Model based on https://github.com/22-hours/cabrita

Install dependencies
```
!pip install -q datasets loralib sentencepiece
!pip uninstall transformers -y
!pip install git+https://github.com/huggingface/transformers.git
!pip -q install git+https://github.com/huggingface/peft.git
!pip -q install bitsandbytes
```

Import
```
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
import textwrap
```

Define model
```
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")

model = LlamaForCausalLM.from_pretrained(
    "decapoda-research/llama-7b-hf",
    load_in_8bit=True,
    device_map="auto",
)

model = PeftModel.from_pretrained(model, "berchielli/cabrita-7b-pt-br")
```

Use the model for inferences
```
generation_config = GenerationConfig(
    temperature=0.9,
    top_p=0.75,
    num_beams=4,
)

prompt = 

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
)
```