license, base_model, tags, pipeline_tag, library_name

license	base_model	tags	pipeline_tag	library_name
gemma	google/gemma-3-1b-it	gemma terminal linux command-generation sft Mica fine-tuned	text-generation	transformers

Gemma 3 1B Terminal Assistant

A fine-tuned version of Google's Gemma 3 1B Instruction Tuned model specialized for terminal command generation.

This model was trained to understand natural language requests and generate safe, minimal terminal commands.

Model Details

Base Model:

• google/gemma-3-1b-it

Fine-tuning Method:

• Supervised Fine-Tuning (SFT)
• Mica fine-tuning
• Mica merged into the base model

Training Dataset:

• mshojaei77/terminal-command-execution-sft

Task:

• Linux terminal commands
• Windows commands
• Shell scripting
• Command explanation
• Safe command generation

Example Usage

Python Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch


model_name = "Paulwalker4884/gemma-3-1b-terminal-assistant"


tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)


messages = [
    {
        "role": "system",
        "content": "You are a safe terminal command assistant."
    },
    {
        "role": "user",
        "content": "Find all python files recursively"
    }
]


inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)


outputs = model.generate(
    inputs,
    max_new_tokens=128,
    temperature=0.2
)


response = tokenizer.decode(
    outputs[0][inputs.shape[-1]:],
    skip_special_tokens=True
)


print(response)

---

Example

Input:

Find all python files recursively

Output:

find . -name "*.py"

Input:

Find all log files modified in the last 7 days and save them

Output:

find . -name "*.log" -mtime -7 > recent_logs.txt

Safety

This model is trained to avoid blindly generating destructive commands.

For potentially dangerous operations, users should verify commands before execution.

Limitations

• The model may generate incorrect commands.
• Always review generated commands before running them.
• Performance depends on the quality of the input prompt.

Training Information

Dataset size:

• Train: 31,429 examples
• Evaluation: 239 examples

Frameworks:

• Transformers
• TRL
• PEFT
• PyTorch

BY : Yasin Keykha