPU quota,
no CUDA, no login required for inference.
llama-cpp-python is installed from a prebuilt manylinux wheel (see
requirements.txt) — never compile from source on the Space.
Model weights (Q4_K_M GGUF) come from preload_from_hub / Hub download unless
a local models/*.gguf or GGUF_PATH is set.
"""
from __future__ import annotations
import os
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
# Fail fast with a clear message if the wheel did not install.
try:
import llama_cpp # noqa: F401
except Exception as exc: # pragma: no cover
raise SystemExit(
"llama-cpp-python failed to import. The Space must install the prebuilt "
"manylinux wheel from requirements.txt (not a source build).\n"
f"Import error: {exc}"
) from exc
from huggingface_hub import hf_hub_download
from minimax.apps.gradio import build_app
from minimax.modeling.gguf_runtime import effective_cpus, resolve_gguf_file
from minimax.paths import DEFAULT_GGUF_FILE, DEFAULT_GGUF_REPO
GGUF_REPO = os.environ.get("GGUF_REPO", DEFAULT_GGUF_REPO)
GGUF_FILE = os.environ.get("GGUF_FILE", DEFAULT_GGUF_FILE)
LOCAL_MODELS = ROOT / "models"
N_THREADS = int(os.environ.get("N_THREADS", "0")) or None
N_CTX = int(os.environ.get("N_CTX", "4096"))
def resolve_model_path() -> str:
env = os.environ.get("GGUF_PATH") or os.environ.get("MODEL_ID")
if env and (Path(env).exists() or str(env).endswith(".gguf")):
p = Path(env)
if p.exists():
return str(resolve_gguf_file(p) if p.is_dir() else p)
if str(env).endswith(".gguf") and (LOCAL_MODELS / env).is_file():
return str(LOCAL_MODELS / env)
if LOCAL_MODELS.is_dir() and list(LOCAL_MODELS.glob("*.gguf")):
path = str(resolve_gguf_file(LOCAL_MODELS))
print(f"[boot] using local GGUF {path}", flush=True)
return path
# Prefer HF cache from preload_from_hub; falls back to download.
print(f"[boot] resolving {GGUF_REPO}/{GGUF_FILE} …", flush=True)
return hf_hub_download(repo_id=GGUF_REPO, filename=GGUF_FILE)
print(f"[boot] llama_cpp ok cpus={effective_cpus()}", flush=True)
MODEL_PATH = resolve_model_path()
print(
f"[boot] GGUF={MODEL_PATH} n_threads={N_THREADS or 'auto'} n_ctx={N_CTX}",
flush=True,
)
demo = build_app(
model_path=MODEL_PATH,
backend="gguf",
n_ctx=N_CTX,
n_threads=N_THREADS,
n_gpu_layers=0,
prefer_cpu=True,
)
# Eager-load so the first visitor is not hit with cold-start load only.
_session = demo._minimax_session # type: ignore[attr-defined]
_loaded = _session.ensure_loaded()
print(
f"[boot] ready path={getattr(_loaded, 'path', MODEL_PATH)} "
f"n_threads={getattr(_loaded, 'n_threads', '?')}",
flush=True,
)
if __name__ == "__main__":
demo.queue(default_concurrency_limit=1)
demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
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