OpenBallast/ballast-t0

⚓ Ballast T0

Quantize the weights. Ballast the knowledge.

The reference ballast — a versioned, rank-quantized knowledge artifact you pair with any language model. 25.4M entities, 197.4M cleaned Wikidata triples, 1.51 GB total, published in 8 nested rank buckets: pick your knowledge level like you pick a GGUF quant.

Measured effect (Gemma-4, 50,147 linked probes, logprob choice scoring):

model	raw	+ full ballast	hallucination
E2B	0.608	0.868	0.242 → 0.074
E4B	0.662	0.910	0.197 → 0.042
12B	0.683	0.910	0.207 → 0.049

+180 MB of ballast lifts E2B past the 12B's raw factual accuracy — the same gain via parameters costs ~19 GB. Full thesis, methodology, and caveats: github.com/OpenBallast/ballast.

Layout & quantization levels

Hive-partitioned Parquet; the partition key is the quantization axis. A level Lk = buckets 0..k — download fewer bucket directories to hold a smaller corpus:

entities/tier=T0/lang=en/rank_bucket={0..7}/*.parquet
triples/tier=T0/lang=en/rank_bucket={0..7}/*.parquet
properties.parquet    # 13,704 property labels, shared by every level
manifest.json         # counts, sizes, hashes, build provenance

level	cumulative	entities
L0	36 MB	top 0.5%
L1	62 MB	top 1%
L2	107 MB	top 2%
L3	179 MB	top 4%
L4	288 MB	top 8%
L5	466 MB	top 16%
L6	756 MB	top 32%
L7	1,507 MB	all 25.4M

Ranking = composite of log(sitelinks) + log(claim count). Buckets nest: L3 is a byte prefix of L7. Truncation degrades honestly — an entity outside the level has no evidence; a triple whose object falls outside the level is dropped, never rendered as a bare Q-id.

Schemas

entities: qid, label, description, aliases[], sitelinks, n_claims (+ rank_bucket from path) triples: qid, pid, value_type (entity|time|quantity|string|coord), value (+ subject's rank_bucket)

Excluded at publish: external identifiers, media references, URLs — bytes that ground nothing a language model can use.

Use it

DuckDB (the layout is built for it):

SELECT t.qid, p.label AS prop, t.value
FROM read_parquet('ballast-t0/triples/**/*.parquet', hive_partitioning=true) t
JOIN read_parquet('ballast-t0/properties.parquet') p USING (pid)
WHERE t.rank_bucket <= 3 AND t.qid = 'Q42';

Evidence rendering (the exact format the published numbers were measured under):

Facts about Douglas Adams:
- place of birth: Cambridge
- occupation: science fiction writer
...

Run it locally (serving format)

serving/sqlite/ carries the same corpus as zstd-compressed per-bucket SQLite — the format consumed by the ballast CLI:

uvx openballast pull --level 3     # 265 MB download -> ~1 GB on disk
uvx openballast serve              # OpenAI grounding proxy :11435 + MCP :11436

level	download	on disk		level	download	on disk
L0	52 MB	0.2 GB		L4	427 MB	1.6 GB
L1	92 MB	0.35 GB		L5	691 MB	2.6 GB
L2	159 MB	0.6 GB		L6	1.1 GB	4.2 GB
L3	265 MB	1.0 GB		L7	2.2 GB	9.2 GB

Each bucket_k.sqlite holds that bucket's entities, names (normalized label/alias index), and triples; levels attach buckets 0..k, so upgrading a level downloads only the new buckets. Truncation semantics identical to the parquet artifact. Parquet remains the canonical/build format; the sqlite tree is a derived serving artifact.

Live demo endpoint

Levels L0–L5 are served free at https://mcp.openballast.org (MCP over streamable HTTP + plain GET) — running entirely on a $0/month free-tier stack as an existence proof. Demo-grade: no auth, no SLA. Reference: docs/mcp.md.

curl "https://mcp.openballast.org/lookup?question=Where+was+Douglas+Adams+born%3F&level=5"

Provenance & license

Built from the Wikidata JSON dump (2026-07 snapshot; parameters and hashes in manifest.json). mul language code honored with English fallback; Wikimedia-internal pages filtered by instance of. Data: CC0 (Wikidata contributors). Companion eval sets: OpenBallast/ballast-evalsets.