---
license: apache-2.0
language:
- en
- hi
task_categories:
- text-generation
size_categories:
- 1M<n<10M
tags:
- home-automation
- text-to-json
- hinglish
- templated
- experimental
configs:
- config_name: prepared
  default: true
  data_files:
  - split: train
    path: data/train.jsonl.gz
  - split: validation
    path: data/validation.jsonl.gz
  - split: test
    path: data/test.jsonl.gz
- config_name: original
  data_files:
  - split: source
    path: source/home-commands-3.4M.jsonl.gz
- config_name: robotics_edge_v1
  data_files:
  - split: test
    path: evaluation/robotics_edge_v1/cases.jsonl
---

# Home Commands JSON v1

English and Romanized Hindi/Hinglish home commands mapped to structured JSON.
This release contains the **exact source and processed snapshot** associated
with [Superfast Tiny Home Robotics JSON 1M v1](https://huggingface.co/sraivante/superfast-tiny-home-robotics-json-1m-v1/tree/v1.0.2).
It also includes a separately authored evaluation challenge set. Native-script
Hindi appears only in a small later diagnostic, not as a supported training
language claim.

Publisher: **sraivante**. Release date: **2026-09-25**. Version: **v1.0.2**.

## Configurations and counts

| Configuration / split | Rows | Role |
| --- | ---: | --- |
| original / source | 3,458,816 | Original received JSONL, losslessly gzip-compressed |
| prepared / train | 910,114 | Actual training examples |
| prepared / validation | 103,089 | Validation split |
| prepared / test | 104,723 | Original held-out test split |
| robotics_edge_v1 / test | 284 | Later stress-test cases; never used to train this model |

The `source` split is provenance data, **not an additional training split**.
Do not combine it with the prepared splits: it contains their source examples
and many duplicates. The prepared configuration is the default.

## Load the data

```python
from datasets import load_dataset

repo = "sraivante/home-commands-json-v1"
data = load_dataset(repo, "prepared", revision="v1.0.2")
print(data["train"][0])

# For a small initial download:
stream = load_dataset(repo, "prepared", split="train", streaming=True, revision="v1.0.2")
print(next(iter(stream)))

challenge = load_dataset(repo, "robotics_edge_v1", split="test", revision="v1.0.2")
source = load_dataset(repo, "original", split="source", streaming=True, revision="v1.0.2")
```

JSONL files are gzip-compressed without changing the decompressed bytes. Each
line is one UTF-8 JSON object. Download and decompress directly if not using
the Hugging Face Datasets library.

## Schema

Original source:

```json
{"instruction":"Please turn on the fan","output":{"activity":"air","subject":"fan","action":"ON"}}
```

This illustrates the schema rather than asserting the example's source line.
The prepared files retain `instruction` and the object-valued `output`, and add:

- `group_id`: canonical phrase-family string used for split assignment.
- `source_line`: one-based line number of the retained example in the original
  decompressed JSONL.

Actions are `ON`, `OFF`, `STATUS`. There are 30 observed combinations across
`fan`, `light`, `motor`, `speaker`, `geyser`, `cooler`, `washing_machine`, `tv`,
`ac`, and `sprinkler`. `motor` refers to the water pump. Full valid combinations
and the field schema are in `metadata/allowed_outputs.json` and
`metadata/output_schema.json`.

Challenge cases have `id`, `category`, `text`, nullable `expected` JSON,
`expected_behavior`, `scope`, `reason`, and overlap-audit fields. `expected=null`
means no single supported command is acceptable: the declared policy calls for
abstention/clarification. It does not mean the trained model supports a reject
class. The four input-guard cases include empty/overlong text intentionally.

## Preparation and split construction

1. Audit JSONL records, output schema and conflicting normalized inputs.
2. Normalize input identity with Unicode NFKC, whitespace normalization and
   case-folding. Remove 2,340,890 duplicate normalized-input rows. Retain one
   source example per distinct normalized input, leaving **1,117,926** examples.
3. Group phrase families after removing punctuation, common greetings/politeness
   prefixes and suffixes, and the article `the`. Stratify family assignment by
   the 30 target commands, with approximately 80/10/10 family proportions and
   seed 20260925. This produces 2,292 / 288 / 288 train/validation/test families.
4. Fit the byte-level BPE tokenizer only on training examples; serialize input
   and JSON response with explicit role tokens. The training objective includes
   input and response next-token targets, excluding padding and initial BOS.

Training contains **22,655,302 prediction tokens** for one pass. Only 4,120
training examples have STATUS targets, versus 905,994 ON/OFF examples; command
imbalance and heavy templating should be considered when evaluating models.

The preparation code is in `preparation/`. The exact tokenizer, token-ID memmaps,
indexes and normalized splits used by training are also included in
`artifacts/home_commands_training_bundle.zip` for reproduction.

