---
license: cc-by-4.0
language:
- en
pretty_name: "GPIO-LLM: Raspberry Pi 5 GPIO request-to-action dataset"
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- raspberry-pi
- gpio
- embedded
- function-calling
- structured-output
- safety
- synthetic
- small-language-model
configs:
- config_name: gpio_actions
  default: true
  data_files:
  - split: train
    path: data/gpio_actions/train.parquet
  - split: eval
    path: data/gpio_actions/eval.parquet
  - split: eval_core
    path: data/gpio_actions/eval_core.parquet
---

# GPIO-LLM: Raspberry Pi 5 GPIO request-to-action dataset

Requests to a Raspberry Pi 5 in plain English, paired with the **structured, validated GPIO action** a
small on-device model should produce: a hardware operation, a clarifying question when the pin or device
is unknown, or a refusal when the request is invalid or unsafe. It was built to train a ~20M-parameter
English model that runs offline on the Pi.

> **Safety.** Model output must never drive hardware directly. Every action is meant to pass a deterministic
> validator and hardware controller first (the project ships a validator and a GPIO simulator). The labels
> encode the project's safety policy; they are not a substitute for correct wiring.

## Quick start

```python
from datasets import load_dataset
ds = load_dataset("AwaleSagar/gpio-llm-rpi5-actions")          # splits: train, eval, eval_core
row = ds["train"][0]
print(row["prompt"] + row["target"])
```

Fine-tune on `target` with the loss masked over `prompt`. The target starts with a space
(`"Assistant:"` | `" {...}"`), so tokenizing the two parts separately gives exactly the tokens of the
joined text.

## Splits

| Split | Rows | Purpose |
|---|---|---|
| `train` | 58,821 | Training |
| `eval` | 26,297 | Held-out evaluation: 132 phrasing templates never used in training |
| `eval_core` | 5,083 | Quick evaluation: 46 rows from every eval template |

Train and eval share no ids, no phrasing templates and no number-normalised text; the largest word-3-shingle
Jaccard similarity between any train and eval row is 0.6923
(limit 0.7). `eval_core` is a subset of `eval`.

## Fields

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique row id |
| `prompt` | string | Model input: optional `Context: {json}` line, the conversation as `User:` / `Assistant:` lines, ending with `Assistant:` |
| `target` | string | Model output: a space and the action as compact JSON |
| `tokens` | int | Tokens of `prompt + target` with the project tokenizer (`gpio_llm_bpe_12k`) |
| `action` | string | Action name (`ask_clarification`, `gpio_mode`, `gpio_pulse`, `gpio_pwm`, `gpio_read`, `gpio_sequence`, `gpio_write`, `report_error`, `wait`) |
| `category` | string | One of `clarification`, `driver`, `error`, `general_hardware`, `gpio_mode`, `gpio_pwm`, `gpio_read`, `gpio_sequence`, `gpio_timing`, `gpio_write`, `safety`, `sensor` |
| `template_id` | string | Phrasing template the row was derived from (the unit of the train/eval split) |
| `source` | string | `golden_manual`, `synthetic_template_v1/v2`, or `teacher_<model>` for model-rewritten wording |
| `difficulty` | int | 1 (direct) to 5 (multi-step or tricky) |
| `fact_refs` | list | Hardware facts (`F-*`) and dataset policies (`P-*`) the label depends on (`configs/rpi5_hardware_facts.json`) |
| `messages` | list | The conversation as role/content turns |
| `context` | string (JSON) | Optional runtime context: `device_mappings`, `available_pins` or `gpio_state` |
| `expected_action` | string (JSON) | The label as a JSON object (same as `target`) |

Actions, fields, enums and reason codes are defined in `configs/action_vocabulary.json`.

## Example

```text
prompt: Context: {"device_mappings":{"door_lock_relay":20}}
        User: Turn on the door lock relay.
        Assistant:
target:  {"action":"gpio_write","pin":20,"value":"HIGH"}
```

## Composition

| Category | Train | Eval |
|---|---|---|
| clarification | 3,608 | 1,672 |
| driver | 1,896 | 1,168 |
| error | 10,853 | 4,711 |
| general_hardware | 162 | 43 |
| gpio_mode | 4,513 | 1,619 |
| gpio_pwm | 5,964 | 2,286 |
| gpio_read | 4,079 | 1,340 |
| gpio_sequence | 6,401 | 3,880 |
| gpio_timing | 6,067 | 3,133 |
| gpio_write | 7,960 | 3,654 |
| safety | 4,187 | 1,674 |
| sensor | 3,131 | 1,117 |

## How it was made

1. **Hardware facts.** Every capability comes from cited sources: the Raspberry Pi documentation, the RP1
   peripherals datasheet (Table 4, checked cell by cell) and the Raspberry Pi 5 product brief. Nothing is
   invented; uncertain facts are excluded from labels (see *Limitations*).
2. **Labels.** 98 hand-written golden rows and fact-grounded templates produce every target
   deterministically. No label was written by a language model.
3. **Wording.** Teacher models only rewrote the user's words for existing targets
   (`deepseek/deepseek-v4.1-flash` 197, `gpt-5.6-terra` 10,997, `z-ai/glm-5.3-flash` 72,306 rows). Each rewrite passed
   deterministic wording checks (same pins, numbers and meaning), the strict validator, duplicate and
   leakage checks, and a judge model (`gpt-5.6-terra`).
4. **Checks.** Strict schema and hardware-rule validation, pin grounding (every pin in a target appears in
   the request or context), exact and near-duplicate removal, contradiction checks, template-level
   train/eval separation, and replay of every target through the GPIO simulator.

## Intended use and limitations

- Intended for training and evaluating small models that map requests to structured hardware actions,
  and for research on clarification and refusal behaviour.
- All requests are synthetic (template or model-rewritten). Real spoken phrasing is under-represented.
- Scope is action vocabulary 1.0 on the 40-pin header: digital I/O, pulls, drive strength, documented
  alternate functions, hardware/software PWM, pulses, waits and sequences. Bus transactions (I2C/SPI/UART
  payloads), sensor protocols, edge-triggered waits and timing precision limits are out of scope.
- Conservative policies are built into the labels, for example: GPIO0/1 are refused, "pin N" means BCM
  GPIO N, "turn on" means HIGH, hedged pin choices trigger a clarifying question, and PWM on GPIO14/15 is
  never taught. Each policy is recorded in `docs/SPEC_V1_DECISIONS.md` (specification 1.0).

## Licence and attribution

- Released under **CC-BY-4.0**.
- User wording in `teacher_*` rows was generated with third-party models; check those providers' terms on
  using model outputs for your use case.
- Hardware facts summarise public Raspberry Pi documentation and datasheets.
- The companion English pretraining text is **not** included. It is a filtered subset of
  [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus)
  (`fineweb-edu-dedup`, ODC-By 1.0, derived from Common Crawl); `provenance/` lists the exact selection so it
  can be rebuilt. The project's pretraining mix used 550M tokens.

## Provenance

`provenance/release_manifest.json` records the SHA-256 of every source file, the counts above, the
pretraining mix and the SPEC targets. Release version: `gpio_llm_v1`.

## Citation

```bibtex
@misc{gpio_llm_dataset,
  title  = {GPIO-LLM: Raspberry Pi 5 GPIO request-to-action dataset},
  author = {GPIO-LLM project},
  year   = {2026},
  note   = {Release gpio_llm_v1}
}
```