| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import re | |
| import torch | |
| 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. | |
| {user_name}: Definetly. | |
| <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_input} | |
| Alice Gate:""" | |
| class EndpointHandler(): | |
| def __init__(self, path = ""): | |
| self.tokenizer = AutoTokenizer.from_pretrained(path) | |
| self.model = AutoModelForCausalLM.from_pretrained( | |
| path, | |
| low_cpu_mem_usage = True, | |
| trust_remote_code = False, | |
| torch_dtype = torch.float16, | |
| ).to('cuda') | |
| def response(self, result, user_name): | |
| result = result.rsplit("Alice Gate:", 1)[1].split(f"{user_name}:",1)[0].strip() | |
| try: | |
| result = result[:[m.start() for m in re.finditer(r'[.!?]', result)][-1]+1] | |
| except Exception: pass | |
| return { | |
| "message": result | |
| } | |
| def __call__(self, data): | |
| inputs = data.pop("inputs", data) | |
| user_name = inputs["user_name"] | |
| user_input = "\n".join(inputs["user_input"]) | |
| input_ids = self.tokenizer( | |
| template.format( | |
| user_name = user_name, | |
| user_input = user_input | |
| ), | |
| return_tensors = "pt" | |
| ).to("cuda") | |
| generator = self.model.generate( | |
| input_ids["input_ids"], | |
| max_new_tokens = 50, | |
| temperature = 0.5, | |
| top_p = 0.9, | |
| top_k = 0, | |
| repetition_penalty = 1.1, | |
| pad_token_id = 50256, | |
| num_return_sequences = 1 | |
| ) | |
| return self.response(self.tokenizer.decode(generator[0], skip_special_tokens=True), user_name) |