Model description

Intended uses

• Research and education on phishing URL / email text classification
• Prototyping security tooling with explicit human review

Out-of-scope uses

• Sole automated decision-making for blocking users or transactions without review
• Spam campaigns, social engineering, or evading security systems
• Languages or domains far from the training distribution

Inference format

Use this exact instruction and layout (training and eval depend on it):

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### Instruction:
Classify the email or URL as phishing or legitimate.

### Input:
<your email body or URL here>

### Response:

The model should complete ### Response: with phishing or legitimate. Prefer temperature 0 / greedy decoding.

Evaluation (held-out test set)

Metrics on 1,000 held-out test examples (500 per class), evaluated with the training adapter + PEFT (evaluate script, left-padding batch inference). Report before merge; merged weights are expected to match closely.

Confusion matrix (rows = true, columns = predicted):

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precision    recall  f1-score   support

    phishing       1.00      0.94      0.97       500
  legitimate       1.00      0.99      1.00       500

   micro avg       1.00      0.96      0.98      1000
   macro avg       1.00      0.96      0.98      1000
weighted avg       1.00      0.96      0.98      1000

Training data

• Source: Synthetic dataset (5,000 rows, balanced 2,500 / 2,500)
• Columns: model_input (email or URL text), label (phishing | legitimate)
• Split: 80/20 stratified → 4,000 train / 1,000 test

📌 Dataset availability: The training dataset is not publicly released as part of this repository. If you are interested in the data for research collaboration or reproducibility purposes, please contact the authors directly via HuggingFace.

Training procedure

Usage

Transformers

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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "Lovely2209/Qwen2.5-1.5B-Phishing-Email-Detector"

tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

text = "http://paypa1-secure.example/verify?id=abc"
prompt = (
    "### Instruction:\n"
    "Classify the email or URL as phishing or legitimate.\n\n"
    f"### Input:\n{text}\n\n"
    "### Response:\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=8,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )
print(tokenizer.decode(out[0], skip_special_tokens=True))

vLLM (merged — no LoRA)

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export VLLM_USE_FLASHINFER_SAMPLER=0   # if nvcc / CUDA toolkit is not installed

python
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from vllm import LLM, SamplingParams
from vllm.sampling_params import StructuredOutputsParams

repo = "Lovely2209/Qwen2.5-1.5B-Phishing-Email-Detector"

llm = LLM(
    model=repo,
    dtype="bfloat16",
    max_model_len=512,
    enforce_eager=True,
)

prompt = (
    "### Instruction:\n"
    "Classify the email or URL as phishing or legitimate.\n\n"
    "### Input:\n"
    "http://paypa1-secure.example/verify\n\n"
    "### Response:\n"
)

sampling = SamplingParams(
    temperature=0.0,
    max_tokens=8,
    structured_outputs=StructuredOutputsParams(
        choice=["phishing", "legitimate"]
    ),
)

outputs = llm.generate([prompt], sampling)
print(outputs[0].outputs[0].text.strip())

License

This model is released under the Apache License 2.0, consistent with Qwen/Qwen2.5-1.5B-Instruct. See LICENSE and NOTICE in this repository.

This project is not affiliated with, endorsed by, or sponsored by Alibaba Cloud or the Qwen Team.

Citation

If you use this model, please cite the base Qwen2.5 work:

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@misc{qwen2.5,
  title = {Qwen2.5: A Party of Foundation Models},
  url = {https://qwenlm.github.io/blog/qwen2.5/},
  author = {Qwen Team},
  month = {September},
  year = {2024}
}

Fine-tune attribution:

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@misc{qwen25-phishing-email-detector-2026,
  title = {Qwen2.5-1.5B Phishing Email Detector (merged QLoRA SFT)},
  author = {Jagriti Singh and Lovely Kumari},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Lovely2209/Qwen2.5-1.5B-Phishing-Email-Detector}},
  note = {Fine-tuned from Qwen/Qwen2.5-1.5B-Instruct}
}