Update README.md

README.md

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+.page { max-width: 860px; margin: 0 auto; padding: 2rem 1rem 3rem; }
+.hero { text-align: center; padding: 3rem 1rem 2.5rem; }
+.hero img { height: 70px; object-fit: contain; margin-bottom: 1.75rem; display: block; margin-left: auto; margin-right: auto; }
+.hero h1 { font-size: 32px; font-weight: 500; letter-spacing: -0.5px; margin-bottom: 0.5rem; }
+.hero p { font-size: 15px; color: var(--color-text-secondary); max-width: 560px; margin: 0 auto 1.75rem; line-height: 1.7; }
+.badges { display: flex; gap: 8px; justify-content: center; flex-wrap: wrap; margin-bottom: 1.75rem; }
+.badge { font-size: 11px; padding: 4px 11px; border-radius: 99px; border: 0.5px solid var(--color-border-secondary); color: var(--color-text-secondary); }
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+.stat { background: var(--color-background-secondary); border-radius: var(--border-radius-md); padding: 1rem; text-align: center; }
+.stat .val { font-size: 22px; font-weight: 500; margin-bottom: 3px; }
+.stat .lbl { font-size: 12px; color: var(--color-text-secondary); }
+.model-card { background: var(--color-background-primary); border: 0.5px solid var(--color-border-secondary); border-radius: var(--border-radius-lg); padding: 1.5rem; }
+.model-name { font-size: 22px; font-weight: 500; margin-bottom: 4px; }
+.model-sub { font-size: 14px; color: var(--color-text-secondary); margin-bottom: 1rem; line-height: 1.6; }
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+}
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+.meta-item .mk { font-size: 11px; color: var(--color-text-secondary); margin-bottom: 3px; }
+.meta-item .mv { font-size: 13px; font-weight: 500; }
+.feature-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(190px, 1fr)); gap: 12px; }
+.feature-card { background: var(--color-background-secondary); border-radius: var(--border-radius-md); padding: 1rem 1.1rem; }
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+.feature-card .fd { font-size: 13px; color: var(--color-text-secondary); line-height: 1.55; }
+.bench-section { margin-bottom: 1.5rem; }
+.bench-title { font-size: 13px; font-weight: 500; margin-bottom: 10px; padding-bottom: 6px; border-bottom: 0.5px solid var(--color-border-tertiary); }
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+td:not(:first-child), th:not(:first-child) { text-align: right; }
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+.framework-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(140px, 1fr)); gap: 8px; }
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+.footer { text-align: center; padding-top: 2rem; font-size: 12px; color: var(--color-text-secondary); line-height: 2; }
 </style>
 
 <div class="page">
 
   <div class="hero">
     <img src="https://i.ibb.co/rGS6dBcf/logo-Astro-X.png" alt="AstroX AI">
+    <p>A frontier-class Mixture-of-Experts language model — competitive with leading closed-source models at a fraction of the training cost. Fully open-source and commercially licensed.</p>
     <div class="badges">
-      <span class="badge accent">MoE Architecture</span>
+      <span class="badge purple">Mixture-of-Experts</span>
       <span class="badge">671B total params</span>
       <span class="badge">37B activated per token</span>
       <span class="badge">128K context</span>
+      <span class="badge">FP8 training</span>
       <span class="badge">MIT License</span>
     </div>
-    <p>A frontier-class Mixture-of-Experts language model. Competitive with leading closed-source models — efficient, powerful, and fully open-source.</p>
-    <div class="links">
-      <a class="link-btn primary" href="https://huggingface.co/teamzero/astrox">HuggingFace</a>
-      <a class="link-btn" href="https://chat.deepseek.com/">Chat Demo</a>
-      <a class="link-btn" href="https://platform.deepseek.com/">API Platform</a>
-      <a class="link-btn" href="https://arxiv.org/abs/2412.19437">Paper</a>
+    <div class="hugging">
+      <span style="font-size:13px; color: var(--color-text-secondary);">huggingface.co/</span><code>teamzero/astrox</code>
     </div>
   </div>
 
