Model Card
This model is fine-tuned version of google/functiongemma-270m-it model for mobile action function calling tasks.
Intended Use
Handles function-calling style mobile actions such as creating calendar events, sending emails, adding contacts, showing maps, managing Wi‑Fi, and toggling the flashlight, based on the google/mobile-actions dataset.
Model Details
- Base Model: google/functiongemma-270m-it
- Fine-tuning: SFT with LoRA
- Dataset: google/mobile-actions
- Params: ~270M (base) + LoRA merged into final weights
Quick Start
pip install torch transformers datasets accelerate huggingface_hub
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_id = "dousery/functiongemma-mobile-actions"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16 if device == "cuda" else torch.float32,
device_map="auto" if device == "cuda" else None,
trust_remote_code=True,
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
if device == "cpu":
model = model.to(device)
dataset = load_dataset("google/mobile-actions", split="train")
text = tokenizer.apply_chat_template(
dataset[0]["messages"][:2],
tools=dataset[0]["tools"],
tokenize=False,
add_generation_prompt=True,
).removeprefix("<bos>")
inputs = tokenizer(text, return_tensors="pt").to(device)
with torch.no_grad():
_ = model.generate(
**inputs,
max_new_tokens=256,
streamer=TextStreamer(tokenizer, skip_prompt=True),
top_p=0.95,
top_k=64,
temperature=1.0,
)
Training Summary
- Frameworks: Unsloth + TRL, PyTorch 2.9.1, Transformers 4.57.3
- Steps: 100 (SFT with LoRA, then merged)
- Effective Batch Size: 8 (bs=4, grad accum=2)
- LR / Scheduler: 2e-4, linear
- LoRA: r=16, alpha=16, dropout=0, ~3.8M trainable params
- Seq Len: 4096
- Hardware: NVIDIA H100 80GB on Modal
- Final Train Loss: 0.2408 | Eval Loss: ~0.0129
Limitations
- Trained for only 100 steps; niche mobile-action domain.
- Datetime formats can drift slightly.
- Best on GPU for speed; CPU works but slower.
Citation
@misc{functiongemma-mobile-actions,
title={FunctionGemma Mobile Actions - Merged for Mobile Function Calling},
author={dousery},
year={2025},
howpublished={\url{https://huggingface.co/dousery/functiongemma-mobile-actions}}
}
License
Apache-2.0 (inherits base model license).