AwaleSagar/gpio-llm-rpi5-actions

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.

What's new in v2.0 (gpio_actions_v2, default)

• Train grows from 58,821 to 1,678,821 rows. The 58,821 original rows are unchanged; 1,620,000 new rows have source == "deterministic_gen_v1".
• Eval is unchanged. eval and eval_core are the same files, byte for byte, so v1 and v2 scores can be compared directly.
• Same schema, same specification. The action vocabulary is still 1.0 (Raspberry Pi 5 / RP1), and the fields and types match v1.
• No language model wrote any of the new rows. A seeded, rule-based generator produced both labels and wording (details below).
• New knowledge base in knowledge/. It holds real board pinouts, sensor modules and hobby projects, with sources, and was used to make the wording more realistic.
• The original release stays available. It is the gpio_actions config and the git tag v1.0:

from datasets import load_dataset
ds = load_dataset("AwaleSagar/gpio-llm-rpi5-actions")                       # v2: 1.68M train rows
v1 = load_dataset("AwaleSagar/gpio-llm-rpi5-actions", "gpio_actions")       # v1: 58,821 train rows
v1 = load_dataset("AwaleSagar/gpio-llm-rpi5-actions", revision="v1.0")      # exact v1 snapshot
original_only = ds["train"].filter(lambda r: r["source"] != "deterministic_gen_v1")

Quick start

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	v2 rows	v1 rows	Purpose
train	1,678,821	58,821	Training (v2 = 58,821 original + 1,620,000 synthetic)
eval	26,297	26,297	Held-out evaluation: 132 phrasing templates never used in training
eval_core	5,083	5,083	Quick evaluation: 46 rows from every eval template

• Train and eval share no ids, no phrasing templates and no number-normalised text.
• eval_core is a subset of eval.
• For the synthetic rows, three eval-leakage tests were applied to every candidate (see How v2 was made). The highest word-3-shingle Jaccard between an accepted row and eval is 0.692, under the 0.7 limit.

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); at most 256 in synthetic rows
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 came from, the unit of the train/eval split. Synthetic rows use dg.<family>.<frame>
source	string	golden_manual, synthetic_template_v1/v2 or teacher_<model> (v1 rows), or deterministic_gen_v1 (v2 synthetic rows)
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.

Examples

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

prompt: User: Pi, pulse GPIO 13 for 3500 milliseconds.        (deterministic_gen_v1)
        Assistant:
target:  {"action":"gpio_pulse","pin":13,"value":"HIGH","duration_ms":3500}

prompt: User: could you turn physical pin 17 on real quick? thanks   (deterministic_gen_v1)
        Assistant:
target:  {"action":"report_error","pin":null,"parameters":{"reason":"not_a_gpio","header_pin":17}}

Composition (v2 train)

Category	Original (v1)	Synthetic (v2)	Total
clarification	3,608	237,680	241,288
driver	1,896	141,960	143,856
error	10,853	299,150	310,003
general_hardware	162	14,838	15,000
gpio_mode	4,513	58,038	62,551
gpio_pwm	5,964	122,877	128,841
gpio_read	4,079	50,868	54,947
gpio_sequence	6,401	207,233	213,634
gpio_timing	6,067	153,668	159,735
gpio_write	7,960	122,908	130,868
safety	4,187	123,284	127,471
sensor	3,131	87,496	90,627

Action	v2 train rows
report_error	452,474
gpio_sequence	254,550
ask_clarification	241,288
gpio_pwm	192,944
gpio_write	169,705
gpio_read	145,574
gpio_pulse	120,829
gpio_mode	62,551
wait	38,906

The synthetic rows use 137 template ids. provenance/v2_composition.json also breaks the data down by difficulty.

How v1 was made (original 58,821 rows)

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. Uncertain facts are left out of labels.

