import re from pprint import pprint from transformers import AutoTokenizer from constants.models import AVAILABLE_MODELS, MODEL_MAP from tclogger import logger class MessageComposer: def __init__(self, model: str = None): if model in AVAILABLE_MODELS: self.model = model else: self.model = "mixtral-8x7b" self.model_fullname = MODEL_MAP[self.model] self.system_roles = ["system"] self.inst_roles = ["user", "system", "inst"] self.answer_roles = ["assistant", "bot", "answer", "model"] self.default_role = "user" def concat_messages_by_role(self, messages): def is_same_role(role1, role2): if ( (role1 == role2) or (role1 in self.inst_roles and role2 in self.inst_roles) or (role1 in self.answer_roles and role2 in self.answer_roles) ): return True else: return False concat_messages = [] for message in messages: role = message["role"] content = message["content"] if concat_messages and is_same_role(role, concat_messages[-1]["role"]): concat_messages[-1]["content"] += "\n" + content else: if role in self.inst_roles: message["role"] = "inst" elif role in self.answer_roles: message["role"] = "answer" else: message["role"] = "inst" concat_messages.append(message) return concat_messages def merge(self, messages) -> str: # Templates for Chat Models # - https://huggingface.co/docs/transformers/main/en/chat_templating # - https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format # - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO#prompt-format # - https://huggingface.co/openchat/openchat-3.5-0106 # - https://huggingface.co/google/gemma-7b-it#chat-template # Mistral and Mixtral: # [INST] Instruction [/INST] Model answer [INST] Follow-up instruction [/INST] # Nous Mixtral: # <|im_start|>system # You are "Hermes 2".<|im_end|> # <|im_start|>user # Hello, who are you?<|im_end|> # <|im_start|>assistant # OpenChat: # GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant: # Google Gemma-it # user # How does the brain work? # model self.messages = messages self.merged_str = "" # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format if self.model in ["mixtral-8x7b", "mistral-7b"]: self.messages = self.concat_messages_by_role(messages) self.cached_str = "" for message in self.messages: role = message["role"] content = message["content"] if role in self.inst_roles: self.cached_str = f"[INST] {content} [/INST]" elif role in self.answer_roles: self.merged_str += f" {self.cached_str} {content} \n" self.cached_str = "" else: self.cached_str = f"[INST] {content} [/INST]" if self.cached_str: self.merged_str += f"{self.cached_str}" # https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO#prompt-format elif self.model in ["nous-mixtral-8x7b"]: self.merged_str_list = [] for message in self.messages: role = message["role"] content = message["content"] if role not in ["system", "user", "assistant"]: role = self.default_role message_line = f"<|im_start|>{role}\n{content}<|im_end|>" self.merged_str_list.append(message_line) self.merged_str_list.append("<|im_start|>assistant") self.merged_str = "\n".join(self.merged_str_list) # https://huggingface.co/openchat/openchat-3.5-0106 elif self.model in ["openchat-3.5"]: self.messages = self.concat_messages_by_role(messages) self.merged_str_list = [] self.end_of_turn = "<|end_of_turn|>" for message in self.messages: role = message["role"] content = message["content"] if role in self.inst_roles: self.merged_str_list.append( f"GPT4 Correct User:\n{content}{self.end_of_turn}" ) elif role in self.answer_roles: self.merged_str_list.append( f"GPT4 Correct Assistant:\n{content}{self.end_of_turn}" ) else: self.merged_str_list.append( f"GPT4 Correct User: {content}{self.end_of_turn}" ) self.merged_str_list.append(f"GPT4 Correct Assistant:\n") self.merged_str = "\n".join(self.merged_str_list) # https://huggingface.co/google/gemma-1.1-7b-it#chat-template elif self.model in ["gemma-7b"]: self.messages = self.concat_messages_by_role(messages) self.merged_str_list = [] self.end_of_turn = "" self.start_of_turn = "" for message in self.messages: role = message["role"] content = message["content"] if role in self.inst_roles: self.merged_str_list.append( f"{self.start_of_turn}user\n{content}{self.end_of_turn}" ) elif role in self.answer_roles: self.merged_str_list.append( f"{self.start_of_turn}model\n{content}{self.end_of_turn}" ) else: self.merged_str_list.append( f"{self.start_of_turn}user\n{content}{self.end_of_turn}" ) self.merged_str_list.append(f"{self.start_of_turn}model\n") self.merged_str = "" + "\n".join(self.merged_str_list) # https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO#prompt-format # https://huggingface.co/openchat/openchat-3.5-0106 # elif self.model in ["openchat-3.5", "nous-mixtral-8x7b"]: elif self.model in ["openchat-3.5", "command-r-plus", "gemma-7b"]: tokenizer = AutoTokenizer.from_pretrained(self.model_fullname) self.merged_str = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) else: self.merged_str = "\n\n".join( [f"{message['role']}: {message['content']}" for message in messages] ) return self.merged_str def decompose_to_system_and_input_prompt( self, messages: list[dict], append_assistant=True ): system_prompt_list = [] user_and_assistant_messages = [] for message in messages: role = message["role"] content = message["content"] if role in self.system_roles: system_prompt_list.append(content) else: user_and_assistant_messages.append(message) system_prompt = "\n".join(system_prompt_list) input_prompt_list = [] input_messages = self.concat_messages_by_role(user_and_assistant_messages) for message in input_messages: role = message["role"] content = message["content"] if role in self.answer_roles: role_content_str = f"`assistant`:\n{content}" else: role_content_str = f"`user`:\n{content}" input_prompt_list.append(role_content_str) input_prompt = "\n\n".join(input_prompt_list) if append_assistant: input_prompt += "\n\n`assistant`:" return system_prompt, input_prompt if __name__ == "__main__": # model = "mixtral-8x7b" # model = "nous-mixtral-8x7b" model = "gemma-7b" # model = "openchat-3.5" # model = "command-r-plus" composer = MessageComposer(model) messages = [ { "role": "system", "content": "You are a LLM developed by OpenAI.\nYour name is GPT-4.", }, {"role": "user", "content": "Hello, who are you?"}, {"role": "assistant", "content": "I am a bot."}, {"role": "user", "content": "What is your name?"}, # {"role": "assistant", "content": "My name is Bing."}, # {"role": "user", "content": "Tell me a joke."}, # {"role": "assistant", "content": "What is a robot's favorite type of music?"}, # { # "role": "user", # "content": "How many questions have I asked? Please list them.", # }, ] # logger.note(f"model: {composer.model}") # merged_str = composer.merge(messages) # logger.note("merged_str:") # logger.mesg(merged_str) system_prompt, input_prompt = composer.decompose_to_system_and_input_prompt( messages ) logger.note("system_prompt:") logger.mesg(system_prompt) logger.note("input_prompt:") logger.mesg(input_prompt) # python -m messagers.message_composer