Update README.md

README.md

@@ -1,21 +1,100 @@
----
-base_model: unsloth/gemma-3-270m-it
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- gemma3_text
-license: apache-2.0
-language:
-- en
----
-
-# Uploaded finetuned  model
-
-- **Developed by:** traromal
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-3-270m-it
-
-This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## 💡 Example Inference Code
+
+You can try this PII Masking model directly with the following script:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import torch
+
+# ----------------------------
+# Load model & tokenizer
+# ----------------------------
+model_name = "traromal/AIccel_entity_masker_Gemma3_270m"
+print(f"Loading model: {model_name}")
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    low_cpu_mem_usage=True
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+print(f"✅ Model loaded on {device}")
+
+# ----------------------------
+# System prompt
+# ----------------------------
+SYSTEM_PROMPT = """You are a global data privacy expert.
+Identify and mask all PII (Personally Identifiable Information) in text.
+Replace each with an appropriate tag like [NAME], [AADHAR_NUMBER], [PHONE], etc.
+Also list detected entities with their type and sensitivity level."""
+
+# ----------------------------
+# Masking function
+# ----------------------------
+def mask_pii(text, stream=False):
+    messages = [
+        {'role': 'system', 'content': SYSTEM_PROMPT},
+        {'role': 'user', 'content': f'Mask all sensitive PII in:\n\n"{text}"'}
+    ]
+
+    chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(chat, return_tensors="pt").to(device)
+
+    if stream:
+        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+        model.generate(
+            **inputs,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.9,
+            top_k=50,
+            do_sample=True,
+            streamer=streamer,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    else:
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.9,
+            top_k=50,
+            do_sample=True,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+        response = tokenizer.decode(outputs[0], skip_special_tokens=False)
+        if "<start_of_turn>model" in response:
+            response = response.split("<start_of_turn>model")[-1].replace("<end_of_turn>", "").strip()
+        print(response)
+        return response
+
+# ----------------------------
+# Quick examples
+# ----------------------------
+examples = [
+    "My name is Rajesh Kumar and my Aadhar number is 1234-5678-9012. Contact me at +91-9876543210.",
+    "Patient Priya Sharma, Blood Group: B+, UHID: MH2023-12345, DOB: 15/08/1990.",
+    "Please transfer ₹50,000 to account 123456789012 (IFSC: HDFC0001234). UPI ID: amit.kumar@paytm.",
+    "John Smith, SSN: 123-45-6789, email: john.smith@gmail.com",
+]
+
+for text in examples:
+    print("\n🧩 Original:", text)
+    print("🔒 Masked:")
+    mask_pii(text, stream=True)
+    print("=" * 80)
+
+# ----------------------------
+# Interactive mode
+# ----------------------------
+while True:
+    user_text = input("\n🔒 Enter text to mask (or 'exit'): ").strip()
+    if user_text.lower() in ["exit", "quit", "q"]:
+        break
+    mask_pii(user_text, stream=True)