qwen2.5-1.5b-pii-redactor

Qwen2.5-1.5B PII Redactor

A fine-tuned Qwen2.5-1.5B-Instruct model for identifying and redacting personally identifiable information (PII) from text.

The model was fine-tuned as a text-generation redaction system: given raw text, it preserves the non-PII content and replaces detected PII spans with typed redaction tags.

Model Overview

Property Value

---

Base model Qwen/Qwen2.5-1.5B-Instruct Task PII detection and text redaction Training method Supervised fine-tuning with LoRA Fine-tuning framework Unsloth + TRL + PEFT Dataset ai4privacy/pii-masking-300k Sequence length 512 tokens LoRA rank 16 LoRA alpha 16 LoRA dropout 0.05 Learning rate 2e-4 Epochs 2 Per-device batch size 4 Gradient accumulation 4 Effective batch size 16 Optimizer adamw_8bit Evaluation split 10% Random seed 42

What the Model Does

The model receives ordinary text such as:

Hi, my name is John Smith and you can reach me at [email protected].

and produces redacted text such as:

Hi, my name is [GIVENNAME1] [LASTNAME1] and you can reach me at [EMAIL].

The exact tag vocabulary is learned from the training data. Examples observed during the notebook sanity check include:

• [GIVENNAME1]
• [LASTNAME1]
• [EMAIL]
• [TEL]
• [BUILDING]
• [STREET]
• [CITY]
• [SOCIALNUMBER]

Do not assume that every PII category uses the same tag spelling. The model follows the conventions present in its training data.

Training Data

The notebook uses:

ai4privacy/pii-masking-300k

Each example contains a source text and a target text in which PII spans have been replaced by redaction labels.

For the recorded training run, 50,000 dataset rows were selected:

• 45,000 examples initially formed the training split.
• 5,000 examples formed the evaluation split.
• Unsloth removed 916 training examples because the response marker was missing after tokenization/truncation.
• The actual training run therefore used 44,084 examples.

The dataset split uses seed 42.

Training Approach

1. Quantized base model

The base model is loaded through Unsloth with 4-bit loading:

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="Qwen/Qwen2.5-1.5B-Instruct",
    max_seq_length=512,
    dtype=None,
    load_in_4bit=True,
)

This reduces memory requirements during fine-tuning.

2. LoRA fine-tuning

Instead of updating the full model, LoRA adapters are attached to the attention and MLP projection layers:

r=16
lora_alpha=16
lora_dropout=0.05

target_modules=[
    "q_proj", "k_proj", "v_proj", "o_proj",
    "gate_proj", "up_proj", "down_proj",
]

The notebook reported approximately 18.46 million trainable parameters.

3. Chat-format training

Each dataset example is converted into a Qwen chat conversation containing:

1. A system instruction describing the redaction task.
2. The original text as the user message.
3. The redacted text as the assistant response.

The tokenizer's Qwen chat template is used to construct the final training sequence.

4. Response-only loss

Training uses Unsloth's train_on_responses_only so that the loss is calculated on the assistant's redacted response rather than the system instruction and user input.

This focuses the fine-tuning signal on the actual redaction behavior.

System Prompt

The model was trained with this system instruction:

You are a data redaction assistant. Given a piece of text, identify every span of personally identifiable information (PII) and replace it with a tag indicating its type, e.g. [FIRSTNAME_1], [EMAIL_1], [PHONENUMBER_1]. Leave all non-PII text exactly as it is. Only output the redacted text, nothing else.

For best results, use the same system prompt during inference.

How to Use

Install

pip install torch transformers accelerate safetensors

If you are working with the original fine-tuning notebook, also install the training stack:

pip install unsloth peft trl datasets bitsandbytes huggingface_hub

Transformers Inference

If this repository contains the merged Safetensors model, it can be loaded directly with Transformers:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "Harsh221/qwen2.5-1.5b-pii-redactor"

SYSTEM_PROMPT = (
    "You are a data redaction assistant. Given a piece of text, identify every "
    "span of personally identifiable information (PII) and replace it with a "
    "tag indicating its type, e.g. [FIRSTNAME_1], [EMAIL_1], [PHONENUMBER_1]. "
    "Leave all non-PII text exactly as it is. Only output the redacted text, "
    "nothing else."
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    torch_dtype="auto",
)

def redact(text, max_new_tokens=256):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": text},
    ]

    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt",
    ).to(model.device)

    with torch.no_grad():
        output_ids = model.generate(
            input_ids=inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,
        )

    new_tokens = output_ids[0][inputs.shape[1]:]
    return tokenizer.decode(
        new_tokens,
        skip_special_tokens=True,
    ).strip()

text = (
    "Hi, my name is John Smith and you can reach me at "
    "[email protected] or 555-0192."
)

print(redact(text))

Expected Behavior

The model should preserve ordinary text while replacing detected PII:

Hi, my name is [GIVENNAME1] [LASTNAME1] and you can reach me at [EMAIL] or [TEL].

The exact output tags can vary according to the entity types represented in the training data.

