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: []

# ARPA MICRO SERIES: F1 MASK **Zero-Latency PII Scrubbing - 270M Parameter Middleware**

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, it acts as a privacy firewall for incoming/outgoing LLM prompts.

GitHub: 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. 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

# 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)

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:

Example Output

{
  "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).

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.