README.md

---
license: mit
language:
- en
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library_name: transformers
inference: false
---

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<div class="hero">
    <div class="kicker">Open Models</div>
    <h1>Aqui-open1 Model Family</h1>
    <p class="tagline">We hereby present the first series of small open models by Aqui, trained from scratch and with an MIT license. The open1 family delivers state-of-the-art performance across reasoning, mathematics, and coding tasks while maintaining efficient inference capabilities.</p>
    
  <div style="margin-top: 20px; display: flex; gap: 12px; flex-wrap: wrap;">
      <div class="pill">🚀 Released September 7, 2025</div>
      <div class="pill">🔓 MIT Licensed</div>
      <div class="pill">⚡ Efficient Architecture</div>
  </div>
</div>

<div class="grid grid-2" style="margin-top: 28px;">
    <div class="card">
        <h2>open1-1.5B-Instruct</h2>
        <p>Ultra-efficient model optimized for edge deployment and real-time applications.</p>
        <div style="margin: 16px 0;">
            <div class="badge">🧠 1.5B parameters</div>
            <div class="badge">📏 128K context</div>
            <div class="badge">⚡ Fast inference</div>
        </div>
        <a href="https://huggingface.co/aquigpt/open1-1.5B-Instruct" class="btn">View Model</a>
    </div>
    
  <div class="card">
      <h2>open1-7.5B-Instruct</h2>
      <p>Balanced model providing exceptional performance across diverse tasks with reasonable compute requirements.</p>
      <div style="margin: 16px 0;">
          <div class="badge">🧠 7.5B parameters</div>
          <div class="badge">📏 32K context</div>
          <div class="badge">🎯 High accuracy</div>
      </div>
      <a href="https://huggingface.co/aquigpt/open1-7.5B-Instruct" class="btn">View Model</a>
  </div>
</div>

<div class="callout" style="margin: 28px 0;">
    <h3>🔜 Coming This Week</h3>
    <p><strong>Aqui-open1-4x8B</strong> — Our biggest non-thinking open model, head-to-head against Qwen3 32B and Llama 3.3 70B. Stay tuned for the most capable open model in the series.</p>
</div>

<hr>

<h2>Benchmark Performance</h2>

<h3>1.5B Model Comparison</h3>
<table>
    <thead>
        <tr>
            <th>Metric</th>
            <th>open1-1.5B-Instruct</th>
            <th>Llama-3.2-1B-Instruct</th>
            <th>LFM2-1.2B</th>
            <th>Qwen3-1.7B</th>
            <th>Gemma-3-1B-it</th>
            <th>SmolLM2-1.7B-Instruct</th>
        </tr>
    </thead>
    <tbody>
        <tr><td>MMLU</td><td><strong>58.5</strong></td><td>46.6</td><td>55.2</td><td>59.1</td><td>40.1</td><td>42.3</td></tr>
        <tr><td>GPQA</td><td><strong>32.3</strong></td><td>28.8</td><td>31.5</td><td>27.7</td><td>21.1</td><td>22.1</td></tr>
        <tr><td>GSM8K</td><td><strong>62.6</strong></td><td>35.7</td><td>58.3</td><td>51.4</td><td>59.6</td><td>48.2</td></tr>
        <tr><td>IFEval</td><td>72.7</td><td>52.4</td><td><strong>74.9</strong></td><td>74.0</td><td>62.9</td><td>56.7</td></tr>
        <tr><td>MGSM</td><td>59.1</td><td>29.1</td><td>55.0</td><td><strong>66.6</strong></td><td>43.6</td><td>38.5</td></tr>
        <tr style="border-top: 2px solid var(--brand);"><td><strong>Average</strong></td><td><strong>57.0</strong></td><td>38.5</td><td>55.0</td><td>55.8</td><td>45.5</td><td>41.6</td></tr>
    </tbody>
</table>

