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Datasets:
tuxevil
/
Home-Assistant-Requests-V5.2-Native-Strict

Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
multilingual
Size:
1K - 10K
Tags:
home-assistant
tool-calling
function-calling
smart-home
qwen3
Libraries:
Datasets
pandas
Polars
License:
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Home-Assistant-Requests-V5.2-Native-Strict / README.md
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metadata 
license: other
license_name: mixed-mit-apache-2.0-cc-by-4.0
task_categories:
  - text-generation
language:
  - multilingual
tags:
  - home-assistant
  - tool-calling
  - function-calling
  - smart-home
  - qwen3
pretty_name: Home Assistant Requests V5.1 Native Strict
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: validation
        path: validation.jsonl
      - split: test
        path: test.jsonl

Home Assistant Requests V5.2 Native Strict

Private research dataset for supervised fine-tuning and regression testing of a small Home Assistant native tool-calling model.

Contract: ha-native-tool-calling-v2.

Frozen snapshot

Split Rows Direct speech Multi-call Maximum rendered tokens
train 3,806 340 78 3,098
validation 530 52 4 2,874
test 633 102 22 2,925

Tokenizer audit:

  • model: unsloth/Qwen3-4B-Instruct-2507
  • revision: 992063681dc2f7de4ee976110199552935cad284
  • MAX_SEQ_LENGTH=4096
  • overlength rows: 0
  • render failures: 0

All 20 Home Assistant native tools are represented. Test contains 52 GetLiveContext cases, at least 20 positive cases per tool, 102 direct/no-tool cases, and 22 multi-call cases.

Files

  • train.jsonl, validation.jsonl, test.jsonl: frozen training/evaluation splits.
  • metadata.json: sources, coverage, tool counts, and readiness report.
  • audit.json: contract, duplicate, leakage, entity-cap, secret, and hash audit.
  • token_audit.json: full tokenizer/template length and supervised-token audit.
  • provenance.json: immutable revisions, input/output hashes, code hashes, and exact build command.

Sources and licensing

This is a mixed-license derivative dataset. Preserve source attribution and applicable notices when redistributing derivatives.

  1. tuxevil/Home-Assistant-Requests-V5-Native, revision fbf847bb63cfb2e49a36435412d610ac8993e8fc, derived from acon96/Home-Assistant-Requests-V2 — MIT.
  2. enfuse/joint-intent-slot-smarthome, revision 39056ca057ae92d71fb7a272f7960567e55dca70 — Apache-2.0. Used in train only through a deterministic strict mapper.
  3. home-assistant/intents, revision 35466626accc5699203fc20a1af470ff173a7893 — CC-BY-4.0. Attribution: Home Assistant intents contributors.
  4. Deterministic internal deficit-only fixtures generated for missing readiness classes. No teacher LLM or paid API generated these fixtures.

The repository-level metadata uses license: other because no single SPDX license describes the combined corpus.

Construction policy

  • Maximum eight exposed entities per row.
  • Tool names and schemas are frozen from Home Assistant Core branch 2026.7.4.
  • Tool arguments must satisfy their supplied schema and refer only to exposed entities/areas.
  • Invalid, hidden-target, conflicting, unsupported, or non-native examples are rejected.
  • Enfuse data is train-only.
  • Official Home Assistant conformance rows are allocated test first, then validation, then train, without row reuse.
  • Final global isolation removes normalized user-text and structured semantic-signature collisions in priority order: test → validation → train.
  • Coverage fixtures only fill measured deficits and receive unique split-specific utterances.

Verified gates

audit.json reports:

  • contract errors: 0
  • duplicate example IDs: 0
  • cross-split semantic overlap: 0
  • cross-split normalized user overlap: 0
  • secret-pattern hits: 0
  • maximum exposed entities: 8
  • training readiness: true

token_audit.json reports zero overlength rows and zero render failures over all 4,969 rows.

Limitations

  • Split isolation prevents leakage created by this build. It cannot prove that a pretrained base model never saw public Home Assistant or Enfuse text during pretraining.
  • Official conformance sentences are useful deterministic holdouts, not a guarantee of out-of-distribution generalization.
  • Deficit fixtures are synthetic deterministic coverage data and may be stylistically narrower than real voice traffic.
  • Passing dataset gates does not promote a model to production. Adapter, merged FP16, Q5, Ollama, and physical Home Assistant smoke gates remain mandatory.
  • Q5 artifacts produced from this dataset remain research-only-pending-ollama-gate until tested against an authorized physical entity.

Reproducibility

See provenance.json for exact revisions, SHA-256 hashes, code hashes, tokenizer versions, and complete build command. Consumers should pin the Hugging Face dataset repository to an immutable commit SHA rather than main.