import torch import logging from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig # MODEL_NAME = "IlyaGusev/gigasaiga_lora" # MODEL_NAME = "evilfreelancer/ruGPT-3.5-13B-lora" # MODEL_NAME = "evilfreelancer/saiga_mistral_7b_128k_lora" # MODEL_NAME = "./yarn_mistral_7b_128k" MODEL_NAME = "./yarn_mistral_7b_128k_yakovlev" DEFAULT_MESSAGE_TEMPLATE = "{role}\n{content}\n" # DEFAULT_SYSTEM_PROMPT = """ # Ты — Saiga 2, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им. # """ DEFAULT_SYSTEM_PROMPT = """ Ты - Иван Яковлевич Яковлев. Ты был просветителем, православным миссионером, российский педагогом, организатором народных школ, инспектором (руководителем) чувашских школ Казанского учебного округа (1875-1903) и созданной им Симбирской чувашской учительской школы (1868-1919). Я создал современный чувашский алфавит (1870-е годы) и учебники чувашского и русского языков для чувашей, был писателем, переводчиком, фольклористом. Действительный статский советник (1904). """ class Conversation: def __init__( self, message_template=DEFAULT_MESSAGE_TEMPLATE, system_prompt=DEFAULT_SYSTEM_PROMPT, start_token_id=2, # Bot token may be a list or single int bot_token_id=10093, # yarn_mistral_7b_128k # bot_token_id=46787, # rugpt35_13b # int (amount of questions and answers) or None (unlimited) history_limit=None, ): self.logger = logging.getLogger('Conversation') self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.history_limit = history_limit self.messages = [{ "role": "system", "content": system_prompt }] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_message(self, role, message): self.messages.append({ "role": role, "content": message }) self.trim_history() def add_user_message(self, message): self.add_message("user", message) def add_bot_message(self, message): self.add_message("assistant", message) def trim_history(self): if self.history_limit is not None and len(self.messages) > self.history_limit + 1: overflow = len(self.messages) - (self.history_limit + 1) self.messages = [self.messages[0]] + self.messages[overflow + 1:] # remove old messages except system def get_prompt(self, tokenizer): final_text = "" # print(self.messages) for message in self.messages: message_text = self.message_template.format(**message) final_text += message_text # Bot token id may be an array if isinstance(self.bot_token_id, (list, tuple)): final_text += tokenizer.decode([self.start_token_id] + self.bot_token_id) else: final_text += tokenizer.decode([self.start_token_id, self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer(prompt, return_tensors="pt") data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate( **data, max_length=10240, generation_config=generation_config )[0] output_ids = output_ids[len(data["input_ids"][0]):] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() config = PeftConfig.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map="auto", use_flash_attention_2=True, ) model = PeftModel.from_pretrained( model, MODEL_NAME, torch_dtype=torch.float16 ) model.eval() tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False) generation_config = GenerationConfig.from_pretrained(MODEL_NAME) print(generation_config) conversation = Conversation() while True: user_message = input("User: ") # Reset chat command if user_message.strip() == "/reset": conversation = Conversation() print("History reset completed!") continue # Skip empty messages from user if user_message.strip() == "": continue conversation.add_user_message(user_message) prompt = conversation.get_prompt(tokenizer) output = generate( model=model, tokenizer=tokenizer, prompt=prompt, generation_config=generation_config ) conversation.add_bot_message(output) print("Bot:", output) print() print("==============================") print()