Instructions to use rudycaz/qwen3-4b-phishing-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rudycaz/qwen3-4b-phishing-detection with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rudycaz/qwen3-4b-phishing-detection")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rudycaz/qwen3-4b-phishing-detection")
model = AutoModelForCausalLM.from_pretrained("rudycaz/qwen3-4b-phishing-detection", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use rudycaz/qwen3-4b-phishing-detection with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rudycaz/qwen3-4b-phishing-detection with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rudycaz/qwen3-4b-phishing-detection"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rudycaz/qwen3-4b-phishing-detection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rudycaz/qwen3-4b-phishing-detection

SGLang

How to use rudycaz/qwen3-4b-phishing-detection with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rudycaz/qwen3-4b-phishing-detection" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rudycaz/qwen3-4b-phishing-detection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rudycaz/qwen3-4b-phishing-detection" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rudycaz/qwen3-4b-phishing-detection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rudycaz/qwen3-4b-phishing-detection with Docker Model Runner:
```
docker model run hf.co/rudycaz/qwen3-4b-phishing-detection
```

qwen3-4b-phishing-detection

This repository contains a phishing-focused model derived from Qwen3-4B. It is intended to support defensive workflows by labeling email content as PHISHING or LEGIT.

Depending on what you uploaded, this repo is either:

Adapter-only (LoRA/QLoRA): requires the base model + this adapter, or

Merged/fused model: can be loaded directly without separately applying an adapter.

Base model

Qwen/Qwen3-4B (use the exact base model you fine-tuned from)

Dataset

This model was fine-tuned using the following dataset:

Naser Abdullah Alam — “Phishing Email Dataset” (Kaggle)
https://www.kaggle.com/datasets/naserabdullahalam/phishing-email-dataset

Intended behavior

Given an email, the intended output is exactly one label:

PHISHING
LEGIT

Recommended prompt format:

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 torch transformers peft accelerate bitsandbytes safetensors

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "rudycaz/qwen3-4b-phishing-detection"  # this repo

tok = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)

email_text = """Subject: Verify your account
Body: Please click the link below to verify...
"""

prompt = (
    "You are a security assistant. Classify the following email as PHISHING or LEGIT.\n\n"
    f"EMAIL:\n{email_text}\n\n"
    "Answer with exactly one word: PHISHING or LEGIT."
)

inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=4)
print(tok.decode(out[0], skip_special_tokens=True))

Downloads last month: 16

Safetensors

Model size

0.6B params

Tensor type

BF16

U32

Model tree for rudycaz/qwen3-4b-phishing-detection

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Qwen/Qwen3-4B-Base

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Qwen/Qwen3-4B

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