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geoffmunn/
Qwen3Guard-NewZealand-Classification-8B

Name: Qwen3Guard-NewZealand-Classification-8B
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Model Card for geoffmunn/Qwen3Guard-NewZealand-Classification-8B

This is a fine-tuned version of Qwen3-8B using LoRA (Low-Rank Adaptation) to classify whether user-provided text is related to New Zealand or not. The model acts as a domain-specific content classifier, returning one of two labels: "related" or "not_related". It was developed as part of the Qwen3Guard demonstration project to showcase how large language models can be adapted for custom classification tasks.

Model Details

Model Description

This model is a binary sequence classifier fine-tuned on a synthetic dataset of New Zealand-related questions and general non-New Zealand text. Built atop the Qwen3-8B foundation model, it uses parameter-efficient fine-tuning via LoRA to adapt the model for topic detection in conversational or input text. It is designed for use in moderation systems where filtering based on geographic, cultural, or national topics like New Zealand is desired.

Developed by: Geoff Munn (@geoffmunn )
Shared by: Geoff Munn
Model type: Causal language model with LoRA adapter for sequence classification
Language(s) (NLP): English
License: MIT License (see GitHub repo )
Finetuned from model: Qwen/Qwen3-8B

Model Sources

Repository: https://github.com/geoffmunn/Qwen3Guard
Demo: Interactive demo available via new_zealand_chat.html in the repository; requires local API server

Uses

Direct Use

The model can directly classify whether a given piece of text is related to New Zealand. Example applications include:

Filtering travel forum posts
Moderating tourism or education chatbots
Enhancing region-specific AI assistants (e.g., for NZ government or tourism services)
Educational or cultural awareness tools focused on New Zealand

Input: A string of text Output: One of two labels — "related" or "not_related"

Downstream Use

This model can be integrated into larger systems such as:

Themed conversational agents (e.g., a New Zealand-focused travel advisor)
Content routing engines that classify user queries by geographic relevance
Fine-tuning starter for other country/region-specific classifiers using similar methodology

Out-of-Scope Use

This model should not be used for:

General content moderation (toxicity, hate speech, etc.)
Medical, legal, or safety-critical decision-making
Multilingual classification (trained only on English)
Detecting nuanced sentiment or emotion
Classifying topics outside geography, culture, or national identity without retraining

It may produce inaccurate classifications when presented with ambiguous place names (e.g., "Auckland" in California), metaphorical language, or topics only tangentially related to New Zealand.

Bias, Risks, and Limitations

The training data consists entirely of synthetically generated questions about New Zealand, which introduces several limitations:

Potential overfitting to question formats rather than natural language statements
Limited coverage of Māori language or te reo phrases (trained on English only)
Uneven representation of regions (e.g., more focus on major cities like Auckland or Wellington)
Biases toward well-known landmarks, history, or pop culture (e.g., Lord of the Rings) over lesser-known local topics

Additionally, because the dataset was auto-generated using prompts, there may be inconsistencies in labeling or artificial phrasing patterns.

Recommendations

Users should validate performance on real-world data before deployment. For production use, consider augmenting the dataset with human-labeled examples and testing across diverse inputs (including Māori terms, regional slang, and edge cases). Always pair this model with broader safeguards if used in public-facing applications.

How to Get Started with the Model

You can load and run inference using Hugging Face Transformers:

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "geoffmunn/Qwen3Guard-NewZealand-Classification-8B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

input_text = "What is the capital city of New Zealand?"
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)

outputs = model(**inputs)
predicted_class_id = outputs.logits.argmax().item()
label = model.config.id2label[predicted_class_id]

print(f"Label: {label}")

Ensure you have the required libraries installed:

pip install transformers torch peft

Training Details

Training Data

The model was trained on a synthetic JSONL dataset containing 2,500 labeled examples of New Zealand-related questions marked as "related", and an equal number of randomly sampled general knowledge questions labeled "not_related". The dataset was generated using a custom script generate_new_zealand_questions.py from the repository.

