Europa-B1

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} .kz-brief-item,.kz-brief-item:first-child,.kz-brief-item:last-child { padding:16px 0; border-right:0; border-bottom:1px solid var(--border); } .kz-brief-item:last-child { border-bottom:0; } .kz-source { grid-template-columns:1fr; gap:6px; } .kz-footer { margin:52px 20px 0; flex-direction:column; } } </style> <div class="kz"> <header class="kz-hero kz-hero--plain"> <div class="kz-ident"> <div class="kz-hero-main"> <h1 class="kz-name">Europa B1</h1> <span class="kz-base"><span class="dot"></span>Kozu AI Research · 9.41B · Apache 2.0</span> <div class="kz-chip-cloud"> <span class="kz-chip"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M5 3h14v2H5zm0 16h14v2H5zM3 5h2v14H3zm16 0h2v14h-2zM9 7h6v2H9zm0 8h6v2H9zM7 9h2v6H7zm8 0h2v6h-2z"/></svg>BF16 weights</span> <span class="kz-chip">Reasoning + instruction</span> <span class="kz-chip">Report EB1-2026.07</span> </div> </div> </div> </header> <div class="kz-thesis"><strong>More capability. Less deliberation.</strong></div> <section class="kz-section"> <div class="kz-shead"><div class="kz-shead-left"><span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M3 3h18v2H3zm0 16h18v2H3zM3 7h12v2H3zm0 4h18v2H3zm0 4h12v2H3z"/></svg></span><h2 class="kz-stitle">Release brief</h2></div><span class="kz-snum">01</span></div> <p class="kz-lead">Europa B1 is a 9B-class reasoning and instruction system. In like-for-like full-budget evaluation, it improves six of seven reported capability rows and reduces thinking tokens on every task where token counts were recorded.</p> <p class="kz-copy">Europa B1 reaches <strong>0.853 GSM8K flexible</strong> and <strong>0.563 MMLU-Pro</strong> in the recorded run. Mean thinking tokens fall by 5.8× on GSM8K and 1.8× on MMLU-Pro.</p> <div class="kz-brief"> <div class="kz-brief-item"><strong>5.8×</strong><span>fewer GSM8K think tokens</span></div> <div class="kz-brief-item"><strong>+0.329</strong><span>MMLU-Pro absolute delta</span></div> <div class="kz-brief-item"><strong>0.455</strong><span>held-out reasoning accuracy · n=200</span></div> </div> </section> <section class="kz-section"> <div class="kz-shead"><div class="kz-shead-left"><span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M4 2h16v2H4zm0 18h16v2H4zM2 4h2v16H2zm18 0h2v16h-2zM6 15h3v3H6zm5-5h3v8h-3zm5-4h3v12h-3z"/></svg></span><h2 class="kz-stitle">Evaluation</h2></div><span class="kz-snum">02</span></div> <p class="kz-lead">The table reports matched comparisons under the same open harness, generation budget, sampling settings, and seed. Values are run-local and should not be compared with vendor-published numbers produced by other evaluation stacks.</p> <div class="kz-table-wrap"> <table class="kz-table"> <thead><tr><th>Full-budget benchmark</th><th class="num">Reference</th><th class="num">Europa B1</th><th class="num">Delta</th><th class="num">Think tokens · ref → B1</th></tr></thead> <tbody> <tr><td>GSM8K · flexible</td><td class="num">0.800</td><td class="num model">0.853</td><td class="num"><span class="kz-up">+0.053</span></td><td class="num">1,444 → 250</td></tr> <tr><td>MMLU-Pro</td><td class="num">0.234</td><td class="num model">0.563</td><td class="num"><span class="kz-up">+0.329</span></td><td class="num">1,411 → 793</td></tr> <tr><td>IFEval · instruction loose</td><td class="num">0.433</td><td class="num model">0.454</td><td class="num"><span class="kz-up">+0.021</span></td><td class="num">4,874 → 4,442</td></tr> <tr><td>IFEval · instruction strict</td><td class="num">0.433</td><td class="num model">0.445</td><td class="num"><span class="kz-up">+0.012</span></td><td class="num">—</td></tr> <tr><td>IFEval · prompt loose</td><td class="num">0.253</td><td class="num model">0.280</td><td class="num"><span class="kz-up">+0.027</span></td><td class="num">—</td></tr> <tr><td>IFEval · prompt strict</td><td class="num">0.253</td><td class="num model">0.273</td><td class="num"><span class="kz-up">+0.020</span></td><td class="num">—</td></tr> <tr><td>GSM8K · strict format</td><td class="num">0.633</td><td class="num model">0.593</td><td class="num"><span class="kz-down">−0.040</span></td><td class="num">—</td></tr> </tbody> </table> </div> <p class="kz-caption">lm-eval 0.4.12 · 32,768-token generation budget · temperature 1.0 · top_p 0.95 · presence penalty 1.5 · thinking enabled · GSM8K/IFEval n=150 · MMLU-Pro n=25 per subtask · seed 42.</p> <ul class="kz-notes"> <li>The internal held-out math set is contamination-filtered with verified ground truth: accuracy moved from 0.200 to 0.455 at n=200. A separate 12-check instruction suite moved from 0.917 to 0.833; its small sample makes it directional, not authoritative.