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license: mit
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# SmallBase Benchmark

**SmallBase** is an evaluation benchmark designed to measure response coherence in small-scale language models. 

### Benchmark Methodology
* **Dataset Size:** 140 curated evaluation questions.
* **Format:** Each item presents a prompt alongside a pair of responses: a coherent (**Chosen**) response and an incoherent/suboptimal (**Rejected**) response.
* **Evaluation Metric:** Models are evaluated on their ability to correctly prefer/predict the **Chosen** context over the **Rejected** context.
* **Scoring System (Hidden Elo):** Every question carries an underlying, hidden difficulty Elo rating. Models gain Elo for correctly identifying coherent responses on tough questions and lose Elo for failing easier ones, yielding a robust overall skill rating.

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## 🏆 Benchmark Leaderboard

> [!NOTE]
> Below **1,000 Elo**, a model is likely random guessing!

![SmallBase Benchmark Leaderboard](bench.png)

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## Analysis

1. **BananaMind Family Leads Overall Coherence:**
   * `BananaMind-2-Medium-Chat` takes the **#1 spot (1254.5 Elo)**, showing a strong advantage in distinguishing coherent contexts. Chat-tuned variants consistently outperform their base counterparts across the BananaMind lineup.

2. **Ultra-Small Models Punching Above Their Weight:**
   * `MultivexAI/Zupra-1.6-50M-Instruct-Ultra-exp` (**1186.9 Elo**) and `veyra-ai/Veyra-30M-Base` (**1166.6 Elo**) perform remarkably well relative to their small parameter budgets (~30M–50M), rivalling much larger models in context selection.

3. **SupraLabs Demonstrates Strong Baseline Consistency:**
   * The `Supra-50M` model family clusters tightly in the **1134.6–1180.0 Elo** range, demonstrating strong baseline stability across both standard base training and reasoning fine-tunes.

4. **Modern Sub-50M Architecture Gap:**
   * Specialized small architectures (BananaMind, Zupra, Supra, Veyra) substantially outperform classic tiny baselines such as `pythia-31m` (1116.3 Elo) and `TinyStories-28M` (1122.3 Elo), proving that recent training techniques significantly improve context coherence at micro-scales.