Instructions to use bixby404/cybersecurity-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bixby404/cybersecurity-assistant with Transformers:

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

pipe = pipeline("text-generation", model="bixby404/cybersecurity-assistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bixby404/cybersecurity-assistant", dtype="auto")

PEFT
How to use bixby404/cybersecurity-assistant with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bixby404/cybersecurity-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bixby404/cybersecurity-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bixby404/cybersecurity-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bixby404/cybersecurity-assistant

SGLang

How to use bixby404/cybersecurity-assistant 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 "bixby404/cybersecurity-assistant" \
    --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": "bixby404/cybersecurity-assistant",
		"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 "bixby404/cybersecurity-assistant" \
        --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": "bixby404/cybersecurity-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bixby404/cybersecurity-assistant with Docker Model Runner:
```
docker model run hf.co/bixby404/cybersecurity-assistant
```

cybersecurity-assistant / README.md

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metadata

library_name: transformers
tags:
  - peft
  - qlora
  - lora
  - cybersecurity
  - text-generation
base_model: microsoft/Phi-3-mini-4k-instruct

Model Card for Cybersecurity Assistant Sandbox

This model is a proof-of-concept conversational LLM fine-tuned to provide clean, direct definitions and concept explanations for machine learning architectures, NLP principles, and cybersecurity fundamentals.

Model Details

Model Description

This is a Parameter-Efficient Fine-Tuning (PEFT) adapter layer built using Low-Rank Adaptation (LoRA). It has been adapted from a quantized 4-bit base LLM to deliver specific, highly structured answers to technical instructions while minimizing verbose internet filler text.

Developed by: bixby404
Model type: Causal Language Model (LoRA Adapter)
Language(s) (NLP): English
Finetuned from model: microsoft/Phi-3-mini-4k-instruct

Model Sources

Repository: https://huggingface.co/bixby404/cybersecurity-assistant
Demo: Hosted via Google Colab and Gradio UI Sandbox

Uses

Direct Use

This model is intended to be used directly as a lightweight technical assistant. It excels at answering explicit, structured definitions matching the instructions provided in its training regimen.

Out-of-Scope Use

This model is not designed for:

Writing production-grade exploit scripts or malicious software.
Analyzing enterprise system log traffic in real-time.
Serving as a standalone automated incident response tool without human oversight.

Bias, Risks, and Limitations

Due to the compact dataset size (100 rows), the model acts primarily as a behavior/style filter rather than an extensive new knowledge repository. It carries a structural dependency on its base architecture (Phi-3) for general reasoning and might display style regression if prompted with heavily out-of-domain scenarios.

Recommendations

Users should verify any specialized security remediations or definitions generated against official standards (such as NIST or MITRE ATT&CK frameworks) before executing them in live corporate lab environments.

How to Get Started with the Model

Use the PyTorch execution code below in a standard Google Colab environment containing a T4 GPU runtime to interface with this model adapter:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
from peft import PeftModel

base_model_name = "microsoft/Phi-3-mini-4k-instruct"
hf_adapter_id = "bixby404/cybersecurity-assistant"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

model = PeftModel.from_pretrained(model, hf_adapter_id)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
test_prompt = "### Instruction:\nWhat is the primary function of an LLM?\n\n### Response:\n"

outputs = generator(test_prompt, max_new_tokens=50, return_full_text=False)
print(outputs[0]['generated_text'])