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
```

rudycaz commited on Mar 4

Commit

7149d3e

verified ·

1 Parent(s): 810c150

Update README.md

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Files changed (1) hide show

README.md +66 -19

README.md CHANGED Viewed

@@ -1,31 +1,78 @@
 ---
-language: en
-library_name: mlx
 tags:
-- mlx
 pipeline_tag: text-generation
 ---
-# rudycaz/qwen3-4b-phishing-detection
-## Use with mlx
-```bash
-pip install mlx-lm
-```
-```python
-from mlx_lm import load, generate
-model, tokenizer = load("rudycaz/qwen3-4b-phishing-detection")
-prompt = "hello"
-if tokenizer.chat_template is not None:
-    messages = [{"role": "user", "content": prompt}]
-    prompt = tokenizer.apply_chat_template(
-        messages, add_generation_prompt=True, return_dict=False,
-    )
-response = generate(model, tokenizer, prompt=prompt, verbose=True)
-```

 ---
+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))