from threading import Thread from typing import Iterator #import torch from transformers.utils import logging from ctransformers import AutoModelForCausalLM from transformers import TextIteratorStreamer, AutoTokenizer logging.set_verbosity_info() logger = logging.get_logger("transformers") config = {'max_new_tokens': 256, 'repetition_penalty': 1.1, 'temperature': 0.1, 'stream': True} model_id = 'TheBloke/Llama-2-7B-Chat-GGML' device = "cpu" model = AutoModelForCausalLM.from_pretrained(model_id, model_type="llama", lib='avx2', hf=True) tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-chat-hf') def get_prompt(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> str: logger.info("get_prompt chat_history=%s",chat_history) logger.info("get_prompt system_prompt=%s",system_prompt) texts = [f'[INST] <>\n{system_prompt}\n<>\n\n'] logger.info("texts=%s",texts) # The first user input is _not_ stripped do_strip = False for user_input, response in chat_history: user_input = user_input.strip() if do_strip else user_input do_strip = True texts.append(f'{user_input} [/INST] {response.strip()} [INST] ') message = message.strip() if do_strip else message logger.info("get_prompt message=%s",message) texts.append(f'{message} [/INST]') logger.info("get_prompt final texts=%s",texts) return ''.join(texts) def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int: logger.info("get_input_token_length=%s",message) prompt = get_prompt(message, chat_history, system_prompt) logger.info("prompt=%s",prompt) input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids'] logger.info("input_ids=%s",input_ids) return input_ids.shape[-1] def run(message: str, chat_history: list[tuple[str, str]], system_prompt: str, max_new_tokens: int = 1024, temperature: float = 0.8, top_p: float = 0.95, top_k: int = 50) -> Iterator[str]: prompt = get_prompt(message, chat_history, system_prompt) inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to(device) streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( inputs, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, top_k=top_k, temperature=temperature, num_beams=1, ) t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for text in streamer: outputs.append(text) yield ''.join(outputs)