Qwen3 0.6B Bash Experiment Model (Merged)
Overview
This model is a merged fine-tune of Qwen3-0.6B trained to predict bash tool calls from natural language requests.
The objective of this project was to understand how modern AI tool calling systems work under the hood by building a custom dataset, fine-tuning a language model, and deploying it as an API.
This repository contains a standalone merged model and does not require PEFT or LoRA loading at runtime.
Base Model
Qwen/Qwen3-0.6B
Training Method
The model was trained using:
- Supervised Fine-Tuning (SFT)
- LoRA
- PEFT
- Transformers
- TRL
The adapter was later merged into the base model to create a standalone deployment-ready model.
Dataset
Dataset size:
- 1,658 training examples
Supported command categories:
Navigation
- pwd
- ls
- ls -la
System Information
- whoami
- hostname
- uname -a
File Creation
- mkdir
- touch
Training examples consisted of:
Natural Language Request
↓
Tool Call
Example:
User:
what is my system information
Model:
<tool_call>
{"name":"bash","arguments":{"command":"uname -a"}}
</tool_call>
Evaluation
Evaluation Accuracy:
1.0
Example:
Prompt:
what is my systems information
Output:
<tool_call>
{"name":"bash","arguments":{"command":"uname -a"}}
</tool_call>
Intended Use
This model is intended for:
- Tool calling experiments
- AI agent development
- Bash command prediction
- Function calling research
- Educational projects
Limitations
The model was trained on a small command set and is not intended to be a general purpose bash assistant.
It predicts commands only from the categories present in the training data.
Author
Mazamesso Meba
GitHub: https://github.com/mjpsm05
Hugging Face: https://huggingface.co/mjpsm