File size: 2,070 Bytes
4530879 c614af5 4530879 c614af5 4530879 c614af5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
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])
|