@@ -77,94 +84,139 @@
     <div class="stat"><div class="val">671B</div><div class="lbl">Total params</div></div>
     <div class="stat"><div class="val">37B</div><div class="lbl">Active per token</div></div>
     <div class="stat"><div class="val">128K</div><div class="lbl">Context window</div></div>
-    <div class="stat"><div class="val">2.79M</div><div class="lbl">GPU hours</div></div>
+    <div class="stat"><div class="val">2.79M</div><div class="lbl">H800 GPU hours</div></div>
   </div>
 
   <hr class="divider">
 
-  <div style="margin-bottom: 2.5rem;">
-    <div class="section-title">Model</div>
+  <div class="section">
+    <div class="section-label">Model</div>
     <div class="model-card">
-      <div class="model-info">
-        <div class="name">AstroX</div>
-        <div class="sub">Instruction-tuned · Reinforcement Learning · R1 reasoning distillation</div>
-        <div class="pill-row">
-          <span class="pill blue">671B / 37B active</span>
-          <span class="pill teal">128K context</span>
-          <span class="pill amber">FP8</span>
-          <span class="pill purple">MoE</span>
-        </div>
+      <div class="model-name">AstroX</div>
+      <div class="model-sub">Instruction-tuned chat model with reinforcement learning and R1 long-chain-of-thought reasoning distillation. The only available model in the AstroX family.</div>
+      <div class="pill-row">
+        <span class="pill purple">MoE</span>
+        <span class="pill blue">671B / 37B active</span>
+        <span class="pill teal">128K context</span>
+        <span class="pill amber">FP8 weights</span>
+      </div>
+      <div class="model-meta">
+        <div class="meta-item"><div class="mk">Architecture</div><div class="mv">DeepSeekMoE + MLA</div></div>
+        <div class="meta-item"><div class="mk">Experts</div><div class="mv">256 total · 8 active</div></div>
+        <div class="meta-item"><div class="mk">Pre-training data</div><div class="mv">14.8T tokens</div></div>
+        <div class="meta-item"><div class="mk">License</div><div class="mv">MIT + Model Agreement</div></div>
       </div>
-      <a class="dl-btn" href="https://huggingface.co/teamzero/astrox">Download on HuggingFace</a>
     </div>
   </div>
 
-  <div style="margin-bottom: 2.5rem;">
-    <div class="section-title">Key innovations</div>
+  <div class="section">
+    <div class="section-label">Architecture highlights</div>
+    <div class="arch-row">
+      <div class="arch-card">
+        <div class="ak">Attention</div>
+        <div class="av">Multi-head Latent Attention (MLA)</div>
+        <div class="ad">Reduces KV cache memory footprint significantly vs. standard MHA, enabling practical long-context inference.</div>
+      </div>
+      <div class="arch-card">
+        <div class="ak">Load balancing</div>
+        <div class="av">Auxiliary-loss-free strategy</div>
+        <div class="ad">Balances expert load without the performance penalty of traditional auxiliary loss terms.</div>
+      </div>
+      <div class="arch-card">
+        <div class="ak">Training objective</div>
+        <div class="av">Multi-Token Prediction (MTP)</div>
+        <div class="ad">Predicts multiple future tokens simultaneously, improving performance and enabling speculative decoding.</div>
+      </div>
+      <div class="arch-card">
+        <div class="ak">Post-training</div>
+        <div class="av">R1 reasoning distillation</div>
+        <div class="ad">Verification and reflection patterns from DeepSeek-R1 are distilled into the model while keeping output style controlled.</div>
+      </div>
+    </div>
+  </div>
+
+  <div class="section">
+    <div class="section-label">Key innovations</div>
     <div class="feature-grid">
       <div class="feature-card">
-        <div class="ft">Auxiliary-loss-free balancing</div>
-        <div class="fd">Novel MoE load balancing that minimizes performance degradation during routing.</div>
+        <div class="ft">FP8 mixed precision</div>
+        <div class="fd">First large-scale FP8 training validated on a 671B model. Cuts compute cost without quality loss.</div>
       </div>
       <div class="feature-card">
-        <div class="ft">Multi-token prediction</div>
-        <div class="fd">MTP training objective boosts performance and enables speculative decoding.</div>
+        <div class="ft">Zero training instability</div>
+        <div class="fd">No irrecoverable loss spikes and no rollbacks throughout the entire pre-training run.</div>
       </div>
       <div class="feature-card">
-        <div class="ft">FP8 mixed precision</div>
-        <div class="fd">First validated large-scale FP8 training — lower cost, no quality loss.</div>
+        <div class="ft">Full comm/compute overlap</div>
+        <div class="fd">Co-designed algorithms and hardware nearly eliminate the communication bottleneck in cross-node MoE training.</div>
       </div>
       <div class="feature-card">
-        <div class="ft">R1 reasoning distillation</div>
-        <div class="fd">Distills long-chain-of-thought reasoning from DeepSeek-R1 into a standard LLM.</div>
+        <div class="ft">Speculative decoding ready</div>
+        <div class="fd">The MTP module can be repurposed as a draft head for inference acceleration out of the box.</div>
       </div>
     </div>
   </div>
 
