| --- |
| license: apache-2.0 |
| pipeline_tag: text-generation |
| datasets: |
| - thenexthub/OpenData-1T |
| --- |
| |
| # 🧠 OpenModel-1T-A50B-Instruct |
|
|
| - **Repository:** `thenexthub/OpenModel-1T-A50B-Instruct` |
| - **Organization:** NeXTHub |
| - **Model Type:** Mixture-of-Experts (MoE) Large Language Model |
| - **Parameters:** 1 Trillion total | 50 Billion active per forward pass |
| - **Context Length:** 128K tokens |
| - **Architecture:** Evo-CoT MoE Transformer (Evolutionary Chain-of-Thought) |
| - **Training Tokens:** 20+ Trillion reasoning-dense, high-quality tokens |
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| --- |
|
|
| ## 🔍 Overview |
|
|
| **OpenModel-1T-A50B-Instruct** represents a major leap in NeXTHub’s pursuit of scalable, efficient, and deeply reasoning general-purpose AI. |
| 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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|
|
| ## ⚙️ Key Features |
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|
| * 🧩 **1T Total | 50B Active MoE Design:** Trillion-parameter scale with sparse activation for exceptional throughput efficiency. |
| * 🧠 **Evo-CoT Training:** Evolutionary chain-of-thought reinforcement — model learns to reason *about* its own reasoning. |
| * 📚 **20T+ Token Corpus:** Pre-trained on a curated, reasoning-dense dataset spanning code, math, science, multilingual text, and human reasoning. |
| * ⏱️ **128K Context Window:** Long-context comprehension for entire projects, books, or datasets. |
| * 🧮 **Reasoning-Optimized Objective:** Curriculum emphasizing precision in long-form logic and mathematical reasoning. |
| * 🧩 **Cross-Domain Instruction Tuning:** Fine-tuned for professional reasoning, code synthesis, mathematics, and complex dialogue. |
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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: |
|
|
| * **DeepSeek-V3.1-Terminus** |
| * **Kimi-K2-Instruct-0905** |
| * **GPT-5-main (API)** |
| * **Gemini-2.5-Pro (API)** |
|
|
| ### 🧩 Benchmark Results |
|
|
| | Domain | Benchmark | OpenModel-1T-A50B-Instruct | SOTA Comparison | |
| | :---------------------------------- | :----------------- | :--------------------------------------------------------------------- | :------------------------------- | |
| | **Mathematics (Competition-Level)** | AIME-25 | **Extended Pareto frontier** of reasoning length vs. accuracy | ✓ Superior | |
| | **Professional Math** | MATH-500 | Outperforms by **+6.2%** over DeepSeek-V3.1 | ✓ Superior | |
| | **Logical Reasoning** | ARC-C / GPQA | Demonstrates **state-of-the-art coherence** and low hallucination rate | ✓ Superior | |
| | **Code Generation** | HumanEval+ / MBPP+ | Outperforms Kimi-K2-Instruct by **~8% pass@1** | ✓ Superior | |
| | **General Dialogue** | MT-Bench | Comparable to GPT-5-main; improved factual grounding | ✓ On Par / Better in Logic Depth | |
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| --- |
|
|
| ## 🧬 Design Philosophy |
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|
| OpenModel-1T was built not just to scale intelligence, but to **evolve it**. |
| The Evo-CoT process simulates intellectual growth — allowing reasoning pathways to mutate, recombine, and self-select under performance feedback, akin to neural evolution. |
| This architecture fuses **cognitive diversity** with **efficiency**, enabling the model to “think deeper, not longer.” |
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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 |
| - 🧩 MTP Layers – enhanced compositional reasoning |
| - 🚀 Aux-loss-free, sigmoid-scoring expert routing with zero-mean updates |
| - 🧠 QK Normalization – fully stable convergence at scale |
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| --- |
|
|
| ## 💡 Applications |
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| * Autonomous code generation and debugging |
| * AI-assisted scientific research |
| * Complex data analytics and mathematical modeling |
| * Multi-agent collaboration and orchestration |
| * Educational tutoring and theorem proving |
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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. |
| Safety layers include prompt-level moderation, reasoning self-checks, and toxicity filters. |
| The model does **not** produce or endorse harmful, biased, or illegal content. |
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| --- |
|
|
| ## 📦 Technical Specs |
|
|
| | Specification | Detail | |
| | :-------------------- | :------------------------------------------ | |
| | **Total Parameters** | 1 Trillion | |
| | **Active Parameters** | 50 Billion | |
| | **Architecture** | Transformer-MoE with Evo-CoT | |
| | **Training Tokens** | 20+ Trillion | |
| | **Context Length** | 128K | |
| | **Precision** | FP8 / BF16 hybrid | |
| | **License** | Apache-2.0 with AI-Responsible Use Addendum | |
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| --- |
|
|
| ## 🧭 Citation |
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|
| If you use OpenModel-1T in your research or products, please cite: |
|
|
| ``` |
| @misc{thenexthub-openmodel-1t-a50b, |
| title={OpenModel-1T-A50B-Instruct: Open Source, Trillion-Scale MoE Model with Evolutionary Chain-of-Thought}, |
| author={NeXTHub}, |
| year={2025}, |
| howpublished={\url{https://huggingface.co/thenexthub/OpenModel-1T-A50B-Instruct}}, |
| } |
| ``` |
