| --- |
| 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]) |
|
|