Batch Processing

For multiple documents, call redact() for each document and store the returned redacted text.

For long documents, do not assume that a single generation call will safely process arbitrarily large inputs. Chunk long documents within the model's supported context and design a document-level merge strategy.

Training Notebook

The repository can include the original training notebook:

PII_Redaction_SLM_Finetune_Unsloth(2).ipynb

The notebook is organized as follows:

1. Environment check
2. Library setup
3. Optional Hugging Face authentication
4. Configuration
5. Load Qwen2.5-1.5B-Instruct
6. Add LoRA adapters
7. Load ai4privacy/pii-masking-300k
8. Convert examples to Qwen chat format
9. Apply response-only loss
10. Configure SFT training
11. Build the trainer
12. Train with checkpoint/resume support
13. Run inference sanity checks
14. Run a regex-based PII leak sanity check
15. Save/export the model
16. Optional Hugging Face Hub upload
17. Deployment guidance

Training Configuration

The recorded run used:

Dataset rows selected: 50,000
Train split:           45,000
Eval split:             5,000
Actual training rows:  44,084
Epochs:                 2
Effective batch size:  16
Learning rate:          2e-4
Max sequence length:   512
Checkpoint interval:   200 optimizer steps
Evaluation interval:   200 optimizer steps

The trainer is configured to keep the best checkpoint according to eval_loss.

Resume Training

The notebook checks the output directory for the latest checkpoint and calls:

trainer.train(resume_from_checkpoint=last_checkpoint)

If a checkpoint exists, training can resume without restarting from zero.

Sanity Check Results

The notebook tested two manually selected examples.

Example 1

Input contained:

• a person's name
• an email address
• a telephone number

The model produced:

Hi, my name is [GIVENNAME1] [LASTNAME1] and you can reach me at [EMAIL] or [TEL].

Example 2

Input contained:

• a building/address
• street
• city
• credit-card-like number

The model produced:

Please ship the order to [BUILDING] [STREET], [CITY], and charge card [SOCIALNUMBER].

The notebook's regex leak check reported:

0 / 2 sample outputs had a possible PII leak.

These are sanity-check results, not a comprehensive benchmark. They should not be interpreted as a production accuracy, precision, recall, F1, or privacy guarantee.

Export Formats

The training notebook supports three intended export paths:

LoRA adapter

Smallest artifact. Requires the original base model plus PEFT/LoRA at inference time.

qwen2.5-pii-redactor/
└── lora_adapter/

Merged 16-bit model

LoRA weights merged into the base model. Intended for standard Transformers-style deployment.

qwen2.5-pii-redactor/
└── merged_16bit/

GGUF

Quantized export intended for runtimes such as llama.cpp or Ollama.

qwen2.5-pii-redactor/
└── gguf/

Limitations

This model is a generative PII redactor, not a formal compliance or privacy guarantee.

Important limitations:

• The notebook's automated leak check covers only a small set of regex patterns.
• Only two manually selected inference examples were used for the recorded sanity check.
• eval_loss is a token-level training metric and does not directly measure PII recall or precision.
• The model may miss PII types that are rare or poorly represented in the training data.
• Generative models can occasionally modify non-PII text, produce unexpected tags, or hallucinate redactions.
• Long documents should be handled with an explicit chunking and reconciliation strategy.
• Production deployments should use a broader held-out evaluation set containing the PII categories and languages relevant to the application.

For high-risk privacy workflows, combine the model with deterministic validators, regex/rule-based detectors, confidence or risk thresholds, and human review where appropriate.

Deployment

The notebook outlines these deployment options:

---

Option Suitable use

---

Transformers + FastAPI Simple self-hosted API and low/moderate traffic

Ollama + GGUF Local development or CPU-oriented deployment

vLLM Higher-throughput production inference

Hugging Face Inference Endpoints Managed production deployment

For a production PII-redaction API, recommended additions include:

• request validation
• authentication/authorization
• rate limiting
• structured logging without storing raw PII
• request-size limits
• deterministic post-generation PII scanning
• monitoring for missed PII
• evaluation/regression tests
• secure handling and deletion of input/output data

Reproducibility

The notebook uses:

Base model: Qwen/Qwen2.5-1.5B-Instruct
Dataset:    ai4privacy/pii-masking-300k
Seed:       42
Max length: 512
LoRA r:     16
LoRA alpha: 16
Dropout:    0.05
Epochs:     2
LR:         2e-4

The recorded environment reported:

GPU:         NVIDIA GeForce RTX 3050 Laptop GPU
GPU memory:  6 GB
Unsloth:     2026.8.21
PEFT:        0.15.2
Transformers: 4.52.4

License

The base model used for fine-tuning is:

Qwen/Qwen2.5-1.5B-Instruct

Check the base model's license and the dataset's license/terms before redistributing this derivative model or using it commercially.

This repository's metadata currently declares Apache-2.0; verify that this is appropriate for your intended redistribution and the applicable upstream terms.

Citation

If you use this model, please cite this repository and the upstream Qwen2.5 model.