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35c3998 | 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 | #!/usr/bin/env python3
"""Load the base model + FakeNews adapter for local inference."""
from __future__ import annotations
from peft import PeftModel
import transformers
from transformers import AutoConfig, AutoModelForCausalLM, AutoProcessor, AutoTokenizer
BASE_MODEL_ID = "Qwen/Qwen2.5-1.5B-Instruct"
ADAPTER_PATH = "adapter"
def main() -> int:
config = AutoConfig.from_pretrained(BASE_MODEL_ID)
config_name = type(config).__name__.casefold()
is_vl = any(token in config_name for token in ("vision", "vl", "multi"))
if is_vl:
model_loader = getattr(transformers, "AutoModelForImageTextToText", None)
if model_loader is None:
model_loader = getattr(transformers, "AutoModelForVision2Seq", None)
if model_loader is None:
raise RuntimeError("This transformers version does not support VL auto loaders.")
processor = AutoProcessor.from_pretrained(BASE_MODEL_ID)
tokenizer = processor.tokenizer
model = model_loader.from_pretrained(BASE_MODEL_ID)
else:
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(BASE_MODEL_ID)
model = PeftModel.from_pretrained(model, ADAPTER_PATH)
prompt = "Classify this claim as real or fake and explain: The moon is made of cheese."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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