---
license: gemma
library_name: gguf
base_model: unsloth/functiongemma-270m-it
tags:
- function-calling
- tool-use
- on-device
- embedded
- gguf
- llama-cpp
- smartpanel
- synaptics
- functiongemma
language:
- en
pipeline_tag: text-generation
---

# SmartPanel FunctionGemma 270M

Fine-tuned [FunctionGemma 270M](https://huggingface.co/google/functiongemma-270m) for on-device function-calling inside Brinq's **SmartPanel** manufacturing-assistant demo. Shipped on the Synaptics Astra SL2619 SoC (2×Cortex-A55 @ 2 GHz, 1 TOPS Torq/Coral NPU, 2 GB DDR4) at Embedded World 2026.

## What this model does

Given a user utterance and a list of tool declarations, the model emits one or more `<start_function_call>call:NAME{...}<end_function_call>` blocks or a plain natural-language reply. It was trained specifically to hit **sub-500 ms decode latency on the SL2619** without giving up tool-selection accuracy on the SmartPanel domain.

> **Scope.** The fine-tune is specific to the SmartPanel tool schema (maintenance procedures, alarm acknowledgement, photo capture, knowledge lookup). It's published here as **prior art / starting checkpoint** for the related Coral Dev Board physical-AI demo at Google IO 2026, not as a general-purpose function-calling model.

## Files

| File | Format | Size | Recommended use |
|------|--------|------|-----------------|
| `smartpanel-v15-q4_k_m.gguf` | GGUF Q4_K_M | 253 MB | **Production.** Runs via llama.cpp on 2 GB / 2-core ARM targets. |
| `smartpanel-v15-f16.gguf` | GGUF F16 | 543 MB | Canonical checkpoint for re-quantization or further fine-tuning. |
| `smartpanel-v12-q4_k_m.gguf` | GGUF Q4_K_M | 253 MB | Mid-production milestone. |
| `smartpanel-v8-q4_k_m.gguf` | GGUF Q4_K_M | 253 MB | Device deployment milestone (what our SL2619 test boards have shipped with since Feb). |
| `smartpanel-v4-q4_k_m.gguf` | GGUF Q4_K_M | 253 MB | First version with correct `call:` output format. Benchmark reference. |

**Recommended starting point: `smartpanel-v15-q4_k_m.gguf`.**

## Version lineage

| Version | Date | Format | Notes |
|---------|------|--------|-------|
| v4 | 2026-01-18 | `call:` | First correct output format. **84.2%** domain accuracy, 142 ms avg latency on local llama-cpp. |
| v8 | 2026-02-24 | `call:` | Deployed to Ollama on SL2619 test boards. |
| v8-moveworks | 2026-02-26 | `call:` | Variant trained with additional Moveworks-flavored examples. Not included here. |
| v8-fixed | 2026-02-27 | `call:` | Tokenizer hotfix. |
| v9–v13 | Feb 27 – Mar 1 | `call:` | Data curation + prompt-template iterations. |
| v15 | 2026-03-03 | `call:` | Current production. |

(v14 was trained but rolled forward into v15 before quantization — no separate artifact exists.)

## Prompt format

FunctionGemma's native format. The tokenizer ships the `<start_function_call>`, `<end_function_call>`, `<start_function_declaration>`, `<end_function_declaration>`, `<start_function_response>`, `<end_function_response>`, and `<start_of_turn>` / `<end_of_turn>` special tokens.

```
<start_of_turn>user
You are a model that can do function calling with the following functions

<start_function_declaration>
declaration:set_led_color{description:<escape>Set RGB LED color<escape>,parameters:{...}}
<end_function_declaration>
<start_function_declaration>
declaration:play_buzzer{description:<escape>Sound the buzzer<escape>,parameters:{...}}
<end_function_declaration>

Turn the lights red and beep
<end_of_turn>
<start_of_turn>model
<start_function_call>call:set_led_color{color:<escape>red<escape>}<end_function_call><start_function_call>call:play_buzzer{pattern:<escape>beep<escape>}<end_function_call>
<end_of_turn>
```

Stop tokens: `<end_of_turn>`, `<end_function_call>`, `<eos>`.
Recommended generation params: `temperature=0.1`, `top_p=0.9`, `num_ctx=2048`.

## Usage

### llama-cpp-python

```python
from llama_cpp import Llama

llm = Llama(
    model_path="smartpanel-v15-q4_k_m.gguf",
    n_ctx=1024,
    n_threads=2,
    verbose=False,
)

prompt = """<start_of_turn>user
You are a model that can do function calling with the following functions
<start_function_declaration>
declaration:acknowledge_alarm{description:<escape>Dismiss the current alarm<escape>,parameters:{properties:{},required:[],type:<escape>OBJECT<escape>}}
<end_function_declaration>

Ack the alarm
<end_of_turn>
<start_of_turn>model
"""

out = llm(prompt, max_tokens=128, temperature=0.1, stop=["<end_of_turn>"])
print(out["choices"][0]["text"])
```

### Ollama

```bash
# Download the gguf, then:
cat > Modelfile <<'EOF'
FROM ./smartpanel-v15-q4_k_m.gguf
PARAMETER temperature 0.1
PARAMETER num_ctx 2048
PARAMETER stop "<end_of_turn>"
PARAMETER stop "<end_function_call>"
PARAMETER stop "<eos>"
EOF

ollama create smartpanel -f Modelfile
ollama run smartpanel "Ack the alarm"
```

## Benchmark (v3 / pre-v15, Jan 2026)

On SmartPanel domain (llama-cpp-python, Q4_K_M, local dev machine):

| Model | Domain | Accuracy | Avg Latency | Output Format |
|-------|--------|----------|-------------|---------------|
| Mobile Actions base | mobile | 100 % | 178 ms | `call:` |
| SmartPanel v1 | smartpanel | 66.7 % | 355 ms | ❌ `declaration:` |
| SmartPanel v2 | smartpanel | 36.8 % | 135 ms | ❌ partial output |
| **SmartPanel v3** (precursor to v4) | smartpanel | **84.2 %** | **142 ms** | ✅ `call:` |
| Mobile Actions (cross-domain) | smartpanel | 66.7 % | 159 ms | `call:` |

v15 numbers forthcoming — benchmarks live in the Brinq internal repo.

## Training

- Base: `unsloth/functiongemma-270m-it` (BF16)
- Method: LoRA fine-tune via Unsloth + TRL (SFTTrainer)
- Hardware: A100 80GB (Docker, `unsloth` image)
- Quantization: llama.cpp `convert_hf_to_gguf.py --outtype f16` then `llama-quantize ... 15` (Q4_K_M)

Training scripts, curated datasets, and eval harnesses live in Brinq's internal repo (not public). For the related Coral demo's dataset generators and fine-tune recipe (which are shipping public), see [BrinqAI/coral-functiongemma-demo](https://github.com/BrinqAI/coral-functiongemma-demo) (currently private, planned public around Google IO 2026).

## License

Gemma Terms of Use. By using this model you agree to the terms at https://ai.google.dev/gemma/terms.

## Citation

```
@misc{brinqai_smartpanel_functiongemma_2026,
  author       = {Brinq AI},
  title        = {SmartPanel FunctionGemma 270M},
  year         = 2026,
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/BrinqAI/smartpanel-functiongemma-270m}},
}
```

## Acknowledgements

- Google DeepMind for [FunctionGemma 270M](https://ai.google.dev/gemma/docs/functiongemma)
- [Unsloth](https://github.com/unslothai/unsloth) for the fast fine-tune path
- Synaptics Astra team for the SL2619 / Astra SDK