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 / README.md

rudycaz

Update README.md

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	---
	base_model: Qwen/Qwen3-4B
	tags:
	- transformers
	- peft
	- lora
	- qwen
	- phishing
	- email-security
	- cybersecurity
	language:
	- en
	pipeline_tag: text-generation
	---

	# 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:

	```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 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))