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from threading import Thread
from typing import Iterator
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
# Original version
# model_id = "LinkSoul/Chinese-Llama-2-7b"
# 4 bit version
model_id = "LinkSoul/Chinese-Llama-2-7b-4bit"
if torch.cuda.is_available():
if model_id.endswith("4bit"):
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
local_files_only=True,
torch_dtype=torch.float16
)
else:
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
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.strip()} [/INST] {response.strip()} </s><s> [INST] ')
texts.append(f'{message.strip()} [/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')['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').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)
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