Qwen3-4B Bash Tool Calling LoRA

This model is a LoRA fine-tune of Qwen3-4B trained to predict bash tool calls from natural language instructions.

Purpose

The goal of this project was to learn and understand how modern AI tool calling works under the hood by training a language model to map natural language requests into structured tool invocations.

The model predicts bash commands rather than executing them directly.

Example:

Input:

What is my system information?

Output:

<tool_call>
{"name":"bash","arguments":{"command":"uname -a"}}
</tool_call>

Training Data

The dataset contains synthetic bash tool-calling examples generated using Llama 3.2.

Supported command categories:

• Navigation

  • pwd
  • ls
  • ls -la

• System Information

  • whoami
  • hostname
  • uname -a

• File Creation

  • mkdir
  • touch

Examples are formatted as tool-calling conversations.

Base Model

Qwen/Qwen3-4B

Fine-Tuning Method

LoRA (PEFT)

Configuration:

• Rank (r): 16
• Alpha: 32
• Dropout: 0.05

Evaluation

Command prediction accuracy:

100%

Intended Use

This model is intended for:

• Learning tool calling
• Educational projects
• Agent experimentation
• Bash command prediction

This model is not intended for production deployment or unrestricted command execution.

Author

Mazamesso Meba