<h3>7.5B Model Comparison</h3>
<table>
    <thead>
        <tr>
            <th>Benchmark</th>
            <th>open1-7.5B-Instruct</th>
            <th>Llama-3.1-8B-Instruct</th>
            <th>LFM-7B</th>
            <th>Qwen3-8B</th>
            <th>Gemma-3-12B-it</th>
            <th>Nemotron-Nano-9B-v2</th>
        </tr>
    </thead>
    <tbody>
        <tr><td>MMLU</td><td><strong>75.8</strong></td><td>68.7</td><td>69.4</td><td>71.6</td><td>72.5</td><td>74.5</td></tr>
        <tr><td>HumanEval</td><td>82.3</td><td>71.7</td><td>70.1</td><td>84.8</td><td>84.8</td><td><strong>86.2</strong></td></tr>
        <tr><td>GPQA Diamond</td><td><strong>52.2</strong></td><td>25.9</td><td>32.9</td><td>45.2</td><td>34.9</td><td>40.8</td></tr>
        <tr><td>IFEval</td><td>78.9</td><td>77.0</td><td>71.6</td><td>83.4</td><td>81.5</td><td><strong>84.3</strong></td></tr>
        <tr><td>AIME 2025</td><td>18.9</td><td>4.3</td><td>2.1</td><td><strong>20.2</strong></td><td>18.3</td><td>20.1</td></tr>
        <tr style="border-top: 2px solid var(--brand);"><td><strong>Average</strong></td><td><strong>61.6</strong></td><td>49.5</td><td>49.2</td><td>61.0</td><td>58.4</td><td>61.2</td></tr>
    </tbody>
</table>

<hr>

<h2>Key Features</h2>

<div class="grid grid-2">
    <div class="card">
        <h3>🎯 Superior Reasoning</h3>
        <p>Exceptional performance on MMLU, GPQA, and mathematical reasoning tasks, outperforming models of similar and larger sizes.</p>
    </div>
    
  <div class="card">
      <h3>⚡ Optimized Architecture</h3>
      <p>Efficient transformer design enabling fast inference while maintaining high accuracy across diverse benchmarks.</p>
  </div>
  
  <div class="card">
      <h3>🌍 Multilingual Support</h3>
      <p>Trained on 20+ languages with robust performance across linguistic boundaries and cultural contexts.</p>
  </div>
  
  <div class="card">
      <h3>🔓 MIT Licensed</h3>
      <p>Complete freedom for commercial use, modification, and redistribution with minimal restrictions.</p>
  </div>
</div>

<hr>

<h2>Usage</h2>

<div class="codeblock">
  <pre>
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    # Load the model and tokenizer
    tokenizer = AutoTokenizer.from_pretrained("aquigpt/open1-1.5B-Instruct")
    model = AutoModelForCausalLM.from_pretrained("aquigpt/open1-1.5B-Instruct")
    
    # Generate text
    inputs = tokenizer("Explain quantum computing:", return_tensors="pt")
    outputs = model.generate(**inputs, max_length=200, temperature=0.7)
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
  </pre>
</div>

<details>
    <summary>Training Details</summary>
    <p>The open1 models were trained from scratch on a diverse, high-quality dataset spanning code, mathematics, reasoning, and multilingual text. Training utilized advanced techniques including:</p>
    <ul>
        <li>Supervised fine-tuning on instruction-following data</li>
        <li>Constitutional AI for alignment and safety</li>
        <li>Advanced attention mechanisms for extended context</li>
        <li>Multi-stage training with curriculum learning</li>
    </ul>
</details>

<blockquote>
    <strong>Note:</strong> These models are designed for research and commercial applications. While they demonstrate strong performance, users should conduct appropriate testing for their specific use cases.
</blockquote>

<div style="text-align: center; margin-top: 40px; color: var(--muted);">
    <p>Built with ❤️ by the Aqui team • MIT • September 2025</p>
</div>