Instructions to use vanshkamra12/CyberSecurity-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanshkamra12/CyberSecurity-Model with Transformers:

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

pipe = pipeline("text-generation", model="vanshkamra12/CyberSecurity-Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vanshkamra12/CyberSecurity-Model")
model = AutoModelForCausalLM.from_pretrained("vanshkamra12/CyberSecurity-Model")
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

vLLM

How to use vanshkamra12/CyberSecurity-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanshkamra12/CyberSecurity-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vanshkamra12/CyberSecurity-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vanshkamra12/CyberSecurity-Model

SGLang

How to use vanshkamra12/CyberSecurity-Model 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 "vanshkamra12/CyberSecurity-Model" \
    --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": "vanshkamra12/CyberSecurity-Model",
		"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 "vanshkamra12/CyberSecurity-Model" \
        --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": "vanshkamra12/CyberSecurity-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use vanshkamra12/CyberSecurity-Model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vanshkamra12/CyberSecurity-Model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vanshkamra12/CyberSecurity-Model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vanshkamra12/CyberSecurity-Model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="vanshkamra12/CyberSecurity-Model",
    max_seq_length=2048,
)

Docker Model Runner
How to use vanshkamra12/CyberSecurity-Model with Docker Model Runner:
```
docker model run hf.co/vanshkamra12/CyberSecurity-Model
```

CyberSecurity-Model / README.md

vanshkamra12

Update README.md

a950722 verified about 1 month ago

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metadata

base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - mistral
  - cybersecurity
  - threat-intelligence
  - cve
license: apache-2.0
language:
  - en

🛡️ CyberThreat Intel LLM (Phi-3-mini Fine-Tuned)

This is a fine-tuned version of Microsoft's Phi-3-mini-4k-instruct, optimized specifically to act as a Cybersecurity Threat Analyst. It takes raw CVE vulnerability data and generates professional, structured threat intelligence reports.

▶️ Try the Live Demo: CyberThreat Intel Analyzer (Hugging Face Space)

💻 Code & Dataset: GitHub Repository

🎯 What it does

Feed the model a raw CVE description, CVSS score, and vendor, and it will generate a comprehensive report including:

Executive Summary (Plain English explanation)
Technical Analysis (Vectors, complexity, privileges)
Indicators of Compromise (IOCs)
MITRE ATT&CK Mappings
Risk Assessment
Remediation Steps
Detection Rules (YARA/Sigma)

🧠 Model Details

Base Model: Phi-3-mini-4k-instruct (3.8B parameters)
Training Method: QLoRA (4-bit quantization) with Unsloth
Trainable Parameters: 29.8M (0.78% of total)
Training Data: 471 synthetic instruction-tuning pairs generated using Llama 3.1 8B from raw NIST NVD CVE data.
Final Training Loss: 0.337

🚀 How to use in Python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "vanshkamra12/CyberSecurity-Model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map="auto"
)

prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
Analyze the following vulnerability data and produce a structured threat intelligence report.

### Input:
CVE ID: CVE-2024-21762
Description: A out-of-bound write vulnerability in FortiOS SSL VPN allows a remote unauthenticated attacker to execute arbitrary code or commands via specially crafted HTTP requests.
CVSS Score: 9.8 CRITICAL
Vendor: Fortinet

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1000, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))