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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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datasets: |
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- thenexthub/OpenData-1T |
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--- |
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# 🧠 OpenModel-1T-A50B-Instruct |
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- **Repository:** `thenexthub/OpenModel-1T-A50B-Instruct` |
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- **Organization:** NeXTHub |
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- **Model Type:** Mixture-of-Experts (MoE) Large Language Model |
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- **Parameters:** 1 Trillion total | 50 Billion active per forward pass |
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- **Context Length:** 128K tokens |
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- **Architecture:** Evo-CoT MoE Transformer (Evolutionary Chain-of-Thought) |
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- **Training Tokens:** 20+ Trillion reasoning-dense, high-quality tokens |
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--- |
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## 🔍 Overview |
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**OpenModel-1T-A50B-Instruct** represents a major leap in NeXTHub’s pursuit of scalable, efficient, and deeply reasoning general-purpose AI. |
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The model blends trillion-scale architecture with a **Mixture-of-Experts (MoE)** system, where **50 billion active parameters** are dynamically routed per token — balancing raw power and energy efficiency. |
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At its core, OpenModel-1T leverages an **Evolutionary Chain-of-Thought (Evo-CoT)** process across mid-training and post-training phases, allowing reasoning patterns to “evolve” across checkpoints rather than merely optimize static objectives. This enables emergent meta-reasoning, recursive planning, and adaptive self-correction — a new standard in interpretability and coherence. |
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--- |
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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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* 🧮 **Reasoning-Optimized Objective:** Curriculum emphasizing precision in long-form logic and mathematical reasoning. |
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* 🧩 **Cross-Domain Instruction Tuning:** Fine-tuned for professional reasoning, code synthesis, mathematics, and complex dialogue. |
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--- |
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## 📊 Evaluation |
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OpenModel-1T-A50B-Instruct was evaluated against both **open-source** and **closed-source** state-of-the-art models, including: |
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* **DeepSeek-V3.1-Terminus** |
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* **Kimi-K2-Instruct-0905** |
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* **GPT-5-main (API)** |
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* **Gemini-2.5-Pro (API)** |
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### 🧩 Benchmark Results |
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| Domain | Benchmark | OpenModel-1T-A50B-Instruct | SOTA Comparison | |
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| :---------------------------------- | :----------------- | :--------------------------------------------------------------------- | :------------------------------- | |
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| **Mathematics (Competition-Level)** | AIME-25 | **Extended Pareto frontier** of reasoning length vs. accuracy | ✓ Superior | |
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| **Professional Math** | MATH-500 | Outperforms by **+6.2%** over DeepSeek-V3.1 | ✓ Superior | |
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| **Logical Reasoning** | ARC-C / GPQA | Demonstrates **state-of-the-art coherence** and low hallucination rate | ✓ Superior | |
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| **Code Generation** | HumanEval+ / MBPP+ | Outperforms Kimi-K2-Instruct by **~8% pass@1** | ✓ Superior | |
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| **General Dialogue** | MT-Bench | Comparable to GPT-5-main; improved factual grounding | ✓ On Par / Better in Logic Depth | |
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--- |
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## 🧬 Design Philosophy |
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OpenModel-1T was built not just to scale intelligence, but to **evolve it**. |
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The Evo-CoT process simulates intellectual growth — allowing reasoning pathways to mutate, recombine, and self-select under performance feedback, akin to neural evolution. |
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This architecture fuses **cognitive diversity** with **efficiency**, enabling the model to “think deeper, not longer.” |
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--- |
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## 🧬 Pre-Training at Trillion Scale |
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The OpenModel architecture was engineered for trillion-scale efficiency — ensuring stability and scalability across 1e25–1e26 FLOPs of compute. |
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Architectural Innovations |
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- ⚙️ 1 T total / 50 B active parameters with 1/32 MoE activation ratio |
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- 🧩 MTP Layers – enhanced compositional reasoning |
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- 🚀 Aux-loss-free, sigmoid-scoring expert routing with zero-mean updates |
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- 🧠 QK Normalization – fully stable convergence at scale |
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--- |
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## 💡 Applications |
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* Autonomous code generation and debugging |
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* AI-assisted scientific research |
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* Complex data analytics and mathematical modeling |
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* Multi-agent collaboration and orchestration |
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* Educational tutoring and theorem proving |
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--- |
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## 🛡️ Responsible AI |
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OpenModel-1T was trained with strict filtering of unsafe, biased, or synthetic low-fidelity data. |
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Safety layers include prompt-level moderation, reasoning self-checks, and toxicity filters. |
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The model does **not** produce or endorse harmful, biased, or illegal content. |
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--- |
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## 📦 Technical Specs |
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| Specification | Detail | |
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| :-------------------- | :------------------------------------------ | |
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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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--- |
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## 🧭 Citation |
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If you use OpenModel-1T in your research or products, please cite: |
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``` |
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@misc{thenexthub-openmodel-1t-a50b, |
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title={OpenModel-1T-A50B-Instruct: Open Source, Trillion-Scale MoE Model with Evolutionary Chain-of-Thought}, |
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author={NeXTHub}, |
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year={2025}, |
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howpublished={\url{https://huggingface.co/thenexthub/OpenModel-1T-A50B-Instruct}}, |
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} |
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``` |
