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from threading import Thread
from typing import Iterator
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
model_id = "meta-llama/Llama-2-7b-chat-hf"
if torch.cuda.is_available():
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"<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=10.0, 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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