Update README.md

README.md

@@ -1,7 +1,103 @@
 ---
-base_model: twelcone/pii-phi
+base_model: twelcone/pii-phi-mlx
 library_name: mlx
 pipeline_tag: text-generation
 tags:
 - mlx
 ---
+
+# Overview
+
+`pii-phi-mlx` is a CoreML fine-tuned version of `Phi-3.5-mini-instruct` designed to extract Personally Identifiable Information (PII) from unstructured text for Mac devices. The model outputs PII entities in a structured JSON format according to strict schema guidelines.
+
+# Training Prompt Format
+
+```text
+# GUIDELINES
+- Extract all instances of the following Personally Identifiable Information (PII) entities from the provided text and return them in JSON format.
+- Each item in the JSON list should include an 'entity' key specifying the type of PII and a 'value' key containing the extracted information.
+- The supported entities are: PERSON_NAME, BUSINESS_NAME, API_KEY, USERNAME, API_ENDPOINT, WEBSITE_ADDRESS, PHONE_NUMBER, EMAIL_ADDRESS, ID, PASSWORD, ADDRESS.
+
+# EXPECTED OUTPUT
+- The json output must be in the format below:
+{
+    "result": [
+        {"entity": "ENTITY_TYPE", "value": "EXTRACTED_VALUE"},
+        ...
+    ]
+}
+```
+
+# Supported Entities
+
+* PERSON\_NAME
+* BUSINESS\_NAME
+* API\_KEY
+* USERNAME
+* API\_ENDPOINT
+* WEBSITE\_ADDRESS
+* PHONE\_NUMBER
+* EMAIL\_ADDRESS
+* ID
+* PASSWORD
+* ADDRESS
+
+# Intended Use
+
+The model is intended for PII detection in text documents to support tasks such as data anonymization, compliance, and security auditing.
+
+# Limitations
+
+* Not guaranteed to detect all forms of PII in every context.
+* May return false positives or omit contextually relevant information.
+
+---
+
+# Installation
+
+Install the `vllm` package to run the model efficiently:
+
+```bash
+pip install vllm
+```
+
+---
+
+# Example:
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM("Fsoft-AIC/pii-phi")
+
+system_prompt = """
+# GUIDELINES
+- Extract all instances of the following Personally Identifiable Information (PII) entities from the provided text and return them in JSON format.
+- Each item in the JSON list should include an 'entity' key specifying the type of PII and a 'value' key containing the extracted information.
+- The supported entities are: PERSON_NAME, BUSINESS_NAME, API_KEY, USERNAME, API_ENDPOINT, WEBSITE_ADDRESS, PHONE_NUMBER, EMAIL_ADDRESS, ID, PASSWORD, ADDRESS.
+
+# EXPECTED OUTPUT
+- The json output must be in the format below:
+{
+    "result": [
+        {"entity": "ENTITY_TYPE", "value": "EXTRACTED_VALUE"},
+        ...
+    ]
+}
+"""
+pii_message = "I am James Jake and my employee number is 123123123"
+
+sampling_params = SamplingParams(temperature=0, max_tokens=1000)
+outputs = llm.chat(
+    [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": pii_message},
+    ],
+    sampling_params,
+)
+
+
+for output in outputs:
+    generated_text = output.outputs[0].text
+    print(generated_text)
+```