Orbit-3-8B-Llama-thinking

Orbit-3-8B-Llama-thinking

A Fine-tuned Llama 3 for Advanced Cybersecurity Reasoning

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Overview

Orbit-3-8B-Llama-thinking is a language model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct using a cybersecurity reasoning dataset to enhance its analytical reasoning and problem-solving capabilities in the cybersecurity domain.

This model is specifically designed for:

• Malware analysis and threat intelligence
• Secure programming and code writing
• Security documentation and best practices
• Exploit research and vulnerability analysis
• Reasoning for security problem-solving

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Model Architecture

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Training Configuration

LoRA Configuration

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Dataset Distribution

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Installation

pip install transformers torch accelerate

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

MODEL_NAME = "OpenPathAI/Orbit-3-8B-Llama-thinking"

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

question = "Explain about malware and how to prevent it"
prompt = f"### Instruction:\n{question}\n\n### Response:\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = response.replace(prompt, "").strip()

print(response)

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Chat Format

Standard Format

### Instruction:
[Your question or instruction]

### Response:
[The model's answer]

With System Prompt

### System:
[System instruction or context]

### Instruction:
[Your question or instruction]

### Response:
[The model's answer]

Example

Input:

### System:
You are a cybersecurity expert. Provide detailed and accurate information.

### Instruction:
How can SQL injection attacks be prevented?

### Response:

Output:

SQL injection attacks can be prevented through several methods:

1. Use parameterized queries (prepared statements)
2. Validate and sanitize input
3. Escape special characters
4. Use ORM frameworks
5. Apply the principle of least privilege

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Recommended Use Cases

• Cybersecurity education and training
• Security documentation creation
• Code review and secure coding assistance
• Threat intelligence analysis
• Security best practice recommendations

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Responsible Use Guidelines

Guideline Description Educational Use Use for learning and research purposes Defensive Security Help improve security posture Illegal Activities DO NOT use for illegal activities Malware Creation DO NOT use to create malicious software Human Oversight Always verify security advice with experts

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License

This model is licensed under the Apache License 2.0. See LICENSE for more details.

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Developed by OpenPathAI

This model was fine-tuned using LoRA and merged with the base model for ease of use.