---
license: apache-2.0
language:
- en
tags:
- zero-latency
- pii-scrubbing
- pii
- compliance
- privacy
- function-calling
- arpa
- micro-f1-mask
- micro-series
pipeline_tag: text-generation
library_name: transformers
base_model: google/gemma-3-270m-it
datasets:
- synthetic
model-index:
- name: micro-f1-mask
  results: []
---

<div align="center">

# ARPA MICRO SERIES: F1 MASK

**Zero-Latency PII Scrubbing - 270M Parameter Middleware**

<a href="https://huggingface.co/google/gemma-3-270m-it"><img src="https://img.shields.io/badge/base-Gemma_3_270M-bae6fd?style=flat-square" alt="Base Model"></a>
<a href="#binary-mapping--tokens"><img src="https://img.shields.io/badge/task-PII_Scrubbing-efcefa?style=flat-square" alt="Task"></a>
<a href="#training-methodology"><img src="https://img.shields.io/badge/training-PEFT_LoRA-bbf7d0?style=flat-square" alt="Training"></a>
<a href="https://www.apache.org/licenses/LICENSE-2.0.html"><img src="https://img.shields.io/badge/license-Apache_2.0-fde68a?style=flat-square" alt="License"></a>

</div>

---

**ARPA Micro Series: F1 Mask** is a high-performance fine-tuned model built to provide real-time identification and tokenization of Personally Identifiable Information (PII) for secure cloud computing. 

Developed by [ARPA Hellenic Logical Systems](https://arpacorp.net), it acts as a privacy firewall for incoming/outgoing LLM prompts.

**GitHub**: [arpahls/micro-f1-mask](https://github.com/arpahls/micro-f1-mask) — Full training pipeline, Redis vault, and infrastructure.

## Model Summary

F1 Mask is a specialized fine-tune of [Gemma 3 270M IT](https://huggingface.co/google/gemma-3-270m-it). It is trained exclusively to output structured `replace_pii` function calls, effectively mapping sensitive data to safe tokens before they reach cloud-based LLMs.

## Quick Start

### 1. Register with Ollama

```bash
# Direct SafeTensors registration
ollama create micro-f1-mask --from arpacorp/micro-f1-mask

# Run detection
ollama run micro-f1-mask "John Doe called from 555-0123 about invoice GB29NWBK60161331926819."
```

### 2. Python (Transformers)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("arpacorp/micro-f1-mask")
tokenizer = AutoTokenizer.from_pretrained("arpacorp/micro-f1-mask")

prompt = """<start_of_turn>user
You are Micro F1 Mask. Extract PII and output the 'replace_pii' function call.
Draft an email to Jane Smith at jane@example.com.<end_of_turn>
<start_of_turn>model
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Binary Mapping & Tokens

The model uses a deterministic tokenization scheme:

| Category | Token |
|----------|-------|
| INDIVIDUAL | [INDIVIDUAL_N] |
| FINANCIAL | [FINANCIAL_N] |
| LOCATION | [LOCATION_N] |
| CONTACT | [CONTACT_N] |
| ACCESS | [ACCESS_N] |
| CORP | [CORP_N] |

### Example Output

```json
{
  "name": "replace_pii",
  "arguments": {
    "entities": [
      {"type": "INDIVIDUAL", "val": "Jane Smith", "id": "[INDIVIDUAL_1]"},
      {"type": "CONTACT", "val": "jane@example.com", "id": "[CONTACT_1]"}
    ]
  }
}
```

## Training Methodology

- **Dataset**: 1,000 synthetic samples generated via high-entropy LLM workflows.
- **Method**: PEFT / LoRA (Rank 16, Alpha 32).
- **Epochs**: 3.
- **Accuracy**: 76.10% (token-level generation).
- **Latency**: Sub-50ms (inference on RTX 2070).

## Production Optimization Roadmap

While this repository provides a fully functional 1,000-sample prototype, reaching 95%+ enterprise accuracy requires the following architectural optimizations:

### 1. Hard-Negative Mining (Re-training)
To push accuracy into the high 90s, the model must iteratively learn from its mistakes:
1. **Scale**: Use the synthetic generator to produce 10,000 - 50,000 highly diverse samples tailored to your industry.
2. **Evaluate**: Run an evaluation script to benchmark against samples of your real-world traffic.
3. **Mine Edge Cases**: Every time the model misses a PII token (a "hard negative"), extract that sentence structure, generate 500 synthetic variations of that specific edge-case, and re-run the fine-tuning pipeline.

### 2. Human-In-The-Loop (HITL) Workflows
For mission-critical data, we recommend extending the middleware bridge to include human oversight:

- **Pre-Cloud Quarantine (Maximum Security):** Modify the endpoint so that when F1 Mask detects PII, the API payload pauses. The application UI highlights the detected entities to the user. The user manually verifies the masking *before* the payload is authorized to hit the external cloud.
- **Post-Reconstruction Review (Quality Control):** Allow the fully automated process to finish. Before the final reconstructed cloud response is saved or emailed, route it to an analyst dashboard where a human can manually verify the grammar of the reconstructed payload.

## Enterprise Solutions

The public release of **ARPA F1 Mask** serves as a lightweight demonstration of how the *Function One (F1)* architecture can be fine-tuned for structured privacy enforcement. 

For mission-critical infrastructure, ARPA offers an actively maintained, highly robust enterprise tier. Organizations can deploy our gated version out-of-the-box and completely offload the burden of continuous maintenance, bespoke fine-tuning, concept drift avoidance, and rigorous scenario evaluations. 

For enterprise licensing or to discuss tailoring the F1 model to your proprietary data schemas, reach out to: **[input@arpacorp.net](mailto:input@arpacorp.net)**

## Ethical Considerations

**Data Provenance**: No real PII was used in the training of this model. All examples were synthetically generated to mimic enterprise communication patterns.

**Intended Use**: This model is designed for middleware. It is not intended to be used as a conversational assistant. It is a one-way security gate that focuses exclusively on privacy enforcement.

---

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<img src="https://raw.githubusercontent.com/ARPAHLS/skillware/main/assets/arpalogo.png" width="50px">

Built by [ARPA Hellenic Logical Systems](https://arpacorp.net)

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