Create README.md

How to use model added

README.md

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+# PII Guardian PHI3 Mini LORA
+
+This repository contains a fine-tuned model for detecting Personally Identifiable Information (PII) using PHI3 Mini with LoRA applied to the query, key, and value layers. The model is optimized for accuracy and efficiency in identifying various PII entities.
+
+## How to Use
+
+You can use the following Python code to load and run the model:
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Check the available device
+if torch.cuda.is_available():
+    device = "cuda"
+elif torch.backends.mps.is_available():
+    device = "mps"
+else:
+    device = "cpu"
+
+print(f"Using device: {device}")
+
+# Check for bf16 support
+is_bf16_support = False
+try:
+    tensor_bf16 = torch.tensor([1.0], dtype=torch.bfloat16, device=device)
+    is_bf16_support = True
+    print("bf16 tensors are supported.")
+except TypeError:
+    print("bf16 tensors are not supported.")
+
+# Load the base model and tokenizer
+base_model = "microsoft/Phi-3-mini-4k-instruct"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+# Load the fine-tuned PII detection model with LoRA
+model = AutoModelForCausalLM.from_pretrained(base_model, return_dict=True, device_map=device, torch_dtype=torch.bfloat16 if is_bf16_support else torch.float16)
+pii_model = PeftModel.from_pretrained(model, "ab-ai/PII-Guardian-Phi3-Mini-LORA")
+
+# Example input text
+input_text = "Hi Abner, just a reminder that your next primary care appointment is on 23/03/1926. Please confirm by replying to this email [email protected]."
+
+# Define the model prompt
+model_prompt = f"""### Instruction:
+    Identify and extract the following PII entities from the text, if present: companyname, pin, currencyname, email, phoneimei, litecoinaddress, currency, eyecolor, street, mac, state, time, vehiclevin, jobarea, date, bic, currencysymbol, currencycode, age, nearbygpscoordinate, amount, ssn, ethereumaddress, zipcode, buildingnumber, dob, firstname, middlename, ordinaldirection, jobtitle, bitcoinaddress, jobtype, phonenumber, height, password, ip, useragent, accountname, city, gender, secondaryaddress, iban, sex, prefix, ipv4, maskednumber, url, username, lastname, creditcardcvv, county, vehiclevrm, ipv6, creditcardissuer, accountnumber, creditcardnumber. Return the output in JSON format.
+
+    ### Input:
+    {input_text}
+
+    ### Output:
+    
+    """
+
+# Tokenize the input and generate a response
+inputs = tokenizer(model_prompt, return_tensors="pt").to(device)
+# adjust max_new_tokens according to your need
+outputs = pii_model.generate(**inputs, do_sample=True, max_new_tokens=120)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)