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from peft import LoraConfig, get_peft_model, PeftModel
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from transformers import AutoTokenizer, AutoModelForCausalLM
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class MyModel():
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def __init__(self):
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model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
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lora_path = "DS_RL_model"
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self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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self.model = PeftModel.from_pretrained(model, lora_path)
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self.generation_config = {
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"max_new_tokens": 2048,
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"temperature": 0.9,
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"top_p": 1.0,
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"repetition_penalty": 1.2,
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}
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def predict(self, text):
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prompt = "根据以下关键词生成一首歌词，歌词中包含多个句子，句子与句子之间使用/隔开，让我们一步一步的思考（思考过程包含在<think>和</think>之间）：" + text
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input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.model.device)
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outputs = self.model.generate(input_ids, **self.generation_config)
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decoded = self.tokenizer.decode(outputs[0], skip_special_tokens=False)
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return decoded
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