-  <div style="margin-bottom: 2.5rem;">
-    <div class="section-title">Benchmark highlights</div>
-    <div class="tabs">
-      <div class="tab active" onclick="switchTab(this,'math')">Math & Reasoning</div>
-      <div class="tab" onclick="switchTab(this,'code')">Code</div>
-      <div class="tab" onclick="switchTab(this,'general')">General</div>
-    </div>
-    <div id="math" class="tab-content active">
-      <table class="benchmark-table">
-        <tr><th>Benchmark</th><th>GPT-4o</th><th>Claude 3.5 Sonnet</th><th>AstroX</th></tr>
-        <tr><td>AIME 2024 (Pass@1)</td><td>9.3</td><td>16.0</td><td class="best">39.2</td></tr>
-        <tr><td>MATH-500 (EM)</td><td>74.6</td><td>78.3</td><td class="best">90.2</td></tr>
-        <tr><td>CNMO 2024 (Pass@1)</td><td>10.8</td><td>13.1</td><td class="best">43.2</td></tr>
-      </table>
+  <div class="section">
+    <div class="section-label">Benchmark performance — math &amp; reasoning</div>
+    <table>
+      <tr><th style="width:44%">Benchmark</th><th>GPT-4o</th><th>Claude 3.5 Sonnet</th><th>AstroX</th></tr>
+      <tr><td>AIME 2024 (Pass@1)</td><td>9.3</td><td>16.0</td><td class="best">39.2</td></tr>
+      <tr><td>MATH-500 (EM)</td><td>74.6</td><td>78.3</td><td class="best">90.2</td></tr>
+      <tr><td>CNMO 2024 (Pass@1)</td><td>10.8</td><td>13.1</td><td class="best">43.2</td></tr>
+      <tr><td>GSM8K (EM)</td><td>—</td><td>—</td><td class="best">89.3</td></tr>
+    </table>
+  </div>
+
+  <div class="section">
+    <div class="section-label">Benchmark performance — code</div>
+    <table>
+      <tr><th style="width:44%">Benchmark</th><th>GPT-4o</th><th>Claude 3.5 Sonnet</th><th>AstroX</th></tr>
+      <tr><td>LiveCodeBench (Pass@1)</td><td>34.2</td><td>32.8</td><td class="best">37.6</td></tr>
+      <tr><td>Codeforces (Percentile)</td><td>23.6</td><td>20.3</td><td class="best">51.6</td></tr>
+      <tr><td>Aider-Polyglot (Acc.)</td><td>16.0</td><td>45.3</td><td class="best">49.6</td></tr>
+      <tr><td>HumanEval-Mul (Pass@1)</td><td>80.5</td><td>81.7</td><td class="best">82.6</td></tr>
+    </table>
+  </div>
+
+  <div class="section">
+    <div class="section-label">Benchmark performance — general</div>
+    <table>
+      <tr><th style="width:44%">Benchmark</th><th>GPT-4o</th><th>Claude 3.5 Sonnet</th><th>AstroX</th></tr>
+      <tr><td>MMLU (EM)</td><td>87.2</td><td>88.3</td><td class="best">88.5</td></tr>
+      <tr><td>Arena-Hard</td><td>80.4</td><td>85.2</td><td class="best">85.5</td></tr>
+      <tr><td>AlpacaEval 2.0</td><td>51.1</td><td>52.0</td><td class="best">70.0</td></tr>
+      <tr><td>DROP (3-shot F1)</td><td>83.7</td><td>88.3</td><td class="best">91.6</td></tr>
+    </table>
+  </div>
+
+  <div class="section">
+    <div class="section-label">Supported inference frameworks</div>
+    <div class="framework-grid">
+      <div class="fw"><div class="fwn">SGLang</div><div class="fwd">Recommended · FP8 + BF16 · NVIDIA + AMD</div></div>
+      <div class="fw"><div class="fwn">vLLM</div><div class="fwd">FP8 + BF16 · pipeline parallelism</div></div>
