llama_llm.py 提示词修改
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@ -74,7 +74,7 @@ llm_model_dict = {
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"vicuna-13b-hf": {
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"name": "vicuna-13b-hf",
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"pretrained_model_name": "vicuna-13b-hf",
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"local_model_path": "/media/checkpoint/vicuna-13b-hf",
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"local_model_path": None,
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"provides": "LLamaLLM"
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},
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@ -98,9 +98,10 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
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"""
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formatted_history = ''
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history = history[-self.history_len:] if self.history_len > 0 else []
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for i, (old_query, response) in enumerate(history):
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formatted_history += "[Round {}]\n问:{}\n答:{}\n".format(i, old_query, response)
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formatted_history += "[Round {}]\n问:{}\n答:".format(len(history), query)
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if len(history) > 0:
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for i, (old_query, response) in enumerate(history):
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formatted_history += "### Human:{}\n### Assistant:{}\n".format(old_query, response)
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formatted_history += "### Human:{}\n### Assistant:".format(query)
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return formatted_history
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def prepare_inputs_for_generation(self,
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@ -140,12 +141,13 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
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"max_new_tokens": self.max_new_tokens,
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"num_beams": self.num_beams,
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"top_p": self.top_p,
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"do_sample": True,
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"top_k": self.top_k,
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"repetition_penalty": self.repetition_penalty,
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"encoder_repetition_penalty": self.encoder_repetition_penalty,
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"min_length": self.min_length,
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"temperature": self.temperature,
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"eos_token_id": self.eos_token_id,
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"eos_token_id": self.checkPoint.tokenizer.eos_token_id,
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"logits_processor": self.logits_processor}
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# 向量转换
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@ -178,6 +180,6 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
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response = self._call(prompt=softprompt, stop=['\n###'])
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answer_result = AnswerResult()
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answer_result.history = history + [[None, response]]
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answer_result.history = history + [[prompt, response]]
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answer_result.llm_output = {"answer": response}
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yield answer_result
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@ -75,8 +75,8 @@ class MOSSLLM(BaseAnswer, LLM, ABC):
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repetition_penalty=1.02,
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num_return_sequences=1,
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eos_token_id=106068,
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pad_token_id=self.tokenizer.pad_token_id)
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response = self.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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pad_token_id=self.checkPoint.tokenizer.pad_token_id)
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response = self.checkPoint.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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self.checkPoint.clear_torch_cache()
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history += [[prompt, response]]
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answer_result = AnswerResult()
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