<jupyter_start><jupyter_code>!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q git+https://github.com/huggingface/peft.git
!pip install -q git+https://github.com/huggingface/accelerate.git@main
!pip install huggingface_hub
!pip install bitsandbytes
!pip install SentencePiece
import os

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from huggingface_hub import notebook_login
import torch

notebook_login()
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig, BitsAndBytesConfig

model_name = "decapoda-research/llama-7b-hf"
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name, quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map="auto", use_auth_token=True)
%%time
model = PeftModel.from_pretrained(model, "tloen/alpaca-lora-7b", adapter_name="eng_alpaca")
%%time
model.load_adapter("22h/cabrita-lora-v0-1", adapter_name="portuguese_alpaca")
model
model.to("cuda")
import torch

device = "cuda"


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.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""


def evaluate(
    instruction,
    input=None,
    temperature=0.1,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=256,
    **kwargs,
):
    prompt = generate_prompt(instruction, input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to(device)
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        no_repeat_ngram_size=3,
        **kwargs,
    )

    with torch.no_grad():
        generation_output = model.generate(
            input_ids=input_ids,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens,
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return output.split("### Response:")[1].strip()
%%time
model.set_adapter("eng_alpaca")
instruction = "Tell me about alpacas."

print(evaluate(instruction))
%%time
model.set_adapter("portuguese_alpaca")
instruction = "Invente uma desculpa criativa pra dizer que não preciso ir à festa."

print(evaluate(instruction))
with model.disable_adapter():
    instruction = "Invente uma desculpa criativa pra dizer que não preciso ir à festa."

    print(evaluate(instruction))<jupyter_output>I'm sorry, but I can't go to the party. I'm sick. I have a cold. I don't feel well. I need to stay at home and rest.
I have a lot of homework to do. My dog ate my homework. My homework is too hard. I didn't have time to do it. It's too late. I forgot about it.
My parents won't let me go. My parents are out of town. They're on vacation. They have to work. They are sick. They need to take care of my brother.
They're not home. They went to the grocery store. They took the car to the mechanic. They had to go to a meeting. They were in a hurry. They forgot about me.
Their car broke down. Their car ran out of gas. They got a flat tire. They couldn't find a parking space. They didn' t have enough money. They lost their wallet.
It's raining. The roads are icy. There's a blizzard. There are too many cars on the road. There was an accident.