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Model: eternisai/Anonymizer-0.6B Source: Original Platform
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README.md
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library_name: transformers
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license: cc-by-nc-4.0
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---
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# Model Card for eternisai/Anonymizer-0.6B
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SLMs for semantically similar replacement of PII to provide better end-user privacy.
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### Model description
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The **Anonymizer-0.6B** is a lightweight privacy-preserving language model trained for **surgical anonymization of personal data** before queries leave your device. It detects and replaces sensitive information (names, companies, identifiers, financials, etc.) with semantically similar alternatives, while preserving query intent and meaning.
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This 0.6B model is optimized for **latency and mobile use**, making it a good fit as a speculative decoder or lightweight anonymizer inside the [Enchanted app](http://link.freysa.ai/appstore). Larger variants (1.7B / 4B) deliver stronger anonymization accuracy, but 0.6B runs fastest on consumer hardware.
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## Intended use
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* **Primary use**: Running inside the Eternis app to protect user queries before they are sent to larger LLMs.
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* **Secondary use**: Standalone anonymizer model for research or integration into other privacy-preserving workflows.
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* **Good for**: Detecting and replacing PII while leaving public knowledge intact.
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* **Not for**: General-purpose generation.
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## Training details
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* **Base**: Qwen3-0.6B.
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* **Data**: ~30k samples covering PII replacement + non-replacement categories.
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* **Method**: Supervised fine-tuning → GRPO with GPT-4.1 as judge.
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* **Latency**: <200ms TTFT, near-instant completion when quantized.
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## Limitations
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* Performs worse on nuanced anonymization tasks compared to 1.7B and 4B.
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* Edge cases (rare identifiers, subtle contextual PII) may be missed.
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## Usage Example
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⚠️ **Important**: This model requires specific formatting using the tokenizer's chat template. Do not use raw prompts directly.
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### Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import json
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# Load model and tokenizer
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model_name = "eternisai/Anonymizer-0.6B"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True
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)
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# Define the task instruction
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TASK_INSTRUCTION = """You are an anonymizer. Your task is to identify and replace personally identifiable information (PII) in the given text.
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Replace PII entities with semantically equivalent alternatives that preserve the context needed for a good response.
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If no PII is found or replacement is not needed, return an empty replacements list.
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REPLACEMENT RULES:
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• 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.).
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• 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).
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• Projects / codenames / internal tools: Always replace with a neutral two-word alias of similar length.
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• 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.
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• 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.
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• Identifiers: (emails, phone #s, IDs, URLs, account #s) Always replace with format-valid dummies; keep domain class (.com big-tech, .edu, .gov).
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• Monetary values: Replace personal income, invoices, bids by × [0.8 – 1.25] to keep order-of-magnitude. Keep public list prices & market caps.
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• Quotes / text snippets: If the quote contains PII, swap only the embedded tokens; keep the rest verbatim."""
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# Define tool schema (required!)
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tools = [{
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"type": "function",
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"function": {
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"name": "replace_entities",
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"description": "Replace PII entities with anonymized versions",
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"parameters": {
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"type": "object",
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"properties": {
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"replacements": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"original": {"type": "string"},
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"replacement": {"type": "string"}
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},
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"required": ["original", "replacement"]
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}
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}
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},
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"required": ["replacements"]
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}
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}
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}]
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# Your query to anonymize
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query = "Hi, my son Elijah works at TechStartup Inc and makes $85,000 per year."
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# Format messages properly (critical step!)
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messages = [
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{"role": "system", "content": TASK_INSTRUCTION},
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{"role": "user", "content": query + "\n/no_think"}
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]
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# Apply chat template with tools
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formatted_prompt = tokenizer.apply_chat_template(
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messages,
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tools=tools,
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tokenize=False,
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add_generation_prompt=True
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)
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# Tokenize and generate
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inputs = tokenizer(formatted_prompt, return_tensors="pt", truncation=True).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=250, temperature=0.3, do_sample=True, top_p=0.9)
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# Decode and extract response
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response = tokenizer.decode(outputs[0], skip_special_tokens=False)
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assistant_response = response.split("assistant")[-1].split("<|im_end|>")[0].strip()
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print("Response:", assistant_response)
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# Expected output format:
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# <|tool_call|>{"name": "replace_entities", "arguments": {"replacements": [{"original": "Elijah", "replacement": "Nathan"}, {"original": "TechStartup Inc", "replacement": "DataSoft LLC"}, {"original": "$85,000", "replacement": "$72,000"}]}}</|tool_call|>
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```
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### Parsing the Response
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```python
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def parse_replacements(response):
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"""Extract replacements from model response"""
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try:
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if '<|tool_call|>' in response:
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start = response.find('<|tool_call|>') + len('<|tool_call|>')
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end = response.find('</|tool_call|>')
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elif '<tool_call>' in response:
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start = response.find('<tool_call>') + len('<tool_call>')
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end = response.find('</tool_call>')
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else:
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return None
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if end != -1:
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json_str = response[start:end].strip()
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tool_data = json.loads(json_str)
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return tool_data.get('arguments', {}).get('replacements', [])
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except:
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return None
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# Parse the response
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replacements = parse_replacements(assistant_response)
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if replacements:
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for r in replacements:
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print(f"Replace '{r['original']}' with '{r['replacement']}'")
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```
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### Output Format
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The model outputs tool calls in this format:
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**With PII detected:**
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```json
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<|tool_call|>
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{"name": "replace_entities", "arguments": {"replacements": [
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{"original": "John", "replacement": "Marcus"},
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{"original": "Microsoft", "replacement": "TechCorp"},
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{"original": "$5000", "replacement": "$4200"}
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]}}
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</|tool_call|>
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```
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**No PII detected:**
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```json
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<|tool_call|>
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{"name": "replace_entities", "arguments": {"replacements": []}}
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</|tool_call|>
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```
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## Important Notes
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1. **Chat Template Required**: The model will NOT work with raw prompts. You must use `tokenizer.apply_chat_template()` with the tools parameter.
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2. **Tool Schema Required**: The tools schema must be provided to the chat template for proper formatting.
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3. **Special Marker**: User queries need the `/no_think` marker appended.
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4. **Response Format**: The model outputs structured tool calls wrapped in `<|tool_call|>` tags (or `<tool_call>` in some versions).
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## Common Issues
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**Issue**: Model outputs gibberish or doesn't follow the format
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**Solution**: Ensure you're using `apply_chat_template` with the tools parameter
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**Issue**: Model doesn't detect obvious PII
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**Solution**: Make sure to append `/no_think` to the user query
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**Issue**: Getting errors about missing tools
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**Solution**: The tools schema is required - see the example above
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## Technical Details
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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**:
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<details>
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<summary>Internal prompt structure (auto-generated, for reference only)</summary>
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```
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[BEGIN OF TASK INSTRUCTION]
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You are an anonymizer. Your task is to identify and replace personally identifiable information (PII)...
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[END OF TASK INSTRUCTION]
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[BEGIN OF AVAILABLE TOOLS]
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[{"type": "function", "function": {"name": "replace_entities", ...}}]
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[END OF AVAILABLE TOOLS]
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[BEGIN OF FORMAT INSTRUCTION]
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Use the replace_entities tool to specify replacements...
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[END OF FORMAT INSTRUCTION]
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[BEGIN OF QUERY]
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Your text to anonymize goes here
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/no_think
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[END OF QUERY]
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```
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This structure is created automatically when you use `tokenizer.apply_chat_template()` - never construct it manually.
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</details>
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