---
library_name: peft
tags:
- text-generation
inference: true
widget:
- text: 'mask all personally identificable information PII including person names
    for the text below: John Doe, currently lives at 1234 Elm Street, Springfield,
    Anywhere 12345.  He can be reached at johndoe@email.com or at the phone number
    555-123-4567. His social security number is 123-45-6789, and he has a bank account
    number 9876543210 at Springfield Bank. John attended Springfield University where
    he earned  a Bachelor''s degree in Computer Science. He now works at Acme Corp
    and his employee ID is 123456. John''s medical record number is MRN-001234, and
    he has a history of asthma and high blood pressure. His primary care physician
    is Dr. Jane Smith,  who practices at Springfield Medical Center. His recent blood
    test results show a cholesterol level of 200 mg/dL and a  blood glucose level
    of 90 mg/dL.

    '
base_model: meta-llama/Llama-2-7b-hf
---
install the required packages : peft, transformers, BitsAndBytes , accelerate 

```python

import transformers
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from torch import cuda, bfloat16

base_model_id = 'meta-llama/Llama-2-7b-hf'

device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)


hf_auth = "hf_your-huggingface-access-token"
model_config = transformers.AutoConfig.from_pretrained(
    base_model_id,
    use_auth_token=hf_auth
)

model = transformers.AutoModelForCausalLM.from_pretrained(
    base_model_id,
    trust_remote_code=True,
    config=model_config,
    quantization_config=bnb_config,
    device_map='auto',
    use_auth_token=hf_auth
)

config = PeftConfig.from_pretrained("Ashishkr/PII-Masking")
model = PeftModel.from_pretrained(model, "Ashishkr/PII-Masking").to(device)

model.eval()
print(f"Model loaded on {device}")

tokenizer = transformers.AutoTokenizer.from_pretrained(
    base_model_id,
    use_auth_token=hf_auth
)

```


```python
def remove_pii_info(
    model: AutoModelForCausalLM,
    tokenizer: AutoTokenizer,
    prompt: str,
    max_new_tokens: int = 128,
    temperature: float = 0.92):

    inputs = tokenizer(
        [prompt],
        return_tensors="pt",
        return_token_type_ids=False).to(device)

    max_new_tokens = inputs["input_ids"].shape[1]

    # Check if bfloat16 is supported, otherwise use float16
    dtype_to_use = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16

    with torch.autocast("cuda", dtype=dtype_to_use):
        response = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            return_dict_in_generate=True,
            eos_token_id=tokenizer.eos_token_id,
            pad_token_id=tokenizer.pad_token_id,
        )

    decoded_output = tokenizer.decode(
        response["sequences"][0],
        skip_special_tokens=True,
    )

    return decoded_output[len(prompt) :]

prompt = """
 Input: "John Doe, currently lives at 1234 Elm Street, Springfield, Anywhere 12345. 
 He can be reached at johndoe@email.com or at the phone number 555-123-4567. His social security number is 123-45-6789,
  and he has a bank account number 9876543210 at Springfield Bank. John attended Springfield University where he earned 
  a Bachelor's degree in Computer Science. He now works at Acme Corp and his employee ID is 123456. John's medical record number
   is MRN-001234, and he has a history of asthma and high blood pressure. His primary care physician is Dr. Jane Smith, 
   who practices at Springfield Medical Center. His recent blood test results show a cholesterol level of 200 mg/dL and a 
   blood glucose level of 90 mg/dL.
" Output:  """
# You can use the function as before
response = remove_pii_info(
    model,
    tokenizer,
    prompt,
    temperature=0.7)

print(response)
```