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---
library_name: transformers
license: cc-by-nc-4.0
---
# Model Card for eternisai/Anonymizer-0.6B
SLMs for semantically similar replacement of PII to provide better end-user privacy.
### Model description
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.
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.
## Intended use
* **Primary use**: Running inside the Eternis app to protect user queries before they are sent to larger LLMs.
* **Secondary use**: Standalone anonymizer model for research or integration into other privacy-preserving workflows.
* **Good for**: Detecting and replacing PII while leaving public knowledge intact.
* **Not for**: General-purpose generation.
## Training details
* **Base**: Qwen3-0.6B.
* **Data**: ~30k samples covering PII replacement + non-replacement categories.
* **Method**: Supervised fine-tuning → GRPO with GPT-4.1 as judge.
* **Latency**: <200ms TTFT, near-instant completion when quantized.
## Limitations
* Performs worse on nuanced anonymization tasks compared to 1.7B and 4B.
* Edge cases (rare identifiers, subtle contextual PII) may be missed.
## 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-0.6B"
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"}]}}</|tool_call|>
```
### 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('</|tool_call|>')
elif '<tool_call>' in response:
start = response.find('<tool_call>') + len('<tool_call>')
end = response.find('</tool_call>')
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"}
]}}
</|tool_call|>
```
**No PII detected:**
```json
<|tool_call|>
{"name": "replace_entities", "arguments": {"replacements": []}}
</|tool_call|>
```
## 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 `<tool_call>` 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**:
<details>
<summary>Internal prompt structure (auto-generated, for reference only)</summary>
```
[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.
</details>

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
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"Qwen3ForCausalLM"
],
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"attention_dropout": 0.0,
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"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
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"initializer_range": 0.02,
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"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "float16",
"transformers_version": "4.55.4",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "4.55.4"
}

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"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

1
vocab.json Normal file
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