README.md

---
library_name: peft
language:
- pt
---
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16

## Algoritmo para utilização do modelo

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM,BitsAndBytesConfig,AutoTokenizer

#Quantização
use_4bit = True 
bnb_4bit_compute_dtype = "float16" 
bnb_4bit_quant_type = "nf4" 
use_nested_quant = False


# Carrega o tokenizer e modelo com configuração QLoRA
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)

bnb_config = BitsAndBytesConfig(load_in_4bit = use_4bit,
                                bnb_4bit_quant_type = bnb_4bit_quant_type,
                                bnb_4bit_compute_dtype = compute_dtype,
                                bnb_4bit_use_double_quant = use_nested_quant)


#Import do modelo
config = PeftConfig.from_pretrained("MatNLP/Sectrum")
base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf",quantization_config = bnb_config)
model = PeftModel.from_pretrained(base_model, "MatNLP/Sectrum")


# Carrega o tokenizador
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf", trust_remote_code = True,skip_special_tokens=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"


# Prepara o prompt
prompt = "Como proteger meu e-mail?"

# Cria o pipeline
pipe = pipeline(task = "text-generation",
                model = model,
                tokenizer = tokenizer,
                max_length = 200)
                #streamer=TextStreamer(tokenizer,skip_prompt=True)


# Executa o pipeline e gera o texto a partir do prompt inicial
resultado = pipe(f"<s>[INST] {prompt} [/INST]")


print(resultado[0]['generated_text'].split("[/INST]")[1].split('<\s>')[0])