Welcome to your go-to guide for utilizing the Cyber-risk-llama-3-8B model, a fine-tuned marvel designed to tackle cyber threats and enhance your cybersecurity applications. Let’s embark on this journey to decode its features, intended applications, and practical implementations!

Model Overview

The Cyber-risk-llama-3-8B model is an upgraded version of meta-llama, fine-tuned on the vanessasmlcybersecurity_32k_instruction_input_output dataset. Its primary aim is to analyze and classify cybersecurity data according to NIST taxonomy and IT Risks based on ITC EBA guidelines.

Intended Use

• Intended users: Data scientists and developers in cybersecurity.
• Out-of-scope use cases: Not to be used for medical advice, legal decisions, or critical life systems.

Training Data and Procedure

This model is trained on data focusing on current cybersecurity news and employs sophisticated methodologies to ensure reliability and effectiveness.

• Preprocessing: The text data was tokenized with the tokenizer of meta-llama.
• Hardware: Utilized GPUs with mixed precision (FP16BF16).
• Optimizer: Paged AdamW with a cosine learning rate schedule.
• Epochs: The model underwent training for 1 epoch.
• Batch size: 4 per device.

How to Use the Model

Let’s dive into how to set up and execute the Cyber-risk-llama-3-8B model:

Step-by-Step Implementation

python
model_id = "vanessasmlcyber-risk-llama-3-8b"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

Now that you have loaded the model, it’s time to define your user prompts:

python
example_prompt_1 = "Question: What are the cyber threats present in the article? Explain why.\nArticle: More than one million Brits over the age of 45 have fallen victim to some form of email-related fraud, as the internet supersedes the telephone as the favored channel for scammers, according to Aviva."
example_prompt_2 = "What are the main 5 ITC EBA IT risks?"
messages = [
    {"role": "system", "content": "You are an IT supervisor from a supervisory institution."},
    {"role": "user", "content": example_prompt_2},
]
prompt = pipeline.tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids(eot_id)
]
outputs = pipeline(
    prompt,
    max_new_tokens=500,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.1,
    top_p=0.9,
)
print(outputs[0]['generated_text'][len(prompt):])

This code fetches insights regarding cybersecurity threats based on your provided article.

Understanding the Code: An Analogy

Think of the Cyber-risk-llama-3-8B model as a well-trained cybersecurity consultant. The prompts are like questions that you ask during a consultation. Firstly, you load your consultant (the model) with a simple script, just like scheduling a meeting. Next, you present your scenario (defined prompts) where the consultant analyzes the provided information (the article), gives detailed findings (outputs), and helps you understand the prevailing cyber risks. Both the manner of questioning and the context determine the accuracy and relevance of responses.

Troubleshooting

If you encounter issues while deploying the model, consider the following troubleshooting ideas:

• Ensure your GPU drivers are up-to-date.
• Check the installed versions of necessary libraries (like transformers and torch).
• Validate the templates and prompts for any formatting errors.
• Review the logs for any runtime errors.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Limitations and Bias

While the model proves robust in its domain, it may fall short outside cybersecurity contexts. Be mindful of data biases that could impact predictions.

Conclusion

By leveraging the Cyber-risk-llama-3-8B model, data scientists and developers can significantly enhance their cybersecurity applications. With carefully structured prompts, this model can provide insightful analyses on cyber threats and associated risks, making it an essential tool for the cybersecurity landscape.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.