Instructions to use ssingh20062000/security-copilot-qwen2.5-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ssingh20062000/security-copilot-qwen2.5-3b with Transformers:

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

pipe = pipeline("text-generation", model="ssingh20062000/security-copilot-qwen2.5-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ssingh20062000/security-copilot-qwen2.5-3b")
model = AutoModelForCausalLM.from_pretrained("ssingh20062000/security-copilot-qwen2.5-3b", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use ssingh20062000/security-copilot-qwen2.5-3b with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ssingh20062000/security-copilot-qwen2.5-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ssingh20062000/security-copilot-qwen2.5-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ssingh20062000/security-copilot-qwen2.5-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ssingh20062000/security-copilot-qwen2.5-3b

SGLang

How to use ssingh20062000/security-copilot-qwen2.5-3b 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 "ssingh20062000/security-copilot-qwen2.5-3b" \
    --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": "ssingh20062000/security-copilot-qwen2.5-3b",
		"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 "ssingh20062000/security-copilot-qwen2.5-3b" \
        --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": "ssingh20062000/security-copilot-qwen2.5-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ssingh20062000/security-copilot-qwen2.5-3b with Docker Model Runner:
```
docker model run hf.co/ssingh20062000/security-copilot-qwen2.5-3b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Security Co-pilot (security-copilot-qwen2.5-3b)

A 3B-parameter model fine-tuned to triage suspicious messages. Give it an email, URL, SMS message, or call transcript and it answers with a verdict: PHISHING, SCAM, SPAM, or SAFE. It follows the verdict with a one- or two-sentence reason that cites the evidence.

It is small enough to run on a laptop once quantized, so messages can be checked without sending them to an external API.

Results

Validation set, 400 held-out examples:

Metric	Value
Accuracy	95.5%
Macro F1	95.4%
Unparseable answers	0

Input type	Accuracy
conversation	100.0%
email	97.2%
sms	89.7%
url	91.2%

Label	Precision	Recall	F1	Support
PHISHING	96.6%	91.4%	93.9%	93
SCAM	98.9%	97.9%	98.4%	96
SPAM	96.2%	92.6%	94.3%	54
SAFE	92.7%	97.5%	95.0%	157

These are validation results. Evaluation on the separate test set, and a comparison with the untuned base model, are reported separately.

How to use

import json, torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "ssingh20062000/security-copilot-qwen2.5-3b"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")
cfg = json.load(open(hf_hub_download(repo, "prompt_config.json")))

message = "Your bank KYC has expired. Update now at kyc-update-verify.in/login or your account will be blocked."
messages = [
    {"role": "system", "content": cfg["system_prompt"]},
    {"role": "user", "content": cfg["user_message_template"].format(input_type="sms", input=message)},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
output = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))   # "Verdict: ..."  (then "Reason: ...")

Use the exact prompt in prompt_config.json, and format inputs the way the training data was formatted: emails start with From: / Subject: lines followed by a blank line and the body, and URLs are given without http:// or https://.

The LoRA adapter on its own is in the adapter/ folder, for use with peft on top of Qwen/Qwen2.5-3B-Instruct.

Training data

About 18,500 examples after cleaning, balanced across four input types:

Emails: Nazario (phishing), Nigerian Fraud (advance-fee scams), CEAS 2008, SpamAssassin, Enron-Spam, and Ling-Spam (spam and legitimate mail)
URLs: a public phishing-site URL dataset
SMS and call transcripts: SMS spam plus translated scam-call and everyday conversation transcripts

Cleaning went beyond de-duplication. Collection artefacts that would let a model identify the source dataset instead of the threat were removed or randomised: collector mailbox addresses, anonymisation tokens, template names, currency words that differed by class, transcription marks, MIME boilerplate, and a length difference between classes. Near-duplicates were removed, and whole templates and URL domains were kept within a single split to prevent leakage.

Training procedure

Setting	Value
Method	QLoRA (4-bit NF4 base, LoRA adapters on all linear layers)
LoRA r / alpha / dropout	16 / 32 / 0.05
Learning rate	0.0002 (cosine schedule)
Epochs	1
Effective batch size	16
Max sequence length	1536 tokens
Training examples	14,806
Hardware	1x NVIDIA T4 (Kaggle), fp16

Loss was computed only on the answer tokens, not the prompt.

Limitations

English only. Non-English messages were removed from the training data.
SMS scams are labelled SPAM. The SMS source data doesn't separate scams from other spam, so scam texts (fake prizes, KYC updates) come out as SPAM rather than SCAM.
Dated and skewed data. Most emails are from 2002 to 2008, apart from the phishing set (2015 to 2022). Legitimate emails come mostly from technical mailing lists, so genuine bank alerts, OTPs, and delivery notices may be flagged incorrectly.
No live signals. It sees only the text: it cannot check whether a domain is newly registered, look at attachments, or verify a sender.
Adversarial inputs. Messages written to fool classifiers can evade it.

Use it as a first-pass assistant alongside other controls, not as the only line of defence.

Licence

This model is a derivative of Qwen2.5-3B-Instruct and is released under the Qwen Research License, which restricts commercial use. Read the licence before using it.

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for ssingh20062000/security-copilot-qwen2.5-3b

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1660)

this model

Quantizations

1 model