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 rows
   • gpt-5.6-terra: 10,997 rows
   • z-ai/glm-5.3-flash: 72,306 rows

   Each rewrite passed deterministic wording checks, the strict validator, duplicate and leakage checks, and a judge model.

4. Checks. These were applied to every row:

   • strict schema and hardware-rule validation
   • pin grounding
   • exact and near-duplicate removal
   • contradiction checks
   • template-level train/eval separation
   • replay through the GPIO simulator

How v2 was made (1,620,000 synthetic rows)

No generative model was sampled. Generation was label first, words second.

1. Label first. A seeded generator (NumPy PCG64) samples a valid action under specification 1.0.
   • It uses the same frozen hardware facts: RP1 alternate-function table, hardware PWM only on GPIO 12/13/18/19, GPIO0/1 reserved, fixed pull-ups on 2/3, drive strengths 2/4/8/12 mA.
   • It follows the same policies, for example no PWM on GPIO14/15, a question instead of a guess, and active-low handling.
2. Words second. The generator builds the request from:
   • hand-written phrase frames and a lexicon
   • register transforms (casual, terse, polite, voice-assistant, typos)
   • device names, context variants and multi-turn follow-ups
   • real wiring scenes from the knowledge base
3. How the code was written. The generator code, frames and lexicon were written with an AI coding assistant (Claude) and reviewed rule by rule. At generation time no text is sampled from any language model. Every row can be reproduced from the seed and the code.
4. Validation gate. All 100% of accepted rows pass these checks:
   • the strict validator
   • replay in the GPIO simulator
   • pin grounding
   • canonical JSON key order
   • prompt/target/token consistency, with at most 256 tokens
   • grammar and wording checks
5. Measurement only. EmbeddingGemma-300M embeddings (256-dimensional Matryoshka) were used for gap analysis, near-duplicate removal, leakage checks and diversity-aware selection. The model never produced text.
6. Eval leakage. A candidate is rejected if any of these is true:
   • its number-normalised skeleton appears in eval
   • its 3-shingle Jaccard with an eval row is at least 0.7
   • its cosine to an eval row reaches the 99th percentile of original train→eval similarity
7. Dedup and selection.
   • Exact duplicates of original, eval or earlier rows are removed.
   • A pair counts as a near-duplicate only if both rows have the same label skeleton and cosine ≥ 0.97.
   • Greedy selection favours novel rows, with a per-template cap of 25% and family quotas from gap analysis.
8. Stages. The data was built in stages, each measured before scaling up: 10K pilot → 100K → knowledge-base run → 500K → 1M. All five were then merged.

Check (final 1M stage)	Result
Validator + simulator	100% of 2.6M candidates checked; only passing rows kept
Eval leakage in accepted rows	0 (10,514 candidates rejected)
Unique prompts	100%
Mean nearest-neighbour cosine to original train	0.878 (72.9% of rows in a novel region)
Labels in a manual review sample	32/32 correct

Stage reports, dedup/leakage reports and review samples are in generator_reports/. The release manifest with file hashes is provenance/v2_release_manifest.json.

Knowledge base (knowledge/)

Real reference data used to make scenes and wording more realistic. No language model wrote any of it.

File	Contents
boards.json	32 Raspberry Pi boards, 16 of them Linux Pis with a 40-pin header. For each GPIO family (BCM2835/6/7 + RP3A0, BCM2711, RP1): alternate functions for GPIO0–27, default pulls, drive strengths, input thresholds and PWM channels. The pinctrl tables were checked cell by cell against datasheets.
sensors_src.yaml	19 common modules (PIR, HC-SR04, relays, DHT22, DS18B20, SG90, L298N, WS2812, …) with signal levels, v1 action mapping, caveats and source URLs
projects.json	122 real project scenes (device → BCM pin, operations, conflicts) from GPIO Zero recipes and Raspberry Pi Foundation projects; 88 fit specification 1.0
sources.json	Source repositories with commits and licences, plus datasheet and module URLs
README.md	Method, verification and known conflicts

Only Raspberry Pi 5 / RP1 facts drive the labels. The other boards are recorded for a future board-aware specification.

