| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| - hi |
| - ar |
| - ja |
| - ko |
| - fr |
| - de |
| - es |
| - pt |
| - it |
| - ru |
| - tr |
| - vi |
| - th |
| - id |
| - ms |
| - bn |
| - ur |
| - ta |
| - te |
| - mr |
| - gu |
| - pa |
| - fa |
| base_model: |
| - Neura-Tech-AI/Neuron-4B-Instruct |
| - Qwen/Qwen3-4B-Instruct-2507 |
| - Qwen/Qwen3-4B-Thinking-2507 |
| new_version: Neura-Tech-AI/Neuron-46x4B-Instruct |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - Neuron-46x4B-Instruct |
| - Neura Tech AI |
| --- |
| |
| ## Neuron-46x4B-Instruct |
|
|
| «A large-scale, sparse Mixture-of-Experts language model engineered by **Neura Tech AI**, combining 46 specialized experts with efficient sparse activation for high-capacity reasoning and instruction following.» |
|
|
| # Overview |
|
|
| **Neuron-46x4B-Instruct** is a high-capacity instruction-tuned **Mixture-of-Experts (MoE)** language model developed by **Neura Tech AI**. |
|
|
| The model combines **46 specialized experts**, each based on a 4B-scale expert design, resulting in approximately **125B total parameters** while activating only approximately **8B parameters per token** during inference. |
|
|
| This sparse architecture is designed to provide the representational capacity of a much larger model while keeping per-token computation substantially lower than a dense 125B-parameter model. |
|
|
| **Neuron-46x4B-Instruct** is designed for demanding AI workloads including reasoning, coding, multilingual conversations, mathematics, long-context understanding, and agentic applications. |
|
|
| # Model Architecture & Details |
|
|
| - **Model Name:** Neuron-46x4B-Instruct |
| - **Developer:** Neura Tech AI |
| - **Architecture:** Sparse Mixture of Experts (MoE) |
| - **Total Parameters:** ~125B |
| - **Active Parameters:** ~8B per token |
| - **Total Experts:** 46 |
| - **Expert Scale:** ~4B parameters per expert |
| - **Base Model Family:** Qwen3 |
| - **Model Type:** Instruction-Tuned Causal Language Model |
| - **License:** Apache-2.0 |
| - **Primary Format:** Safetensors |
|
|
| # Why Neuron-46x4B-Instruct? |
|
|
| Neuron-46x4B-Instruct is built around the idea that **model capacity and inference efficiency do not necessarily have to scale together**. |
|
|
| Instead of activating the entire model for every token, the MoE routing mechanism dynamically selects a subset of specialized parameters. This allows Neuron to maintain a very large overall parameter capacity while keeping the number of active parameters significantly lower. |
|
|
| With approximately **125B total parameters and ~8B active parameters**, Neuron-46x4B-Instruct is designed to offer a strong balance between: |
|
|
| - Large model capacity |
| - Sparse computation |
| - Expert specialization |
| - Reasoning capability |
| - Instruction following |
| - Multilingual performance |
| - Efficient inference |
|
|
| # Key Features |
|
|
| # 🧠 Large-Scale Sparse MoE |
|
|
| Neuron-46x4B-Instruct contains **46 specialized experts** within a sparse MoE architecture. The router dynamically determines which experts should process each token. |
|
|
| # ⚡ Efficient Active Computation |
|
|
| Although the model contains approximately **125B total parameters**, only around **8B parameters are active per token**, significantly reducing the computational workload compared with activating the entire parameter set. |
|
|
| # 🔬 Expert Specialization |
|
|
| The large expert pool allows different experts to specialize in different patterns, domains, languages, reasoning behaviors, and instruction types. |
|
|
| # 💻 Coding & Software Engineering |
|
|
| Neuron is designed for programming-related workloads including: |
|
|
| - Code generation |
| - Debugging |
| - Code explanation |
| - Scripting |
