⚓ OpenBallast

How much of a bigger model's factual advantage is just memorized trivia — and can you buy it back with a file instead of parameters? We measured it. You can: 40–100× cheaper per byte at full precision, ≈15× against the bigger model's cheapest intact quant.

Small models reason fine — they just don't know much. OpenBallast builds ballasts: versioned, rank-quantized knowledge artifacts you pair with any local model — pick your knowledge level like you pick a GGUF quant. The triple tier is CC0; the prose tier carries Wikipedia's CC BY-SA; every artifact states its own license.

Measured (two model families; 50,147 recall probes, 90.5% corpus-linked, plus a 43,137-probe hallucination suite — 8-way multiple-choice scored from token probabilities with abstention; open-ended generative numbers are lower and reported separately in the retrieval results):

• Raw, Gemma-4's E2B/E4B/12B score 61/66/68%; given the same corpus to look facts up in, all three land at 87–91%. Replicates on Qwen3.5 (0.8B–9B: raw 32–54%, ballasted 77–83%, and the ballasted 4B beats the ballasted 9B). Size gaps are mostly memorization gaps.
• An E2B + a 470 MB file beats the 12B raw with a real, non-generative lookup in the loop; the bf16 parameter route costs ≈19 GB. Hallucination on answerable questions: 0.24 → 0.07.
• On open-ended generation with prose retrieval, a 9B + corpus + a two-pass support check beats a 12B alone by +17.5 points at less VRAM (details).
• Grounding cuts multi-hop hallucination 3–20× but raises fabrication on unanswerable questions — it fixes answerable questions, it does not teach abstention.
• Ballasted accuracy doubles as a 4-bit damage diagnostic: nf4 breaks some models and not others, unpredictably from size, and only the grounded score tells you which.
• Corpora tuned to a specific model's knowledge gaps lose to the one generic corpus at every equal-bytes level — a negative result we publish with the same prominence as the wins.

• 📦 ballast-t0 — the triple corpus, 8 quantization levels, CC0
• 📚 ballast-t1 — full-body Wikipedia passages, same levels, CC BY-SA (+ CC0 statements sidecar)
• 🎓 ballast-t2 — 61 OpenStax textbooks as passages, git-SHA-pinned provenance, CC BY
• 🧪 ballast-evalsets — 50k recall probes + 43k hallucination probes
• 🛠️ ballast-cli — uvx openballast: pull or build a corpus, serve a grounding proxy/MCP, profile your model, run the three-arm benchmark
• 📖 Thesis + methodology + numbers
• ⚡ Live demo endpoint (MCP): https://mcp.openballast.org — demo-grade, no SLA, runs on a $0/month stack