+      <div class="fw"><div class="fwn">LMDeploy</div><div class="fwd">Offline + online · PyTorch-native</div></div>
+      <div class="fw"><div class="fwn">TensorRT-LLM</div><div class="fwd">BF16 · INT4/INT8 quant</div></div>
+      <div class="fw"><div class="fwn">AMD GPU</div><div class="fwd">via SGLang · FP8 + BF16</div></div>
+      <div class="fw"><div class="fwn">Huawei Ascend</div><div class="fwd">via MindIE · BF16</div></div>
     </div>
-    <div id="code" class="tab-content">
-      <table class="benchmark-table">
-        <tr><th>Benchmark</th><th>GPT-4o</th><th>Claude 3.5 Sonnet</th><th>AstroX</th></tr>
-        <tr><td>LiveCodeBench (Pass@1)</td><td>34.2</td><td>32.8</td><td class="best">37.6</td></tr>
-        <tr><td>Codeforces (Percentile)</td><td>23.6</td><td>20.3</td><td class="best">51.6</td></tr>
-        <tr><td>Aider-Polyglot (Acc.)</td><td>16.0</td><td>45.3</td><td class="best">49.6</td></tr>
-      </table>
+  </div>
+
+  <div class="section">
+    <div class="section-label">Quick start</div>
+    <div style="background: var(--color-background-secondary); border-radius: var(--border-radius-md); padding: 1rem 1.25rem;">
+      <div style="font-size: 12px; color: var(--color-text-secondary); margin-bottom: 8px;">Convert FP8 weights to BF16</div>
+      <code style="font-family: var(--font-mono); font-size: 12px; color: var(--color-text-primary); display: block; line-height: 1.8;">python fp8_cast_bf16.py \<br>&nbsp;&nbsp;--input-fp8-hf-path /path/to/fp8_weights \<br>&nbsp;&nbsp;--output-bf16-hf-path /path/to/bf16_weights</code>
     </div>
-    <div id="general" class="tab-content">
-      <table class="benchmark-table">
-        <tr><th>Benchmark</th><th>GPT-4o</th><th>Claude 3.5 Sonnet</th><th>AstroX</th></tr>
-        <tr><td>MMLU (EM)</td><td>87.2</td><td>88.3</td><td class="best">88.5</td></tr>
-        <tr><td>Arena-Hard</td><td>80.4</td><td>85.2</td><td class="best">85.5</td></tr>
-        <tr><td>AlpacaEval 2.0</td><td>51.1</td><td>52.0</td><td class="best">70.0</td></tr>
-      </table>
+    <div style="background: var(--color-background-secondary); border-radius: var(--border-radius-md); padding: 1rem 1.25rem; margin-top: 8px;">
+      <div style="font-size: 12px; color: var(--color-text-secondary); margin-bottom: 8px;">Run interactive inference (2 nodes · 8 GPUs each)</div>
+      <code style="font-family: var(--font-mono); font-size: 12px; color: var(--color-text-primary); display: block; line-height: 1.8;">torchrun --nnodes 2 --nproc-per-node 8 generate.py \<br>&nbsp;&nbsp;--node-rank $RANK --master-addr $ADDR \<br>&nbsp;&nbsp;--ckpt-path /path/to/AstroX \<br>&nbsp;&nbsp;--config configs/config_671B.json \<br>&nbsp;&nbsp;--interactive --temperature 0.7 --max-new-tokens 200</code>
     </div>
   </div>
 
   <div class="footer">
-    Code: MIT License &nbsp;·&nbsp; Model: Model Agreement &nbsp;·&nbsp; Commercial use supported
+    Code license: MIT &nbsp;·&nbsp; Model license: Model Agreement &nbsp;·&nbsp; Commercial use supported<br>
+    huggingface.co/teamzero/astrox
   </div>
 
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