Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+- fr
+- de
+- es
+- pt
+- it
+- ja
+- ko
+- ru
+- zh
+- ar
+- fa
+- id
+- ms
+- ns
+- pl
+- ro
+- sr
+- sv
+- tr
+- uk
+- vi
+- hi
+- bn
+library_name: transformers
+inference: false
+---
+
+<style>
+    :root{
+      --bg: #0b0c0f;
+      --panel: #0f1117;
+      --ink: #e9eefc;
+      --muted: #9aa3b2;
+      --brand: #433aac; /* purple */
+      --brand-2: #6b4fe8; /* lighter purple accent */
+      --border: rgba(255,255,255,.08);
+      --glow: rgba(67,58,172,.25);
+      --radius: 16px;
+    }
+    *{ box-sizing: border-box }
+    body{ margin: 0; padding: 28px; background: var(--bg); color: var(--muted); font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; }
+    .card{
+      background: linear-gradient(180deg,rgba(255,255,255,.02),rgba(255,255,255,.00));
+      border:1px solid var(--border);
+      border-radius: var(--radius);
+      padding:16px;
+    }
+    .badge{
+      display:inline-flex;align-items:center;gap:.5rem;
+      padding:.35rem .6rem;border:1px solid var(--border);border-radius:999px;
+      color:var(--muted);font-size:.85rem
+    }
+    .grid{ display:grid; gap:18px }
+    .grid-2{ grid-template-columns:repeat(2,minmax(0,1fr)); }
+    .grid-3{ grid-template-columns:repeat(3,minmax(0,1fr)); }
+    @media(max-width:900px){ .grid-2,.grid-3{ grid-template-columns:1fr } }
+    .kicker{
+      display:inline-block;letter-spacing:.12em;text-transform:uppercase;
+      color:var(--muted);font-size:.75rem;margin-bottom:.5rem
+    }
+    h1,h2,h3{ color:var(--ink); margin:0 0 .4rem 0; line-height:1.1 }
+    h1{ font-size:2.25rem; font-weight:800 }
+    h2{ font-size:1.3rem; font-weight:700 }
+    h3{ font-size:1.05rem; font-weight:700 }
+    p,li{ color:var(--muted); line-height:1.6 }
+    hr{ border:none; height:1px; background:var(--border); margin:28px 0 }
+    a.btn{
+      display:inline-block; padding:.7rem 1rem; border-radius:12px;
+      background: linear-gradient(180deg,var(--brand),#3a2d98);
+      color:var(--ink); text-decoration:none; font-weight:600;
+      box-shadow: 0 10px 30px var(--glow);
+    }
+    a.btn.ghost{
+      background:transparent; color:var(--ink); border:1px solid var(--border)
+    }
+    kbd{
+      background:#0c1322;color:#cfe0ff;border:1px solid #1a2742;border-bottom-color:#142138;
+      padding:.12rem .4rem;border-radius:6px;font-size:.85rem
+    }
+    .codeblock{
+      background:#0b1220;border:1px solid #15233d;border-radius:12px;padding: 8px;overflow:auto;
+      margin: 1rem 0;
+    }
+    .codeblock pre {
+      margin: 0;
+      color: var(--ink);
+    }
+    .tagline{
+      font-size:1.05rem;color:#c6d5ff
+    }
+    .pill{
+      display:inline-flex;align-items:center;gap:.4rem;
+      padding:.35rem .6rem;border-radius:999px;border:1px dashed var(--border);color:#b9c5db
+    }
+    .hero{
+      background:
+        radial-gradient(600px 240px at 20% 0%,rgba(67,58,172,.18),transparent 60%),
+        radial-gradient(600px 240px at 80% 10%,rgba(107,79,232,.12),transparent 60%);
+      border:1px solid var(--border);
+      border-radius:20px; padding:28px
+    }
+    details{
+      border:1px solid var(--border);border-radius:12px;padding:14px;background:rgba(255,255,255,.02)
+    }
+    summary{ cursor:pointer;color:var(--ink);font-weight:700 }
+    blockquote{
+      margin:0;padding:14px;border-left:3px solid var(--brand);background:rgba(67,58,172,.06);
+      border-radius:0 10px 10px 0;color:#8f7ce8
+    }
+    table{ width:100%; border-collapse:collapse; margin: 1rem 0; }
+    th,td{ text-align:left; padding:10px; border-bottom:1px solid var(--border); color:var(--muted); font-size: .9rem; }
+    th{ color:var(--brand-2); font-weight: 700; }
+    .callout{
+      border:1px solid var(--border);border-radius:14px;padding:14px;background:rgba(255,255,255,.02)
+    }
+    .metadata{
+      background: #0a0b0e; border: 1px solid var(--border); border-radius: 12px; 
+      padding: 16px; margin-bottom: 24px; font-family: 'Monaco', 'Menlo', monospace; 
+      font-size: .85rem; color: #8a91a3;
+    }
+</style>
+
+<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-open1-30B-A7B</strong> — Our flagship model featuring advanced reasoning capabilities and multimodal understanding. 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>