README.md

---
license: apache-2.0
library_name: transformers
tags:
- llama
- llama-3
- finetuned
- cyber-security
- reasoning
- instruction-tuning
- lora
- peft
- text-generation
language:
- en
pipeline_tag: text-generation
base_model: meta-llama/Meta-Llama-3-8B-Instruct
new_version: OpenPathAI/Orbit-3.1-Llama-thinking
---

# Orbit-3-8B-Llama-thinking

**A Fine-tuned Llama 3 for Advanced Cybersecurity Reasoning**

---

## 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

---

## 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 |

---

## 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` |

---

## 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 |

---

### Installation

```bash
pip install transformers torch accelerate
```

Basic Usage

```python
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)
```

---

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
```

---

Recommended Use Cases

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

---

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

---

License

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

---

Developed by OpenPathAI

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