| - Software architecture |
| - Technical reasoning |
|
|
| # 🧮 Reasoning & Mathematics |
|
|
| The model is intended to handle multi-step analytical tasks, mathematical reasoning, logical problems, and complex instructions. |
|
|
| # 🌍 Multilingual |
|
|
| **Neuron-46x4B-Instruct** supports a broad range of languages, including: |
|
|
| - English |
| - Chinese |
| - Hindi |
| - Arabic |
| - Japanese |
| - Korean |
| - French |
| - German |
| - Spanish |
| - Portuguese |
| - Italian |
| - Russian |
| - Turkish |
| - Vietnamese |
| - Thai |
| - Indonesian |
| - Malay |
| - Bengali |
| - Urdu |
| - Tamil |
| - Telugu |
| - Marathi |
| - Gujarati |
| - Punjabi |
| - Persian |
| - Etc |
|
|
| # 🤖 Agentic & Tool-Use Workloads |
|
|
| The model can be used as a foundation for AI agents, structured generation, automation systems, tool-calling workflows, and other intelligent applications. |
|
|
| # Model Configuration |
|
|
| | **Property** | **Value** | |
| | :--- | :--- | |
| | **Model** | Neuron-46x4B-Instruct | |
| | **Architecture** | Sparse Mixture of Experts (MoE) | |
| | **Total Parameters** | ~125B | |
| | **Active Parameters** | ~8B per token | |
| | **Total Experts** | 46 | |
| | **Expert Size** | ~4B | |
| | **Context Length** | 262,144 Tokens | |
| | **Model Family** | Qwen3 | |
| | **Model Type** | Instruction-Tuned Causal Language Model | |
| | **Task** | Text Generation | |
| | **Precision** | BF16 | |
| | **License** | Apache-2.0 | |
| | **Format** | Safetensors | |
|
|
| # Base Models |
|
|
| **Neuron-46x4B-Instruct** builds upon the Qwen3 model family and incorporates Neura Tech AI's Neuron model work. |
|
|
| # Base Model Acknowledgment |
|
|
| We sincerely thank the **Qwen Team** for developing and openly releasing the Qwen3 model family under the Apache-2.0 license. |
|
|
| We also acknowledge the upstream models and technologies that contributed to the development of the Neuron model family. |
|
|
| # Intended Use |
|
|
| **Neuron-46x4B-Instruct** can be used for: |
|
|
| - Conversational AI |
| - Coding assistants |
| - AI agents |
| - Research |
| - Education |
| - Mathematics |
| - Content generation |
| - Translation |
| - Document analysis |
| - Software engineering |
| - Multilingual applications |
| - Experimental MoE research |
|
|
| # Performance |
|
|
| **Neuron-46x4B-Instruct** is designed as a high-capacity sparse model with approximately **125B total parameters and ~8B active parameters per token**. |
|
|
| # Inference |
|
|
| **Neuron-46x4B-Instruct** is intended to be used with frameworks that support its model architecture and sparse Mixture-of-Experts implementation. |
|
|
| For deployment, users should ensure that their inference framework supports the specific architecture and routing configuration used by the model. |
|
|
| # Limitations |
|
|
| Despite its large parameter capacity, **Neuron-46x4B-Instruct** can still produce incorrect, incomplete, or hallucinated information. |
|
|
| Model outputs should be verified before being used in safety-critical, legal, financial, or medical applications. |
|
|
| Performance may also vary significantly depending on the inference framework, hardware, quantization method, prompt format, and deployment configuration. |
|
|
| # Developed by: |
|
|
| **Neura Tech AI** |
|
|
| Neuron is part of Neura Tech AI's ongoing research into efficient large-scale language models and sparse Mixture-of-Experts architectures. |
|
|
| # License |
|
|
| **Neuron-46x4B-Instruct** is released under the **Apache-2.0 License**. |
|
|
| Please review the license terms and the licenses of all upstream components before using the model in your application. |
|
|
| --- |
| Neuron-46x4B-Instruct — Large capacity. Sparse activation. Specialized intelligence. |
|
|
| # © 2026 Neura Tech AI |