Intended use and limitations

• Intended use. Training and evaluating small models that map requests to structured hardware actions, and research on clarification and refusal behaviour.
• All requests are synthetic. They come from templates, model rewrites (v1) or the rule-based generator (v2). Real spoken phrasing is under-represented.
• Less wording diversity in v2. Rule-based wording is less varied than the v1 teacher rewrites: distinct-2 is 0.093 vs 0.128 on equal 10K samples. Rows are valid and unique, but many share frames.
• Intent mix is skewed at the 1M stage. 27 generator families ran out of fresh wording, so about 205K rows of their quota went to families with spare candidates:
  • The receiving families include pwm_device, project_scene_seq, pulse, seq_multi, pwm_basic and multi_turn.
  • Intent-cell Gini is 0.414 (0.355 after the 500K stage).
  • If you need a balanced mix, reweight or subsample by template_id or category, or use the v1 config.
• Scope. Action vocabulary 1.0 on the 40-pin header of a Raspberry Pi 5:
  • In scope: digital I/O, pulls, drive strength, documented alternate functions, hardware/software PWM, pulses, waits and sequences.
  • Out of scope: bus transactions (I2C/SPI/UART payloads), sensor protocols, edge-triggered waits and timing precision limits.
• Conservative policies are built into the labels. Examples: 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).
• Not independently verified. v2 has not yet been checked with a training A/B run on eval_core, and has had no independent human review beyond the review samples.

Models trained on this dataset

Three from-scratch Llama-style models were pretrained on 550M fineweb-edu tokens and then fine-tuned on all of gpio_actions_v2 train for 2 epochs. Numbers are greedy exact match on eval, before any validator:

Model	Parameters	eval exact match	Pi Zero 2 W p50 (C engine, eval_core)
gpio-llm-pico-rpi5	2.7M	91.88%	86 ms
gpio-llm-nano-rpi5	5.0M	93.61%	132 ms
gpio-llm-base-rpi5	18.8M	95.17%	498 ms

The training code and the offline C inference engine are at github.com/AwaleSagar/gpio-llm. All of it is grouped in the GPIO-LLM collection.

Licence and attribution

• Dataset. Released under CC-BY-4.0.
• v1 teacher rows. User wording in teacher_* rows was generated with third-party models; check those providers' terms on using model outputs.
• Hardware facts summarise public Raspberry Pi documentation and datasheets.
• Knowledge base. The files in knowledge/ keep the licences of their sources:
  • Raspberry Pi documentation (raspberrypi/documentation), © Raspberry Pi Ltd, CC BY-SA 4.0. Used for board and GPIO facts.
  • Raspberry Pi Foundation learning projects (raspberrypilearning), CC BY-SA 4.0. Used for the project scenes in projects.json; the repos are listed in sources.json. Derived project data is shared under CC BY-SA 4.0.
  • GPIO Zero (gpiozero/gpiozero), © Ben Nuttall, Dave Jones and contributors, BSD-3-Clause. Used for recipes and docstring quotes.
  • pinctrl (raspberrypi/utils), © Raspberry Pi Ltd, BSD-3-Clause. Used for alternate-function tables.
  • Datasheets, vendor pages and tutorials in sensors_src.yaml are cited by URL only. Verbatim quotes from third-party sites were removed from this public copy.
• Pretraining text is not included. The companion English pretraining text is a filtered subset of 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

Version	Git tag	Record
v1 (gpio_llm_v1)	v1.0	provenance/release_manifest.json
v2 (gpio_llm_v1_synth1620k)	v2.0	provenance/v2_release_manifest.json: file SHA-256, spec lock, generator runs and seeds

Citation

@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, v2.0 (1.68M-row synthetic expansion)}
}