Update README.md

README.md

@@ -47,7 +47,87 @@ Instruction-tuned on a compact policy spec + ~20 curated examples emphasizing **
 Judged by a frontier LLM using a deterministic rubric: JSON-only, schema validity, **redacted_text exact match**, and **set-equality** of `(value, replacement_token)` pairs (reason/order ignored). Score: **0.25 +/- 0.05**.
 
 ## How to Use
-Details of deployment can be found in https://docs.distillabs.ai/how-to/model-deployment
+Details of deployment can be found in [docs](https://docs.distillabs.ai/how-to/model-deployment). Deploy the model using vllm or ollama (-gguf version available in this collection) and use the following snippet to get results
+```python
+SYSTEM_PROMPT = """
+You are a problem solving model working on task_description XML block:
+<task_description>
+Produce a redacted version of texts, removing sensitive personal data while preserving operational signals. The model must return a single json blob with:
+
+* **redacted_text** is the input with minimal, in-place replacements of redacted entities.
+* **entities** as an array of objects with exactly three fields {value: original_value, replacement_token: replacement, reason: reasoning}.
+
+## What to redact (→ replacement token)
+
+* **PERSON** — customer/patient/person names (first/last/full; identifying initials) → `[PERSON]`
+* **EMAIL** — any email, including obfuscated `name(at)domain(dot)com` → `[EMAIL]`
+* **PHONE** — any international/national format (separators/emoji bullets allowed) → `[PHONE]`
+* **ADDRESS** — street + number; full postal lines; apartment/unit numbers → `[ADDRESS]`
+* **SSN** — US Social Security numbers → `[SSN]`
+* **ID** — national IDs (PESEL, NIN, Aadhaar, DNI, etc.) when personal → `[ID]`
+* **UUID** — person-scoped system identifiers (e.g., MRN/NHS/patient IDs/customer UUIDs) → `[UUID]`
+* **CREDIT_CARD** — 13–19 digits (spaces/hyphens allowed) → `[CARD_LAST4:####]` (keep last-4 only)
+* **IBAN** — IBAN/bank account numbers → `[IBAN_LAST4:####]` (keep last-4 only)
+* **GENDER** — self-identification (male/female/non-binary/etc.) → `[GENDER]`
+* **AGE** — stated ages (“I’m 29”, “age: 47”, “29 y/o”) → `[AGE_YEARS:##]`
+* **RACE** — race/ethnicity self-identification → `[RACE]`
+* **MARITAL_STATUS** — married/single/divorced/widowed/partnered → `[MARITAL_STATUS]`
+
+
+## Keep (do not redact)
+
+* Card **last-4** when only last-4 is present (e.g., “ending 9021”, “•••• 9021”).
+* Operational IDs: order/ticket/invoice numbers, shipment tracking, device serials, case IDs.
+* Non-personal org info: company names, product names, team names.
+* Cities/countries alone (redact full street+number, not plain city/country mentions).
+
+## Output schema (exactly these fields)
+* **redacted_text** The original text with all the sensitive information replaced with redacted tokens
+* **entities** Array with all the replaced elements, each element represented by following fields
+  * **replacement_token**: one of `[PERSON] | [EMAIL] | [PHONE] | [ADDRESS] | [SSN] | [ID] | [UUID] | [CREDIT_CARD] | [IBAN] | [GENDER] | [AGE] | [RACE] | [MARITAL_STATUS]`
+  * **value**: original text that was redacted
+  * **reason**: brief string explaining the rule/rationale
+
+for example
+{
+  "redacted_text": "Hi, I'm [PERSON] and my email is [EMAIL].",
+  "entities": [
+    { "type": "PERSON", "value": "John Smith", "reason": "person name"},
+    { "type": "EMAIL", "value": "[email protected]", "reason": "email"},
+  ]
+}
+</task_description>
+You will be given a single task with context in the context XML block and the task in the question XML block
+Solve the task in question block based on the context in context block.
+Generate only the answer, do not generate anything else
+"""
+
+PROMPT_TEMPLATE = """
+
+Now for the real task, solve the task in question block based on the context in context block.
+Generate only the solution, do not generate anything else
+<context>
+{context}
+</context>
+<question>Redact provided text according to the task description and return redacted elements.</question>
+"""
+
+from openai import OpenAI
+
+PORT = "PORT GOES HERE"  # 8000 for vllm, 11434 for ollama
+MODEL_NAME = "NAME USED FOR SETTING UP THE CLIENT"
+TEXT_TO_REDACT = "NI number AB123456C confirmed."
+
+client = OpenAI(base_url=f"http://127.0.0.1:{PORT}/v1", api_key="EMPTY")
+chat_response = client.chat.completions.create(
+    model=MODEL_NAME,
+    messages=[
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": PROMPT_TEMPLATE.format(context=TEXT_TO_REDACT)},
+    ],
+    temperature=0,
+)
+```
 
 
 ## Risks & Mitigations