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from threading import Thread | |
from typing import Iterator, List, Tuple | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
model_id = 'meta-llama/Llama-2-7b-chat-hf' | |
# model_id = 'codellama/CodeLlama-13b-Instruct-hf' | |
if torch.cuda.is_available(): | |
model = AutoModelForCausalLM.from_pretrained( | |
model_id, | |
torch_dtype=torch.float16, | |
device_map='auto', | |
cache_dir='models', | |
token="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" | |
) | |
else: | |
model = None | |
tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir='models') | |
def get_prompt(message: str, chat_history: List[Tuple[str, str]], | |
system_prompt: str) -> str: | |
texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n'] | |
# 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()} </s><s>[INST] ') | |
message = message.strip() if do_strip else message | |
texts.append(f'{message} [/INST]') | |
return ''.join(texts) | |
def get_input_token_length(message: str, chat_history: List[Tuple[str, str]], system_prompt: str) -> int: | |
prompt = get_prompt(message, chat_history, system_prompt) | |
input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['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('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) | |