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
```

vanshkamra12 commited on Apr 11

Commit

a950722

verified ·

1 Parent(s): f715937

Update README.md

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README.md +32 -1

README.md CHANGED Viewed

@@ -1,9 +1,30 @@
 # 🛡️ 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)](https://huggingface.co/spaces/vanshkamra12/CyberThreat-Intel-Analyzer)
 **💻 Code & Dataset:** [GitHub Repository](https://github.com/vanshkamra12/CyberThreat-Intel-LLM)
 ---
 ## 🎯 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)
@@ -12,33 +33,43 @@ Feed the model a raw CVE description, CVSS score, and vendor, and it will genera
 - **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
 ```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))

+---
+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)](https://huggingface.co/spaces/vanshkamra12/CyberThreat-Intel-Analyzer)
 **💻 Code & Dataset:** [GitHub Repository](https://github.com/vanshkamra12/CyberThreat-Intel-LLM)
 ---
 ## 🎯 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)
 - **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
 ```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))