Dataset format:

json

{"input": "Where is Fiordland National Park located?", "label": "related"}
{"input": "Who painted the Mona Lisa?", "label": "not_related"}

Place your dataset at: finetuning/new_zealand/new_zealand_guard_dataset.jsonl

Training Procedure

Preprocessing

Text inputs were tokenized using the Qwen3 tokenizer with a maximum sequence length of 512 tokens. Inputs longer than this were truncated. Labels were mapped via:

python

label2id = {"not_related": 0, "related": 1}
id2label = {0: "not_related", 1: "related"}

Training Hyperparameters

Training regime: Mixed precision training (fp16), enabled via Hugging Face Accelerate
Batch size: 2 (per GPU)
Gradient accumulation steps: 16 → effective batch size: 32
Number of epochs: 3
Learning rate: 2e-4
Optimizer: AdamW
Max sequence length: 512
LoRA configuration:
- Rank (r): 16
- Alpha: 32
- Dropout: 0.05
- Target modules: attention query/value layers and MLP up/down projections

Speeds, Sizes, Times

Hardware used: NVIDIA GPU (assumed: A100 or equivalent)
Training time: ~2–3 hours depending on hardware
Checkpoint size: ~3.8 GB (adapter weights only, PEFT format)
Inference memory: < 10 GB VRAM (with quantization further reduction possible)

Evaluation

Testing Data, Factors & Metrics

Testing Data

A 10% holdout test set (~500 samples) was used for evaluation, split from the full dataset during training.

Factors

Evaluation focused on accuracy across:

Well-known vs. obscure NZ locations or facts
Question vs. statement format
Use of local terms (e.g., "Kiwi", "All Blacks", "Te Reo")

Metrics

Accuracy: Primary metric
Precision, Recall, F1-score: Per-class metrics reported during training
Confusion Matrix: Generated internally during test phase

Results

During final evaluation, the model achieved:

Accuracy: ~96–98% (on synthetic test set)
Strong precision/recall for "related" class
Minor false positives on topics involving other Southern Hemisphere countries (e.g., Australia) or general travel queries

Summary

The model performs well on its intended task within the scope of the training distribution but may degrade on edge cases, ambiguous geography, or culturally nuanced references.

Technical Specifications

Model Architecture and Objective

Base architecture: Qwen3-8B (causal decoder-only LLM)
Adaptation method: LoRA (PEFT)
Task head: Sequence classification (single-label)
Objective function: Cross-entropy loss

Compute Infrastructure

Hardware

GPU: NVIDIA A100 / RTX 3090 / L40S or equivalent RAM: ≥ 32 GB system memory recommended

Software

Python 3.10+
PyTorch 2.4+ with CUDA 12.1+
Transformers 4.40+
PEFT 0.18.0
Accelerate, Datasets, Tokenizers

Citation

While no formal paper exists, please cite the GitHub repository if used academically.

BibTeX:

bibtex

@software{munn_qwen3guard_2025,
  author = {Munn, Geoff},
  title = {Qwen3Guard: Demonstration of Qwen3Guard Models for Content Classification},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  url = {https://github.com/geoffmunn/Qwen3Guard}
}

APA:

Munn, G. (2025). Qwen3Guard: Demonstration of Qwen3Guard Models for Content Classification [Software]. GitHub. https://github.com/geoffmunn/Qwen3Guard

Glossary

LoRA (Low-Rank Adaptation): A parameter-efficient fine-tuning technique that adds trainable low-rank matrices to pretrained weights.
PEFT: Parameter-Efficient Fine-Tuning, a Hugging Face library for lightweight adaptation of large models.
GGUF: Format used for running models in llama.cpp; not supported for streaming variant here.
JSONL: JSON Lines format – one JSON object per line.

More Information

For more details, including API server setup and web demos, visit: 👉 https://github.com/geoffmunn/Qwen3Guard

Includes:

Ollama-compatible scripts
Flask-based API server (api_server.py)
HTML chat interface (new_zealand_chat.html)
Dataset generation tools

Model Card Authors

Geoff Munn – Developer and maintainer

Model Card Contact

For questions or feedback, contact the author via GitHub: @geoffmunn

Framework versions

PEFT 0.18.0