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

Component	Detail
Base Model	meta-llama/Meta-Llama-3-8B-Instruct
Model Type	Causal Language Model (Decoder-only)
Total Parameters	8.07 Billion
Trained Parameters	41.9 Million (0.52%)
Architecture	Transformer-based
Context Length	8.192 tokens (during training)
Language	English

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Training Configuration

Parameter	Value
Training Epochs	3
Fine-tuning Method	LoRA (Low-Rank Adaptation)
Precision	FP16
Learning Rate	2e-4
Batch Size	2 (per device)
Gradient Accumulation	16
Optimizer	AdamW
Warmup Steps	100
Max Gradient Norm	1.0

LoRA Configuration

Parameter	Value
LoRA Rank (r)	16
LoRA Alpha	32
LoRA Dropout	0.05
Bias	None
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

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Dataset Distribution

Domain	Description
programming_general	General programming and secure code writing
soc_threat_intel	SOC operations and threat intelligence
malware_analysis	Malware triage and analysis
security_docs	Security documentation and best practices
exploit_development	Exploit research and vulnerability analysis
tool_calls	Security tool usage and automation

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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.