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import torch |
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import logging |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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MODEL_NAME = "evilfreelancer/PavelGPT-7B-128K-v0.1" |
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DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n" |
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DEFAULT_SYSTEM_PROMPT = """Ты — PavelGPT, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.""" |
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class Conversation: |
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def __init__( |
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self, |
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message_template=DEFAULT_MESSAGE_TEMPLATE, |
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system_prompt=DEFAULT_SYSTEM_PROMPT, |
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start_token_id=2, |
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bot_token_id=10093, |
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history_limit=None, |
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): |
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self.logger = logging.getLogger('Conversation') |
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self.message_template = message_template |
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self.start_token_id = start_token_id |
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self.bot_token_id = bot_token_id |
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self.history_limit = history_limit |
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self.messages = [{ |
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"role": "system", |
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"content": system_prompt |
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}] |
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def get_start_token_id(self): |
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return self.start_token_id |
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def get_bot_token_id(self): |
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return self.bot_token_id |
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def add_message(self, role, message): |
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self.messages.append({ |
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"role": role, |
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"content": message |
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}) |
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self.trim_history() |
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def add_user_message(self, message): |
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self.add_message("user", message) |
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def add_bot_message(self, message): |
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self.add_message("assistant", message) |
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def trim_history(self): |
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if self.history_limit is not None and len(self.messages) > self.history_limit + 1: |
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overflow = len(self.messages) - (self.history_limit + 1) |
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self.messages = [self.messages[0]] + self.messages[overflow + 1:] |
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def get_prompt(self, tokenizer): |
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final_text = "" |
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for message in self.messages: |
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message_text = self.message_template.format(**message) |
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final_text += message_text |
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if isinstance(self.bot_token_id, (list, tuple)): |
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final_text += tokenizer.decode([self.start_token_id] + self.bot_token_id) |
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else: |
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final_text += tokenizer.decode([self.start_token_id, self.bot_token_id]) |
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return final_text.strip() |
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def generate(model, tokenizer, prompt, generation_config): |
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data = tokenizer(prompt, return_tensors="pt") |
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data = {k: v.to(model.device) for k, v in data.items()} |
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output_ids = model.generate( |
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**data, |
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max_length=10240, |
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generation_config=generation_config |
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)[0] |
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output_ids = output_ids[len(data["input_ids"][0]):] |
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output = tokenizer.decode(output_ids, skip_special_tokens=True) |
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return output.strip() |
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config = PeftConfig.from_pretrained(MODEL_NAME) |
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model = AutoModelForCausalLM.from_pretrained( |
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config.base_model_name_or_path, |
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load_in_8bit=True, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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use_flash_attention_2=True, |
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) |
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model = PeftModel.from_pretrained( |
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model, |
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MODEL_NAME, |
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torch_dtype=torch.float16 |
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) |
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model.eval() |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False) |
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generation_config = GenerationConfig.from_pretrained(MODEL_NAME) |
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print(generation_config) |
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conversation = Conversation() |
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while True: |
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user_message = input("User: ") |
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if user_message.strip() == "/reset": |
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conversation = Conversation() |
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print("History reset completed!") |
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continue |
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if user_message.strip() == "": |
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continue |
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conversation.add_user_message(user_message) |
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prompt = conversation.get_prompt(tokenizer) |
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output = generate( |
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model=model, |
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tokenizer=tokenizer, |
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prompt=prompt, |
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generation_config=generation_config |
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) |
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conversation.add_bot_message(output) |
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print("Bot:", output) |
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print() |
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print("==============================") |
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print() |
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