</li> <li>IFEval scores the complete response, including the thinking block. Shorter reasoning can therefore improve constraint scores; the behavior affects both compared models.</li> <li>The n=150 IFEval run is the primary instruction-following result and improves all four reported metrics. Lipogram-style constraints remain narrow and unreliable, succeeding roughly 40% of the time in repeated checks.</li> <li>GSM8K strict rewards an unrequested <code>#### N</code> ending. The recorded decline is 0.040; request required formats explicitly.</li> <li>Raw evaluation artifacts are retained under <a href="./bench/">bench/</a> in this repository.</li> </ul> </section> <section class="kz-section"> <div class="kz-shead"><div class="kz-shead-left"><span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M7 2h10v2H7zM5 4h14v16H5zm2 2v12h10V6zm2 2h6v2H9zm0 4h6v2H9z"/></svg></span><h2 class="kz-stitle">Data &amp; development</h2></div><span class="kz-snum">03</span></div> <p class="kz-lead"><strong>kozu_reasoning_v1.1</strong> is a roughly 10k-example blend of verified reasoning traces and human-authored instruction data. The release uses a three-layer quality process for the reasoning portion and a dedicated 800-example format-adherence slice.</p> <div class="kz-source"><strong>Reasoning · ~5k</strong><span>Kozu’s Kuiper trace inverter was designed to recreate Mythos/Fable-style reasoning from answer-scrubbed human reference solutions in <a href="https://huggingface.co/datasets/openai/gsm8k">GSM8K</a> and <a href="https://huggingface.co/datasets/AI-MO/NuminaMath-1.5">NuminaMath 1.5</a>. Retained traces passed symbolic answer verification, derivation checks, deterministic defect scans, and an adversarial judge panel.</span></div> <div class="kz-source"><strong>Instruction · ~5k</strong><span>Human-authored pairs used from <a href="https://huggingface.co/datasets/databricks/databricks-dolly-15k">Databricks Dolly 15k</a> and <a href="https://huggingface.co/datasets/OpenAssistant/oasst2">OpenAssistant OASST2</a>.</span></div> <div class="kz-source"><strong>Format · 800</strong><span>Deterministic rewraps of verified rows for explicitly requested formats including JSON, boxed answers, and named answer markers.</span></div> <details><summary>Source licenses and attribution</summary><div class="kz-detail">GSM8K: MIT. NuminaMath 1.5: Apache-2.0. Databricks Dolly 15k: CC-BY-SA-3.0. OpenAssistant OASST2: Apache-2.0. Reasoning traces were generated with Kuiper-R1 by Kozu AI.</div></details> </section> <section class="kz-section"> <div class="kz-shead"><div class="kz-shead-left"><span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M3 3h18v18H3zm2 2v14h14V5zm2 3h2v2H7zm3 3h2v2h-2zm-3 3h2v2H7zm6 0h4v2h-4z"/></svg></span><h2 class="kz-stitle">Deployment</h2></div><span class="kz-snum">04</span></div> <p class="kz-copy">The repository includes merged BF16 weights, tokenizer assets, processor configuration, generation configuration, and the model chat template. Thinking is enabled by default and appears inside <code>&lt;think&gt;…&lt;/think&gt;</code> before the final answer.</p> <pre><code>vllm serve Michael-Kozu/Europa-B1 --served-model-name europa-b1 \ --max-model-len 8192 --gpu-memory-utilization 0.85 --trust-remote-code</code></pre> <p class="kz-caption">Recommended sampling: temperature 0.6–1.0 and top_p 0.95. Request required output formats explicitly.</p> </section> <section class="kz-section"> <div class="kz-shead"><div class="kz-shead-left"><span class="kz-sglyph"><svg class="kz-icon" viewBox="0 0 24 24" aria-hidden="true"><path fill="currentColor" d="M11 2h2v2h-2zm-4 2h10v2H7zM5 6h14v14H5zm2 2v10h10V8zm4 2h2v4h-2zm0 6h2v2h-2z"/></svg></span><h2 class="kz-stitle">Limitations</h2></div><span class="kz-snum">05</span></div> <ul class="kz-notes" style="margin-top:0"> <li>This is a research preview, not a safety-certified or production-guaranteed system.</li> <li>The strongest evidence covers English reasoning and instruction following. Performance outside those domains is not established here.</li> <li>Letter-avoidance constraints are unreliable, and silent few-shot format imitation remains weaker than explicit format requests.</li> <li>Long reasoning may still be incorrect. Verify outputs for consequential decisions and domain-specific use.</li> </ul> </section> <footer class="kz-footer"><span>Kozu AI · Europa B1</span><span>Apache-2.0 · Model release</span></footer> </div>