Function	N	Format	Name	Required	Exact
`create_contact`	43	88.4%	88.4%	88.4%	86.0%
`create_calendar_event`	42	90.5%	90.5%	81.0%	78.6%
`show_map`	38	97.4%	97.4%	97.4%	68.4%
`open_wifi_settings`	20	90.0%	90.0%	90.0%	90.0%
`send_email`	20	100.0%	100.0%	100.0%	95.0%
`turn_on_flashlight`	20	100.0%	100.0%	100.0%	100.0%
`turn_off_flashlight`	17	100.0%	100.0%	100.0%	100.0%

Training

Setting	Value
Base model	`google/gemma-4-E2B-it`
Dataset	`google/mobile-actions`
Training samples	200
Epochs	1
LoRA rank / alpha	16 / 32
Batch / accumulation	1 / 16
Max sequence length	1024
Precision	bfloat16
Hardware	Apple M1 Pro, MPS
Training time	35m47s including merge

Usage

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoProcessor

base_model = "google/gemma-4-E2B-it"
adapter_id = "YOUR_USERNAME/gemma4-e2b-mobile-actions-200-lora"

processor = AutoProcessor.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id)

Examples

This repository includes a runnable PEFT example:

bash

python examples/run_lora.py \
  --adapter-id ClarkBear/gemma4-e2b-mobile-actions-200-lora \
  --prompt "Turn on the flashlight"

You need Hugging Face access to google/gemma-4-E2B-it.

Limitations

This is an experimental small-data LoRA adapter. It was trained on only 200 examples and should be evaluated before use in production or on-device workflows.