Delete code

code/inference.py

@@ -1,71 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-import re
-
-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:"""
-
-def model_fn(model_dir):
-    # Load model from HuggingFace Hub
-    tokenizer = AutoTokenizer.from_pretrained(model_dir)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_dir,
-        low_cpu_mem_usage = True, 
-        trust_remote_code = False,
-        torch_dtype = torch.float16,
-    ).to('cuda')
-    return model, tokenizer
-
-def create_new_response(result, user_name):
-    result = result.rsplit("Alice Gate:", 1)[1].split(f"{user_name}:",1)[0].strip()
-    parsed_result = re.sub('\*.*?\*', '', result).strip()
-    result = parsed_result if len(parsed_result) != 0 else result.replace("*","")
-    result = " ".join(result.split())
-    try:
-        result = result[:[m.start() for m in re.finditer(r'[.!?]', result)][-1]+1]
-    except Exception: pass
-    return {
-        "message": result
-    }
-
-def predict_fn(data, model_and_tokenizer):
-    # destruct model and tokenizer
-    model, tokenizer = model_and_tokenizer
-    
-    # Tokenize sentences
-    user_name = inputs["user_name"]
-    user_input = "\n".join(inputs["user_input"])
-    prompt = template.format(
-        user_name = user_name,
-        user_input = user_input
-    )
-    input_ids = tokenizer(
-        prompt,
-        return_tensors = "pt"
-    ).to("cuda")
-    generator = 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 create_new_response(tokenizer.decode(generator[0], skip_special_tokens=True), user_name)

code/requirements.txt

@@ -1 +0,0 @@
-accelerate==0.18.0