Instructions to use Neura-Tech-AI/Neuron-6x4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neura-Tech-AI/Neuron-6x4B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Neura-Tech-AI/Neuron-6x4B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Neura-Tech-AI/Neuron-6x4B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Neura-Tech-AI/Neuron-6x4B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Neura-Tech-AI/Neuron-6x4B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Neura-Tech-AI/Neuron-6x4B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Neura-Tech-AI/Neuron-6x4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Neura-Tech-AI/Neuron-6x4B-Instruct

SGLang

How to use Neura-Tech-AI/Neuron-6x4B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Neura-Tech-AI/Neuron-6x4B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Neura-Tech-AI/Neuron-6x4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Neura-Tech-AI/Neuron-6x4B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Neura-Tech-AI/Neuron-6x4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Neura-Tech-AI/Neuron-6x4B-Instruct with Docker Model Runner:
```
docker model run hf.co/Neura-Tech-AI/Neuron-6x4B-Instruct
```

Neuron-6x4B-Instruct

A next-generation open-source Mixture of Experts (MoE) language model developed by Neura Tech AI.

Overview

Neuron-6x4B-Instruct is a high-performance instruction-tuned Mixture of Experts (MoE) language model built upon the Qwen3 architecture family. It features 6 routing experts, each based on a 4B-scale expert design, providing improved expert specialization, efficient sparse computation, and enhanced multilingual capabilities.

Neuron is designed to deliver strong performance across a wide range of AI workloads while maintaining efficient expert routing for inference.

The model is developed entirely by:

Neura Tech AI

Neuron focuses on delivering a capable multilingual AI assistant with strengths in:

High-efficiency sparse expert routing
Advanced logical reasoning
Software engineering and coding assistance
Mathematics and scientific problem solving
Agentic workflows and tool calling
Long-context document understanding
Multilingual communication across major world languages

Model Architecture & Details

Neuron-6x4B-Instruct utilizes a sparse Mixture of Experts architecture where tokens are dynamically routed to specialized experts during inference, improving efficiency without activating every parameter.

Model Name: Neuron-6x4B-Instruct
Base Architecture: Transformer Decoder (Sparse Mixture of Experts)
Parameters: ~24B Total Parameters
Total Experts: 6 Specialists
Expert Size: ~4 Billion Parameters per Expert
Active Parameters: ~4B Parameters Routed per Token (Dynamic Routing)
Context Length: 262,144 Tokens
License: Apache-2.0

Developer

Project: Neuron

Developed by:

Neura Tech AI

Base Model Acknowledgment

We sincerely thank the Qwen Team for releasing the Qwen3 model family under the Apache 2.0 License, which served as the architectural foundation for this project.

Features

6-Expert Sparse MoE Architecture: Dynamic routing across six specialized experts for efficient inference.
Instruction Optimized: Tuned for helpful, structured, and accurate responses.
Advanced Reasoning: Strong performance on multi-step reasoning and analytical tasks.
Coding Assistant: Designed for software development, debugging, scripting, and code generation.
Large Context Window: Supports contexts up to 262K tokens for long documents and conversations.
Tool Calling Ready: Suitable for AI agents, structured outputs, JSON generation, and automation workflows.
Multilingual: Supports a broad range of major global languages.

Performance

Benchmark results will be published after the official evaluation process is completed.

Category	Status
Knowledge	Coming Soon
Reasoning	Coming Soon
Coding	Coming Soon
Mathematics	Coming Soon
Alignment	Coming Soon
Agent	Coming Soon
Multilingual	Coming Soon

Intended Use

Neuron-6x4B-Instruct is intended for:

Conversational AI
Coding assistants
AI agents
Research
Education
Content generation
Translation
Long-context document analysis
Software engineering workflows

Disclaimer

This model is intended for research and production use where appropriate. Outputs should be reviewed before being relied upon in safety-critical, legal, financial, or medical applications.

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Safetensors

Model size

17B params

Tensor type

BF16

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