---
base_model: mlx-community/Phi-3.5-mini-instruct-4bit
tags:
  - mlx
  - mlx-lm
  - phi-3.5
  - peft
  - lora
  - phishing
  - cybersecurity
language:
  - en
pipeline_tag: text-generation
---

# phi35-phish-mlx

This repository contains an **Apple MLX**-format phishing-focused model derived from **Phi-3.5 Mini Instruct (4-bit)**. It is intended to help classify suspicious emails and support security review workflows.

## What’s in this repo

This repo is meant to be used in one of these ways:

- **Fused model** (base + adapter merged into a single MLX model directory), OR
- **Adapter-only** (LoRA adapter weights) to be applied on top of the base model locally

If you are unsure which you uploaded, check the repo file list:
- Fused model typically includes MLX weights + tokenizer/config files for direct inference.
- Adapter-only typically includes adapter weight files/config and requires the base model separately.

## Base model

- `mlx-community/Phi-3.5-mini-instruct-4bit`

## Dataset

This model was fine-tuned for phishing detection using a Kaggle phishing email dataset:

- Kaggle dataset: **“phishing-email-dataset”** (naserabdullahalam)  
  https://www.kaggle.com/datasets/naserabdullahalam/phishing-email-dataset

> If you trained Phi-3.5 on a different Kaggle dataset, replace the link above with the exact dataset URL you used so the citation is accurate.

## Intended behavior

Given an email, the intended output is a single label:
- `PHISHING`
- `LEGIT`

Example prompt format:
```text
You are a security assistant. Classify the following email as PHISHING or LEGIT.

EMAIL:
<paste email here>

Answer with exactly one word: PHISHING or LEGIT.


pip install -U mlx-lm huggingface_hub

from mlx_lm import load, generate

# Option A: load this repo directly (if fused model is uploaded)
MODEL_ID = "rudycaz/phi35-phish-mlx"

model, tokenizer = load(MODEL_ID)

prompt = """You are a security assistant. Classify the following email as PHISHING or LEGIT.

EMAIL:
Subject: Verify your account
Body: Please click the link below to verify...

Answer with exactly one word: PHISHING or LEGIT.
"""

print(generate(model, tokenizer, prompt, max_tokens=8))