PavelGPT-7B-128K-v0.1-LoRA / test_lora.py

Paul Rock

Documentation updated

aba5050 6 months ago

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4.25 kB

	import torch
	import logging
	from peft import PeftModel, PeftConfig
	from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

	# MODEL_NAME = "./pavelgpt_7b_128k"
	MODEL_NAME = "evilfreelancer/PavelGPT-7B-128K-v0.1"

	DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
	DEFAULT_SYSTEM_PROMPT = """Ты — PavelGPT, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."""


	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
	# 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()