Qwen3.5-4B-NL2SH

Model Card: Qwen3.5-4B-NL2SH

This model is a fine-tuned version of Qwen3.5-4B, optimized for converting natural language instructions into functional Linux shell commands.

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IMPORTANT: The model is still under evaluation, so be carefull when using it and always verify its output!

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Model Details:

• Developed by: andreagalasso99
• License: apache-2.0
• Finetuned from model : unsloth/Qwen3.5-4B
• Language: en

tags:

• text-generation-inference
• transformers
• unsloth
• qwen3_5

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Intended Use

Direct Use

The model is designed to assist users in generating shell commands from natural language descriptions. It is particularly useful for:

• Automating routine system administration tasks.
• Helping beginners learn Linux command-line syntax.
• Quick reference for complex flags and piping logic.

Out-of-Scope Use

• Generating malicious scripts or exploits.
• Executing commands automatically without human verification (Always review the output before running it in a terminal).
• Complex scripting in languages other than Shell (unless specified).

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Quick Start

You can use the code below to run inference with the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "andreagalasso99/Qwen3.5-4B-NL2SH"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

SYSTEM_PROMPT = """You are a Linux System Automator. \
Your goal is to convert natural language instructions into precise, \
safe Bash commands. Given the following instruction, \
output a JSON object with two keys: 'command' (the string to execute) \
and 'safety_warning' (a string describing eventual risks, or 'none')."""

def generate_command(user_instruction):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_instruction},
    ]
    
    # Apply ChatML template
    prompt = tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
    )

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id
    )
    
    # Decode and extract only the new tokens
    decoded_output = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
    return decoded_output.strip()

# Example Usage
instruction = "Find all files in /home/user/ that end with .txt and count them."
response = generate_command(instruction)
print(response)

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Training Data

The model was trained on the NL2SH-ALPACA dataset ((https://huggingface.co/datasets/abandonedmonk/NL2SH-ALPACA). This dataset follows the Alpaca instruction format:

• Instruction: The natural language task.
• Input: (Optional) Context for the command.
• Output: The corresponding Shell command.

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Training Details

Hyperparameters

• Epochs: 2
• Batch Size: 64
• Learning Rate: e.g., 2e-4
• Optimizer: AdamW
• Weight Quantization: 4-bit (NF4)
• Precision: Float16

Tools & Frameworks

• PEFT/LoRA
• Unsloth
• Hugging Face Transformers
• TRL

Hardware

• Nvidia A100