app.py

import torch
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-13b-hf")
model = LlamaForCausalLM.from_pretrained(
        "decapoda-research/llama-13b-hf",
        load_in_8bit=True,
        torch_dtype=torch.float16,
        device_map="auto",
)
model = PeftModel.from_pretrained(
    model, "baruga/alpaca-lora-13b",
    torch_dtype=torch.float16
)

def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. Answer step by step.

### Instruction:
{instruction}

### Input:
{input}

### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. Answer step by step.

### Instruction:
{instruction}

### Response:"""

generation_config = GenerationConfig(
    temperature=0.1,
    top_p=0.75,
    num_beams=4,
)

def evaluate(instruction, input=None):
    prompt = generate_prompt(instruction, input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256
    )
    for s in generation_output.sequences:
        output = tokenizer.decode(s)
        print("Response:", output.split("### Response:")[1].strip())