README.md

---
base_model: google/functiongemma-270m-it
library_name: mobiletransformers
pipeline_tag: text-generation
tags:
  - mobiletransformers
  - onnx
  - on-device
  - android
  - lora
  - int4
---

![MobileTransformers](mobiletransformers_banner.png)

# google/functiongemma-270m-it — MobileTransformers package

On-device (Android) package exported from **google/functiongemma-270m-it** with MobileTransformers.

## What this package can do
- `core` — shared files every other group needs
- `inference` — generate or score on device
- `train` — **fine-tune on device**, then merge the adapter back into the base weights

## Fine-tuning method
- **lora** — LoRA — low-rank adapters on the attention projections.
- Rank: `8`
- Adapted modules: `q_proj`, `v_proj`

## Provenance
- Base model: `google/functiongemma-270m-it`
- Selected task: `text-generation-with-past`
- Quantization: int4
- Toolchain: optimum-onnx 0.1.0, transformers 4.57.6, ort-training 1.23.0+cpu

## Licenses
- Framework: not declared in this package — see the repository
- Base model weights: see the base model above (this package redistributes an export of those weights, so their terms govern its contents)

## Android runtime
- Minimum API: 28
- Required ABIs: any

## Variants

| id | EP | quant | engines | features | min API | rec. RAM (MB) |
| --- | --- | --- | --- | --- | --- | --- |
| cpu-int4 | cpu | int4 | native | core, inference, train | 28 | — |

Default variant: `cpu-int4`.

## Running this model

This is a **MobileTransformers package**, not a plain Hugging Face model: it is a manifest plus per-variant ONNX stages and a weight-handoff map. `transformers`, `optimum` and plain `onnxruntime` cannot load it. Use the framework:

**https://github.com/martinkorelic/mobiletransformers**

```kotlin
// Android — pulls, verifies and installs on first use.
val model = MobileTransformers.fromPretrained(
    context = context,
    repoId  = "mobiletransformers/functiongemma-270m-it",
)
```

```bash
# Host — download and inspect the package without a device.
mobiletransformers pull --repo-id mobiletransformers/functiongemma-270m-it
```

## Citation

If you are using this framework for your own work, please cite:

```bibtex
@misc{mobiletransformers2025,
  author       = {Koreli\v{c}, Martin and Pejovi{\'c}, Veljko},
  title        = {MobileTransformers: An On-Device LLM PEFT Framework for Fine-Tuning and Inference},
  year         = {2025},
  howpublished = {\url{https://gitlab.fri.uni-lj.si/lrk/mobiletransformers}}
}
```