qwen3-0.6b-terminal-instruct-lora

Qwen3-0.6B Terminal Command Generator - LoRA Adapters

This repository contains LoRA adapters for generating terminal commands from natural language instructions.

Model Description

• Base Model: Qwen/Qwen3-0.6B
• Fine-tuning Method: QLoRA (4-bit quantization + LoRA)
• Task: Natural language to terminal command generation
• Supported OS: Linux, Windows, macOS

Performance

Quick Start

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model and apply LoRA adapters
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "Eng-Elias/qwen3-0.6b-terminal-instruct-lora")
tokenizer = AutoTokenizer.from_pretrained("Eng-Elias/qwen3-0.6b-terminal-instruct-lora")

# Generate command
def generate_command(instruction, os_tag="[LINUX]"):
    prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{os_tag}\n\n### Response:\n"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=100, do_sample=False)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("### Response:")[-1].strip()

# Example usage
print(generate_command("List all files including hidden ones", "[LINUX]"))
# Output: ls -a

Supported Commands

The model can generate commands for various tasks including:

• File Operations: list, copy, move, delete, find files
• Directory Operations: create, remove, navigate directories
• System Information: disk usage, memory, processes
• Text Processing: grep, sed, awk operations
• Network: ping, curl, wget, netstat
• Compression: tar, zip, gzip operations
• And more...

Input Format

The model uses an Alpaca-style prompt format:

### Instruction:
{natural language description}

### Input:
{OS tag: [LINUX], [WINDOWS], [MAC], or JSON request}

### Response:
{generated command}

OS-Specific Commands

# Linux
generate_command("Show disk usage", "[LINUX]")  # df -h

# Windows
generate_command("Show disk usage", "[WINDOWS]")  # wmic logicaldisk get size,freespace

# macOS
generate_command("Show disk usage", "[MAC]")  # df -h

JSON Output (All OS)

generate_command("Delete file named temp.txt", "Return the command for all operating systems as JSON")
# {"description": "Delete file named temp.txt", "linux": "rm temp.txt", "windows": "del temp.txt", "mac": "rm temp.txt"}

Training Details

• Dataset: Custom terminal command dataset with 10000+ examples
• Training Steps: ~1800
• LoRA Rank (r): 16
• LoRA Alpha: 32
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Learning Rate: 2e-4
• Batch Size: 4
• Gradient Accumulation: 4

Related Models

• Merged Model: Eng-Elias/qwen3-0.6b-terminal-instruct

Experiment Tracking

Training metrics and hyperparameters are logged to Weights & Biases:

• Project: qwen3-terminal-instruct
• Run: qwen3-0.6b-terminal-20251230_2244
• Dashboard: View on W&B

Limitations

• Commands are based on common usage patterns; complex or obscure commands may not be accurate
• The model may occasionally generate slightly different but functionally equivalent commands
• JSON output format is consistent but may vary in structure for edge cases
• Trained primarily on English instructions

License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material

Under the following terms:

• Attribution — You must give appropriate credit
• NonCommercial — You may not use the material for commercial purposes
• ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license

Full license: https://creativecommons.org/licenses/by-nc-sa-4.0/legalcode

Citation

@misc{qwen3-terminal-instruct,
  author = {Eng-Elias},
  title = {Qwen3-0.6B Terminal Command Generator},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Eng-Elias/qwen3-0.6b-terminal-instruct-lora}
}

Acknowledgments

• Qwen Team for the base model
• Hugging Face for the transformers and PEFT libraries