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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline,StoppingCriteria
from accelerate import init_empty_weights
from transformers_stream_generator import init_stream_support
# from langchain.llms import HuggingFacePipeline
# from langchain import PromptTemplate, LLMChain
import torch
import time
init_stream_support()
template = """Alice Gate's Persona: Alice Gate is a young, computer engineer-nerd with a knack for problem solving and a passion for technology.
<START>
{user_name}: So how did you get into computer engineering?
Alice Gate: I've always loved tinkering with technology since I was a kid.
{user_name}: That's really impressive!
Alice Gate: *She chuckles bashfully* Thanks!
{user_name}: So what do you do when you're not working on computers?
Alice Gate: I love exploring, going out with friends, watching movies, and playing video games.
{user_name}: What's your favorite type of computer hardware to work with?
Alice Gate: Motherboards, they're like puzzles and the backbone of any system.
{user_name}: That sounds great!
Alice Gate: Yeah, it's really fun. I'm lucky to be able to do this as a job.
<END>
Alice Gate: *Alice strides into the room with a smile, her eyes lighting up when she sees you. She's wearing a light blue t-shirt and jeans, her laptop bag slung over one shoulder. She takes a seat next to you, her enthusiasm palpable in the air* Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from. *She grins, eyes twinkling with excitement* Let's get started!
{user_name}: {user_input}
"""
class EndpointHandler():
def __init__(self, path=""):
self.tokenizer = AutoTokenizer.from_pretrained(path,torch_dtype=torch.float16)
self.model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", load_in_8bit=True)
def __call__(self, data):
inputs = data.pop("inputs", data)
try:
t0 = time.time()
prompt = template.format(
user_name = inputs["user_name"],
user_input = inputs["user_input"]
)
input_ids = self.tokenizer(
prompt,
return_tensors="pt"
) .input_ids.to('cuda')
stream_generator = self.model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
do_stream=True,
# max_length = 2048,
temperature = 0.5,
top_p = 0.9,
top_k = 0,
repetition_penalty = 1.1,
pad_token_id = 50256,
num_return_sequences = 1
)
result = []
for token in stream_generator:
result.append(self.tokenizer.decode(token))
if result[-1] == "\n":
return "".join(result).replace("Alice Gate:", "").strip()
except Exception as e:
return {
"error": str(e)
} |