## Evaluation scope and known limitations

The associated model reached 100% exact JSON accuracy on the original 104,723
test rows, but only **68/140 (48.6%)** on later supported-command challenges and
**48/120 (40%)** on those without source-phrase overlap. This is evidence that
the original split is not a sufficient real-world generalization test.

The later 284-case suite was frozen before model inference. Twenty cases overlap
an original source phrase (14 train, 3 validation, 3 test); the remaining 264
do not. It is a small assistant-authored diagnostic with correlated wording,
not a probability sample, recorded-speech dataset or robotics safety benchmark.

The training output space has no reject/no-op class, negation semantics,
device instances, speed/position parameters, schedules or multi-step commands.
The associated model emitted recognized commands for all 130 requests requiring
abstention, including 33 ON/OFF commands. **Do not interpret valid JSON as safe
actuation.** Additional human-written data and independent testing are needed
for practical command systems.

## Snapshot identity

These are SHA-256 hashes of the **decompressed original bytes**:

| File | SHA-256 |
| --- | --- |
| Original source | `10822f6617d2cc94dca66d609a1f41769cd5ef2ebba224d85fc7ae79cfbbaeba` |
| Train JSONL | `63313b7aafeb2a2a32e87c660015d745a84698a0db1c23e83c8da94105547430` |
| Validation JSONL | `df6821e7165af193a09b6790de530c83ef9466d25b4a7e01d25f5b392ec3b580` |
| Test JSONL | `a43dc4afaa386e5d4825e26b495159b0beace648488c4d843f153c650adfaa06` |
| Later challenge JSONL | `30cb9a6de18cc12cefcbb6e1aac8a29c7371b0f4ea537e516f38d02b3f417d6c` |

`training_snapshot_manifest.json` is the byte-identical manifest used by the
run. `release_manifest.json` additionally hashes compressed/public artifacts
and links the model version. The historical manifest's private/pre-authorization
note describes its creation state; this release follows the publisher's later
explicit authorization. No source JSONL or split content has been edited.

## Provenance, license and attribution

The source file was supplied by **sraivante** for this project. Its upstream
generation script, historical repository revision, and original creation date
were not provided or independently verified. No external corpus was added in
preparation. The exact file hash identifies the data actually used by the run;
the release date is not a claim about the original source creation date.

**Copyright (c) 2026 sraivante**, limited to original contributions and original
selection/arrangement, under **Apache License 2.0**. See [LICENSE](https://huggingface.co/datasets/sraivante/home-commands-json-v1/blob/v1.0.2/LICENSE) and
[NOTICE](https://huggingface.co/datasets/sraivante/home-commands-json-v1/blob/v1.0.2/NOTICE). Third-party ownership, attribution and terms remain unchanged.
The material supplied to this project did not identify third-party source
repositories; the original-contribution license does not purport to relicense
any unidentified third-party material.

The later diagnostic was assistant-authored during evaluation for the publisher
and is separately versioned and marked in the manifest. It was not mixed into
the training snapshot. Models or revisions trained using this challenge must
use a fresh independent evaluation set for new performance claims.


## Raspberry Pi evaluation snapshot — 2026-09-26

Measurement/data revision: `edge-rpi5-20260926.1`. The original training/source files are unchanged, pinned at `8479d12161767c5aabd7dfc097e6f6c41790201a`. No training or fine-tuning was performed for this update.

The [`edge_benchmarks/rpi5-2026-09-26`](edge_benchmarks/rpi5-2026-09-26) directory adds **284 quality cases**, **10 latency inputs**, and all quality predictions from the corresponding published model on a Raspberry Pi 5 (16 GB, aarch64). The complete model card [reports the measurements](https://huggingface.co/sraivante/superfast-tiny-home-robotics-json-1m-v1/tree/edge-rpi5-20260926.1).

Evaluation role: **existing frozen stress/regression suite, constrained decoding only**. Previously inspected synthetic suite; not a new blind holdout. Original per-case source-overlap metadata retained. The Pi result was **72/284**, with the per-category breakdown in the linked model report. This data does not establish fresh blind or real-world accuracy.

`quality_cases.jsonl` and `latency_cases.jsonl` contain `id`, input `text`, serialized `expected_json` (null means reject/guard), scoring `match` (exact, subset, rejection), and `category`. `quality_predictions.jsonl` adds serialized `output_json`, `correct`, `error`, `elapsed_ms`, and post-inference `cpu_temperature_c`. Exact adapter inputs and original metadata are preserved in `cases/*.json`. [`provenance.json`](edge_benchmarks/rpi5-2026-09-26/provenance.json) records counts, SHA-256, original source revisions and historical identity limitations. Keep this diagnostic separate from training and the original evaluation configurations.

Copyright (c) 2026 sraivante applies only to original benchmark/evaluation material and original selection/arrangement, under Apache License 2.0. Third-party ownership, attribution and licenses remain unchanged.