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.