---
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

---

## 🔍 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.

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.

---

## ⚙️ Key Features

* 🧩 **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.

---

## 📊 Evaluation

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 |

---

## 🧬 Design Philosophy

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.”

---

## 🧬 Pre-Training at Trillion Scale

The OpenModel architecture was engineered for trillion-scale efficiency — ensuring stability and scalability across 1e25–1e26 FLOPs of compute.

Architectural Innovations

- ⚙️ 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

---

## 💡 Applications

* 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

---

## 🛡️ Responsible AI

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.

---

## 📦 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 |

---

## 🧭 Citation

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}},
}
```