Initial upload: v15 (production) + v4/v8/v12 milestones + README

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README.md

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+---
+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

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