# Model Card for eternisai/Anonymizer-4B SLMs for semantically similar replacement of PII to provide better end-user privacy. ### Model description The **Anonymizer-4B** is the strongest model in the Enchanted anonymizer series. Effectively matching GPT-4.1 while being thousands of times smaller. It is the most accurate variant available and powers advanced anonymization in [Enchanted](http://link.freysa.ai/appstore). ## Intended use * **Primary use**: High-accuracy anonymizer inside Enchanted. * **Secondary use**: Deployments where top-quality anonymization is critical (enterprise, research). ## Training details * **Base**: Qwen3-4B. * **Data**: ~30k samples covering PII replacement + non-replacement categories. * **Method**: Supervised fine-tuning → GRPO with GPT-4.1 as judge. * **Score**: 9.55/10 on anonymization quality. * **Latency**: <250ms TTFT, <2s full completion (quantized). ## Limitations * Largest model in the series, not suitable for mobile inference as of August 2025. * Requires MacBook-class hardware or above for real-time use. ## Usage Example ⚠️ **Important**: This model requires specific formatting using the tokenizer's chat template. Do not use raw prompts directly. ### Quick Start ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch import json # Load model and tokenizer model_name = "eternisai/Anonymizer-4B" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True ) # Define the task instruction TASK_INSTRUCTION = """You are an anonymizer. Your task is to identify and replace personally identifiable information (PII) in the given text. Replace PII entities with semantically equivalent alternatives that preserve the context needed for a good response. If no PII is found or replacement is not needed, return an empty replacements list. REPLACEMENT RULES: • Personal names: Replace private or small-group individuals. Pick same culture + gender + era; keep surnames aligned across family members. DO NOT replace globally recognised public figures (heads of state, Nobel laureates, A-list entertainers, Fortune-500 CEOs, etc.). • Companies / organisations: Replace private, niche, employer & partner orgs. Invent a fictitious org in the same industry & size tier; keep legal suffix. Keep major public companies (anonymity set ≥ 1,000,000). • Projects / codenames / internal tools: Always replace with a neutral two-word alias of similar length. • Locations: Replace street addresses, buildings, villages & towns < 100k pop with a same-level synthetic location inside the same state/country. Keep big cities (≥ 1M), states, provinces, countries, iconic landmarks. • Dates & times: Replace birthdays, meeting invites, exact timestamps. Shift day/month by small amounts while KEEPING THE SAME YEAR to maintain temporal context. DO NOT shift public holidays or famous historic dates ("July 4 1776", "Christmas Day", "9/11/2001", etc.). Keep years, fiscal quarters, decade references unchanged. • Identifiers: (emails, phone #s, IDs, URLs, account #s) Always replace with format-valid dummies; keep domain class (.com big-tech, .edu, .gov). • Monetary values: Replace personal income, invoices, bids by × [0.8 – 1.25] to keep order-of-magnitude. Keep public list prices & market caps. • Quotes / text snippets: If the quote contains PII, swap only the embedded tokens; keep the rest verbatim.""" # Define tool schema (required!) tools = [{ "type": "function", "function": { "name": "replace_entities", "description": "Replace PII entities with anonymized versions", "parameters": { "type": "object", "properties": { "replacements": { "type": "array", "items": { "type": "object", "properties": { "original": {"type": "string"}, "replacement": {"type": "string"} }, "required": ["original", "replacement"] } } }, "required": ["replacements"] } } }] # Your query to anonymize query = "Hi, my son Elijah works at TechStartup Inc and makes $85,000 per year." # Format messages properly (critical step!) messages = [ {"role": "system", "content": TASK_INSTRUCTION}, {"role": "user", "content": query + "\n/no_think"} ] # Apply chat template with tools formatted_prompt = tokenizer.apply_chat_template( messages, tools=tools, tokenize=False, add_generation_prompt=True ) # Tokenize and generate inputs = tokenizer(formatted_prompt, return_tensors="pt", truncation=True).to(model.device) outputs = model.generate(**inputs, max_new_tokens=250, temperature=0.3, do_sample=True, top_p=0.9) # Decode and extract response response = tokenizer.decode(outputs[0], skip_special_tokens=False) assistant_response = response.split("assistant")[-1].split("<|im_end|>")[0].strip() print("Response:", assistant_response) # Expected output format: # <|tool_call|>{"name": "replace_entities", "arguments": {"replacements": [{"original": "Elijah", "replacement": "Nathan"}, {"original": "TechStartup Inc", "replacement": "DataSoft LLC"}, {"original": "$85,000", "replacement": "$72,000"}]}} ``` ### Parsing the Response ```python def parse_replacements(response): """Extract replacements from model response""" try: if '<|tool_call|>' in response: start = response.find('<|tool_call|>') + len('<|tool_call|>') end = response.find('') elif '' in response: start = response.find('') + len('') end = response.find('') else: return None if end != -1: json_str = response[start:end].strip() tool_data = json.loads(json_str) return tool_data.get('arguments', {}).get('replacements', []) except: return None # Parse the response replacements = parse_replacements(assistant_response) if replacements: for r in replacements: print(f"Replace '{r['original']}' with '{r['replacement']}'") ``` ### Output Format The model outputs tool calls in this format: **With PII detected:** ```json <|tool_call|> {"name": "replace_entities", "arguments": {"replacements": [ {"original": "John", "replacement": "Marcus"}, {"original": "Microsoft", "replacement": "TechCorp"}, {"original": "$5000", "replacement": "$4200"} ]}} ``` **No PII detected:** ```json <|tool_call|> {"name": "replace_entities", "arguments": {"replacements": []}} ``` ## Important Notes 1. **Chat Template Required**: The model will NOT work with raw prompts. You must use `tokenizer.apply_chat_template()` with the tools parameter. 2. **Tool Schema Required**: The tools schema must be provided to the chat template for proper formatting. 3. **Special Marker**: User queries need the `/no_think` marker appended. 4. **Response Format**: The model outputs structured tool calls wrapped in `<|tool_call|>` tags (or `` in some versions). ## Common Issues **Issue**: Model outputs gibberish or doesn't follow the format **Solution**: Ensure you're using `apply_chat_template` with the tools parameter **Issue**: Model doesn't detect obvious PII **Solution**: Make sure to append `/no_think` to the user query **Issue**: Getting errors about missing tools **Solution**: The tools schema is required - see the example above ## Technical Details The model was trained using the Qwen3 chat template format with tool calling capabilities. The internal prompt structure (shown below for reference) is automatically generated by the tokenizer - **do not construct this manually**:

Internal prompt structure (auto-generated, for reference only)

``` [BEGIN OF TASK INSTRUCTION] You are an anonymizer. Your task is to identify and replace personally identifiable information (PII)... [END OF TASK INSTRUCTION] [BEGIN OF AVAILABLE TOOLS] [{"type": "function", "function": {"name": "replace_entities", ...}}] [END OF AVAILABLE TOOLS] [BEGIN OF FORMAT INSTRUCTION] Use the replace_entities tool to specify replacements... [END OF FORMAT INSTRUCTION] [BEGIN OF QUERY] Your text to anonymize goes here /no_think [END OF QUERY] ``` This structure is created automatically when you use `tokenizer.apply_chat_template()` - never construct it manually.