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