This repository was created for a senior project in ENGT 375 Applied Machine Learning at Old Dominion University. It is provided for educational and research demonstration purposes only. It is not intended for production use, security filtering, or making real-world spam/phishing decisions. Always use established security tools for operational email protection.

Spam Classifier — MLX LoRA Fine-Tune (Qwen 3.5 0.8B)

ENGT 375 — Applied Machine Learning | Spring 2026 | ODU A Qwen 3.5 0.8B language model fine-tuned with LoRA adapters on Apple Silicon using the MLX framework for spam email classification.

Model Details

Base model: Qwen3.5-0.8B (4-bit quantized, OptiQ)
Fine-tuning: LoRA (rank 8, scale 20.0, 1500 iterations)
Framework: MLX (mlx-lm)
Hardware: Apple Silicon (M-series)
Task: Classify emails as spam or ham via chat-style prompts

Evaluation Results

Metric	Value
Test Loss	0.996
Test Perplexity	2.708
Best Training Loss	0.808 (iteration 380)
Final Training Loss	1.050 (iteration 600)

Training Details

Hyperparameter	Value
Training examples	2,204 (3-class: ham 819, spam 816, phishing 569)
Test examples	551 (3-class: ham 216, spam 187, phishing 148)
Iterations	1,500
Batch size	1
Learning rate	1e-5
Gradient checkpointing	Enabled
Prompt masking	Enabled
Max sequence length	1,024
LoRA layers	4
LoRA rank	8
LoRA scale	20.0
Optimizer	Adam
Training time	~60–90 minutes (scaled from the 30 min / 600-iter v0.3.0 reference run)

Hardware

Device: Apple Silicon (M-series), 24GB unified memory
Framework: MLX (Apple's machine learning framework optimized for Apple Silicon)

Dataset

VoltageVagabond/spam-email-dataset

Usage

python

from mlx_lm import load, generate

model, tokenizer = load("models/Qwen3.5-0.8B-OptiQ-4bit", adapter_path="adapters")
prompt = "Classify this email as spam or ham:\n\nSubject: You won a prize!\n..."
response = generate(model, tokenizer, prompt=prompt, max_tokens=100)

Gradio Interface

bash

pip install -r requirements.txt
python app.py

Files

adapters/ — LoRA adapter weights (.safetensors)
fine_tune.py — Training script
app.py — Gradio web interface
training_data/ — Training dataset (JSONL)

Intended Use

This model is an educational demonstration of LLM fine-tuning for text classification, created as part of a university course project. It is suitable for:

Learning how LoRA fine-tuning works on Apple Silicon with MLX
Understanding prompt-based classification with small language models
Comparing LLM-based approaches to traditional ML classifiers

It is not intended for production spam filtering.

Limitations

May misclassify legitimate marketing emails as spam
Trained on English emails only — not suitable for other languages
Small training set (2,204 train / 551 test examples) limits generalization
4-bit quantized base model trades some accuracy for memory efficiency

Related Models

Model	Description	Link
spam-classifier-liquid	Liquid AI LFM2.5-1.2B LoRA fine-tune	VoltageVagabond/spam-classifier-liquid
spam-xai-model	sklearn voting ensemble (RF + LR + SVM) with LIME/SHAP/ELI5 explainability	VoltageVagabond/spam-xai-model
spam-xai-classifier (Space)	Live Gradio web app for the sklearn classifier	VoltageVagabond/spam-xai-classifier

Citation

bibtex

@misc{voltagevagabond2026spammlx,
  title={Spam Classifier — MLX LoRA Fine-Tune (Qwen 3.5 0.8B)},
  author={VoltageVagabond},
  year={2026},
  howpublished={\url{https://huggingface.co/VoltageVagabond/spam-classifier-mlx}},
  note={ENGT 375 — Applied Machine Learning, Old Dominion University, Spring 2026}
}