|
|
| from peft import LoraConfig, get_peft_model, PeftModel
|
| from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
|
| class MyModel():
|
| def __init__(self):
|
| model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
|
| lora_path = "DS_RL_model"
|
| self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| model = AutoModelForCausalLM.from_pretrained(model_name)
|
| self.model = PeftModel.from_pretrained(model, lora_path)
|
| self.generation_config = {
|
| "max_new_tokens": 2048,
|
| "temperature": 0.9,
|
| "top_p": 1.0,
|
| "repetition_penalty": 1.2,
|
| }
|
| def predict(self, text):
|
| 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
|
| input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.model.device)
|
| outputs = self.model.generate(input_ids, **self.generation_config)
|
| decoded = self.tokenizer.decode(outputs[0], skip_special_tokens=False)
|
| return decoded
|
| |
