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README.md
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---
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tags:
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- text-generation
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widget:
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- text:
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---
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---
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tags:
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- text-generation
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widget:
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- text: >
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John Doe, born on January 1, 1990, currently lives at 1234 Elm Street, Springfield, Anywhere 12345.
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He can be reached at [email protected] or at the phone number 555-123-4567. His social security number is 123-45-6789,
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and he has a bank account number 9876543210 at Springfield Bank. John attended Springfield University where he earned
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a Bachelor's degree in Computer Science. He now works at Acme Corp and his employee ID is 123456. John's medical record number
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is MRN-001234, and he has a history of asthma and high blood pressure. His primary care physician is Dr. Jane Smith,
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who practices at Springfield Medical Center. His recent blood test results show a cholesterol level of 200 mg/dL and a
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blood glucose level of 90 mg/dL.
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---
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```python
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import transformers
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from peft import PeftModel, PeftConfig
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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from torch import cuda, bfloat16
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base_model_id = 'meta-llama/Llama-2-7b-hf'
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device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
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bnb_config = transformers.BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type='nf4',
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=bfloat16
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)
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hf_auth = "hf_your-huggingface-access-token"
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model_config = transformers.AutoConfig.from_pretrained(
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base_model_id,
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use_auth_token=hf_auth
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)
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model = transformers.AutoModelForCausalLM.from_pretrained(
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base_model_id,
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trust_remote_code=True,
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config=model_config,
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quantization_config=bnb_config,
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device_map='auto',
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use_auth_token=hf_auth
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)
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config = PeftConfig.from_pretrained("Ashishkr/pii-removal")
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model = PeftModel.from_pretrained(model, "Ashishkr/pii-removal").to(device)
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model.eval()
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print(f"Model loaded on {device}")
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tokenizer = transformers.AutoTokenizer.from_pretrained(
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base_model_id,
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use_auth_token=hf_auth
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)
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```
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```python
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def remove_pii_info(
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model: AutoModelForCausalLM,
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tokenizer: AutoTokenizer,
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prompt: str,
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max_new_tokens: int = 128,
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temperature: float = 0.92):
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inputs = tokenizer(
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[prompt],
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return_tensors="pt",
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return_token_type_ids=False).to(device)
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max_new_tokens = inputs["input_ids"].shape[1]
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# Check if bfloat16 is supported, otherwise use float16
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dtype_to_use = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
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with torch.autocast("cuda", dtype=dtype_to_use):
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response = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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temperature=temperature,
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return_dict_in_generate=True,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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)
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decoded_output = tokenizer.decode(
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response["sequences"][0],
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skip_special_tokens=True,
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)
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return decoded_output[len(prompt) :]
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prompt = """
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Input: "John Doe, born on January 1, 1990, currently lives at 1234 Elm Street, Springfield, Anywhere 12345.
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He can be reached at [email protected] or at the phone number 555-123-4567. His social security number is 123-45-6789,
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and he has a bank account number 9876543210 at Springfield Bank. John attended Springfield University where he earned
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a Bachelor's degree in Computer Science. He now works at Acme Corp and his employee ID is 123456. John's medical record number
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is MRN-001234, and he has a history of asthma and high blood pressure. His primary care physician is Dr. Jane Smith,
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who practices at Springfield Medical Center. His recent blood test results show a cholesterol level of 200 mg/dL and a
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blood glucose level of 90 mg/dL.
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" Output: """
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# You can use the function as before
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response = remove_pii_info(
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model,
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tokenizer,
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prompt,
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temperature=0.7)
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print(response)
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```
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