---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
library_name: peft
---

Model Card for defense-model-v1.0

This model is a LoRA fine-tuned version of the unsloth/meta-llama-3.1-8b-bnb-4bit model, adapted to handle defense and security-related instructions and insights. It aims to provide coherent and contextually relevant responses in the domain of defense strategies, technologies, military cooperation, and related geopolitical factors.

Model Details

Model Description

defense-model-v1.0 is a fine-tuned Large Language Model (LLM) focusing on the defense and security sector. It is particularly geared towards understanding and generating text around modern defense operations, emerging security threats, alliance-building, interoperability among militaries, and advanced military technologies such as UAVs, AI-driven systems, and directed-energy weapons.

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: bayrameker
Model type: Causal Language Model (LoRA fine-tuned LLM)
Language(s) (NLP): Primarily English
License: [More Information Needed] (Check the base model’s license and follow it.)
Finetuned from model [optional]: unsloth/meta-llama-3.1-8b-bnb-4bit

Model Sources [optional]

Repository: https://huggingface.co/bayrameker/defense-model-v1.0
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be used directly for tasks like:

Summarizing articles or documents related to defense strategies.
Explaining the role of new defense technologies.
Discussing global security environments, alliance structures, and challenges facing modern militaries.

Downstream Use [optional]

Fine-tuning further for more specific defense-related QA systems.
Integrating into simulation environments or decision-support tools for military analysts.

Out-of-Scope Use

The model is not intended for real-time critical defense decision-making.
It should not be used as a source of verified intelligence or for making strategic military decisions without expert validation.
Misuse in generating disinformation or sensitive military plans is discouraged.

Bias, Risks, and Limitations

The model’s knowledge may be limited by its training data and could be outdated regarding current events.
Cultural biases or geopolitical biases may appear if the base data had imbalances.
It may produce speculative or hallucinated content, especially when asked about classified or extremely recent events.

Recommendations

Users should:

Verify critical information from reliable defense and security sources.
Be aware of potential biases and incomplete knowledge.
Not rely solely on the model for sensitive defense-related insights.

How to Get Started with the Model

A quick start example (using Transformers):

!pip install transformers accelerate sentencepiece bitsandbytes==0.45.0

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "bayrameker/defense-model-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Explain the importance of interoperability among allied forces."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Sample Outputs

Below are sample instructions, inputs, and responses generated by the model. These were tested after the fine-tuning process:

Example 1

Instruction: Explain the role of UAVs (drones) in modern defense operations.

Input: Focus on their use in reconnaissance and precision strikes.

Response:

Unmanned aerial vehicles, or drones, are a key part of the Defense Department's strategy to achieve overmatch against near-peer adversaries. They enable the U.S. military to gather intelligence and conduct strikes from a safe distance, reducing the risk to U.S. and allied forces. Drones also allow for more frequent and persistent coverage, and their small size allows them to fly in areas where manned aircraft can't go. ...

Example 2

Instruction: Summarize the main challenges facing NATO allies in the current global security environment.

Input: Consider emerging threats like cyber warfare and disinformation campaigns.

Response:

The NATO Allies have a range of challenges in the current security environment. These include the need to adapt to emerging threats and to build resilience against them. One challenge is to maintain a strong defense posture in the face of growing threats from Russia. Another challenge is to address the threat posed by terrorist organizations...

Example 3

Instruction: Identify key defense technologies that will shape the battlefield in the next decade.

Input: Include AI-driven decision support systems and directed-energy weapons.

Response:

The U.S. military and its allies will have to be prepared to fight against a wide array of threats... The Pentagon is developing a new generation of technologies that will give its troops an edge. Artificial Intelligence ... Directed-Energy Weapons ...

Example 4

Instruction: Discuss how integrated air defense systems can deter adversarial drone swarms.

Input: Focus on sensor fusion, counter-UAV tactics, and multi-layered defense.

Response:

The threat posed by unmanned aerial systems, or drones, has grown rapidly... One key aspect of countering drone swarms is sensor fusion... In addition, counter-UAV tactics such as jamming, laser-based systems, and kinetic interceptors can be used to disrupt or destroy drones... multi-layered defense approaches...

Example 5

Instruction: Analyze the importance of alliance-building and interoperability among friendly nations.

Input: Consider recent multinational exercises and joint defense initiatives.

Response:

As the world's sole superpower, the U.S. military is responsible for maintaining security and stability across the globe. As such, it is critical that the United States maintain close ties with its allies and partners. ... Recent multinational exercises have demonstrated the importance of collaboration among friendly nations...

These responses show that the model provides contextually relevant defense-related answers, referencing UAVs, NATO challenges, future technologies, and the value of alliances and interoperability.

Training Details

Training Data

Fine-tuned on a custom dataset (output.jsonl) composed of defense/security-related instructions, inputs, and outputs.

Training Procedure

LoRA fine-tuning applied to the unsloth/meta-llama-3.1-8b-bnb-4bit base model.
Mixed-precision and parameter-efficient tuning were likely used.

Evaluation

No formal benchmark scores provided.
Qualitative examination suggests reasonable coherence and domain relevance.

Environmental Impact

[More Information Needed]

Technical Specifications [optional]

Base model: LLaMA 3.1 variant (8B parameters, 4-bit quantized)
LoRA for parameter-efficient fine-tuning

Citation [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.13.2