| ---
|
| 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.
|
|
|
