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from threading import Thread | |
from typing import Iterator | |
import torch | |
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
model_id = 'meta-llama/Llama-2-13b-chat-hf' | |
if torch.cuda.is_available(): | |
config = AutoConfig.from_pretrained(model_id) | |
config.pretraining_tp = 1 | |
model = AutoModelForCausalLM.from_pretrained( | |
model_id, | |
config=config, | |
torch_dtype=torch.float16, | |
load_in_4bit=True, | |
device_map='auto' | |
) | |
else: | |
model = None | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
def get_prompt(message: str, chat_history: list[tuple[str, str]], | |
system_prompt: str) -> str: | |
texts = [f'[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n'] | |
for user_input, response in chat_history: | |
texts.append(f'{user_input} [/INST] {response} [INST] ') | |
texts.append(f'{message.strip()} [/INST]') | |
return ''.join(texts) | |
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').to("cuda") | |
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) | |