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@@ -28,7 +28,7 @@ At its core, OpenModel-1T leverages an **Evolutionary Chain-of-Thought (Evo-CoT)
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  ## ⚙️ Key Features
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- * 🧩 **1T Total / 50B Active MoE Design:** Trillion-parameter scale with sparse activation for exceptional throughput efficiency.
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  * 🧠 **Evo-CoT Training:** Evolutionary chain-of-thought reinforcement — model learns to reason *about* its own reasoning.
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  * 📚 **20T+ Token Corpus:** Pre-trained on a curated, reasoning-dense dataset spanning code, math, science, multilingual text, and human reasoning.
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  * ⏱️ **128K Context Window:** Long-context comprehension for entire projects, books, or datasets.
@@ -104,7 +104,7 @@ The model does **not** produce or endorse harmful, biased, or illegal content.
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  | **Total Parameters** | 1 Trillion |
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  | **Active Parameters** | 50 Billion |
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  | **Architecture** | Transformer-MoE with Evo-CoT |
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- | **Training Tokens** | 20 Trillion+ |
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  | **Context Length** | 128K |
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  | **Precision** | FP8 / BF16 hybrid |
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  | **License** | Apache-2.0 with AI-Responsible Use Addendum |
 
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  ## ⚙️ Key Features
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+ * 🧩 **1T Total | 50B Active MoE Design:** Trillion-parameter scale with sparse activation for exceptional throughput efficiency.
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  * 🧠 **Evo-CoT Training:** Evolutionary chain-of-thought reinforcement — model learns to reason *about* its own reasoning.
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  * 📚 **20T+ Token Corpus:** Pre-trained on a curated, reasoning-dense dataset spanning code, math, science, multilingual text, and human reasoning.
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  * ⏱️ **128K Context Window:** Long-context comprehension for entire projects, books, or datasets.
 
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  | **Total Parameters** | 1 Trillion |
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  | **Active Parameters** | 50 Billion |
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  | **Architecture** | Transformer-MoE with Evo-CoT |
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+ | **Training Tokens** | 20+ Trillion |
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  | **Context Length** | 128K |
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  | **Precision** | FP8 / BF16 hybrid |
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  | **License** | Apache-2.0 with AI-Responsible Use Addendum |