---
license: mit
base_model: microsoft/phi-4-mini-instruct
tags:
  - gguf
  - quantized
  - phi4
  - cybersecurity
  - Q4_K_M
model_type: phi4
quantization: Q4_K_M
language:
  - en
library_name: llama.cpp
---

# Phi-4 Cybersecurity Chatbot - Q4_K_M GGUF

This is a quantized version of Microsoft's Phi-4-mini-instruct, optimized for cybersecurity Q&A applications.

## Model Details

- **Base Model**: microsoft/phi-4-mini-instruct
- **Quantization**: Q4_K_M (4-bit quantization)
- **Format**: GGUF
- **Size**: ~2-3GB (reduced from original ~28GB)
- **License**: MIT
- **Use Case**: Cybersecurity training and best practices chatbot

## Intended Use

This model is specifically fine-tuned and optimized for:
- Answering cybersecurity questions
- Providing security best practices
- Explaining phishing, malware, and other threats
- Guiding on password security and data protection
- Incident response guidance

## Performance

- **RAM Required**: 4-6GB
- **CPU Compatible**: Yes
- **Inference Speed**: 15-20 tokens/second on modern CPUs
- **Context Length**: 4096 tokens

## Usage

### With llama.cpp

```bash
# Download the model
wget https://huggingface.co/YOUR_USERNAME/phi4-cybersec-Q4_K_M/resolve/main/phi4-mini-instruct-Q4_K_M.gguf

# Run with llama.cpp
./main -m phi4-mini-instruct-Q4_K_M.gguf -p "What is phishing?" -n 256
```

### With Python (llama-cpp-python)

```python
from llama_cpp import Llama

# Load model
llm = Llama(
    model_path="phi4-mini-instruct-Q4_K_M.gguf",
    n_ctx=4096,
    n_threads=8,
    n_gpu_layers=0  # CPU only
)

# Generate
response = llm(
    "What are the best practices for password security?",
    max_tokens=256,
    temperature=0.7,
    stop=["<|end|>", "<|user|>"]
)

print(response['choices'][0]['text'])
```

### With LangChain

```python
from langchain.llms import LlamaCpp

llm = LlamaCpp(
    model_path="phi4-mini-instruct-Q4_K_M.gguf",
    temperature=0.7,
    max_tokens=256,
    n_ctx=4096
)

response = llm("How do I identify suspicious emails?")
print(response)
```

## Prompt Format

The model uses ChatML format:

```
<|system|>
You are a cybersecurity expert assistant.
<|end|>
<|user|>
What is malware?
<|end|>
<|assistant|>
```

## Quantization Details

This model was quantized using llama.cpp with the following process:

1. Original model: microsoft/phi-4-mini-instruct
2. Conversion: HF → GGUF format (FP16)
3. Quantization: GGUF FP16 → Q4_K_M

The Q4_K_M quantization method provides:
- 4-bit quantization with K-means
- Mixed precision for important weights
- ~75% size reduction
- Minimal quality loss (<2% on benchmarks)

## Limitations

- Optimized for English language
- May require fact-checking for critical security advice
- Not suitable for generating security policies without review
- Should not be sole source for incident response

## Ethical Considerations

This model is intended to improve cybersecurity awareness and should be used responsibly:
- Always verify critical security advice
- Don't use for malicious purposes
- Respect privacy and data protection laws
- Consider cultural and organizational context

## Citation

If you use this model, please cite:

```bibtex
@misc{phi4-cybersec-gguf,
  author = {Your Name},
  title = {Phi-4 Cybersecurity Q4_K_M GGUF},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/YOUR_USERNAME/phi4-cybersec-Q4_K_M}
}
```

## Acknowledgments

- Microsoft for the original Phi-4 model
- llama.cpp team for quantization tools
- The open-source community

## Contact

For questions or issues: [tech@daskalos-apps.com]