Instructions to use ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B with Transformers:

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

pipe = pipeline("text-generation", model="ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B")
model = AutoModelForCausalLM.from_pretrained("ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B

SGLang

How to use ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B 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 "ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B" \
    --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": "ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B",
		"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 "ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B" \
        --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": "ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B with Docker Model Runner:
```
docker model run hf.co/ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B
```

This repository hosts a fine-tuned derivative model based on unsloth/Qwen3-1.7B.

Licensing & Upstream Attribution

1. Fine-Tuned Weights License

The modified weights and LoRA adapters hosted in this specific repository are released under a Custom End User License Agreement (EULA). Please refer to the accompanying LICENSE.txt file for terms of use, restrictions, and commercial permissions.

2. Upstream Compliance & Disclosures

In accordance with Section 4 of the Apache License 2.0 governing the base model weights:

Base Architecture & Weights: Originally created by the Qwen Team / Alibaba Cloud.
Optimization Layer: Sourced via the optimized Unsloth AI repository.
Modifications: This model was fine-tuned using the Unsloth library on a custom dataset, altering the original parameter behavior.
Original License Integrity: The original baseline terms are respected. A verbatim copy of the upstream Apache 2.0 License has been included in this repository as APACHE_LICENSE.txt.

Training Data & Attribution

This model was trained using dataset assets sourced from:

Dataset: SMS PHISHING DATASET FOR MACHINE LEARNING AND PATTERN RECOGNITION by sandhya mishra, Devpriya Soni via Mendeley Data
License: Licensed under CC BY 4.0

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Safetensors

Model size

2B params

Tensor type

F16

Model tree for ppsub/PhishDetectAI-SMS-SFT-Qwen3-1.7B

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

unsloth/Qwen3-1.7B

Finetuned

(183)

this model