Instructions to use Lumina-Ai-Official/Nexa-AI-4x4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lumina-Ai-Official/Nexa-AI-4x4B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Lumina-Ai-Official/Nexa-AI-4x4B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lumina-Ai-Official/Nexa-AI-4x4B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Lumina-Ai-Official/Nexa-AI-4x4B-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 Lumina-Ai-Official/Nexa-AI-4x4B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lumina-Ai-Official/Nexa-AI-4x4B-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": "Lumina-Ai-Official/Nexa-AI-4x4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lumina-Ai-Official/Nexa-AI-4x4B-Instruct

SGLang

How to use Lumina-Ai-Official/Nexa-AI-4x4B-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 "Lumina-Ai-Official/Nexa-AI-4x4B-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": "Lumina-Ai-Official/Nexa-AI-4x4B-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 "Lumina-Ai-Official/Nexa-AI-4x4B-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": "Lumina-Ai-Official/Nexa-AI-4x4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lumina-Ai-Official/Nexa-AI-4x4B-Instruct with Docker Model Runner:
```
docker model run hf.co/Lumina-Ai-Official/Nexa-AI-4x4B-Instruct
```

A newer version of this model is available: Neura-Tech-AI/Nexa-AI-4x4B-Instruct

Nexa-AI-4x4B-Instruct

A collaborative open-source large language model featuring an advanced Mixture of Experts (MoE) architecture, developed by Neura Tech AI and Lumina AI.

Overview

Nexa-AI-4x4B-Instruct is an instruction-tuned, state-of-the-art Mixture of Experts (MoE) model built using the framework and foundations of the Qwen3 architecture family. Featuring a dedicated setup of 4 routing experts each scaled at 4B parameters, it provides exceptional computation balancing and advanced contextual intelligence.

This project is jointly developed by:

Neura Tech AI
Lumina AI

Nexa AI focuses on delivering a capable multilingual AI assistant with strong performance in:

High-efficiency compute routing (MoE)
Complex logical reasoning & thinking loops
Advanced coding assistance & software engineering tasks
Mathematics & multi-step academic problem solving
Tool calling, automation, & autonomous AI agents
Deep multilingual understanding (including English, Hindi, Chinese, and more)

Model Architecture & Details

Unlike standard dense models, this variant leverages a modern sparse MoE setup that maps optimized parameter routing to specialized neural layers dynamically during runtime.

Model Name: Nexa-AI-4x4B-Instruct
Base Architecture: Transformer Decoder (Sparse Mixture of Experts)
Parameters: ~16B Total Parameters
Total Experts: 4 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

Developers

Project: Nexa AI
Developed by:

Neura Tech AI
Lumina AI

Base Model Acknowledgment

We sincerely thank the Qwen Team for releasing the foundational Qwen3 model family under the Apache 2.0 License, which served as the structural baseline for this unified architectural development.

Features

Sparse MoE Architecture: Dynamic token routing through 4 separate specialized expert spaces for optimized intelligence per parameter.
Advanced Thinking & Alignment: Highly tuned instruction-following wrapper optimized for structured responses.
High-Quality Code Generation: Native compatibility with complex data tracking, script generations, and error handling.
Vast Multi-turn Context: Massive 262k context space allows keeping track of long documentation or terminal outputs seamlessly.
Native Tool Integration: Solid structure built for external API calls, JSON function routing, and systematic agentic pipelines.

Performance

Benchmark	GPT-4.1-nano-2025-04-14	Qwen3-30B-A3B Non-Thinking	Qwen3-4B Non-Thinking	Nexa-AI-4x4B-Instruct
Knowledge
MMLU-Pro	62.8	69.1	58.0	69.6
MMLU-Redux	80.2	84.1	77.3	84.2
GPQA	50.3	54.8	41.7	62.0
SuperGPQA	32.2	42.2	32.0	42.8
Reasoning
AIME25	22.7	21.6	19.1	47.4
HMMT25	9.7	12.0	12.1	31.0
ZebraLogic	14.8	33.2	35.2	80.2
LiveBench 20241125	41.5	59.4	48.4	63.0
Coding
LiveCodeBench v6 (25.02-25.05)	31.5	29.0	26.4	35.1
MultiPL-E	76.3	74.6	66.6	76.2
Aider-Polyglot	9.8	24.4	13.8	12.9
Alignment
IFEval	74.5	83.5	81.2	83.8
Arena-Hard v2*	15.9	24.8	9.5	43.4
Creative Writing v3	72.7	68.1	53.6	83.5
WritingBench	66.9	72.2	68.5	83.4
Agent
BFCL-v3	53.0	58.6	57.6	61.9
TAU1-Retail	23.5	38.3	24.3	48.7
TAU1-Airline	14.0	18.0	16.0	32.0
TAU2-Retail	-	31.6	28.1	40.4
TAU2-Airline	-	18.0	12.0	24.0
TAU2-Telecom	-	18.4	17.5	13.2
Multilingualism
MultiIF	60.7	70.8	61.3	79.1
MMLU-ProX	56.2	65.1	49.6	61.6
INCLUDE	58.6	67.8	53.8	60.1
PolyMATH	15.6	23.3	16.6	31.1

*: For reproducibility, we report the win rates evaluated by GPT-4.1.

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for Lumina-Ai-Official/Nexa-AI-4x4B-Instruct

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

Neura-Tech-AI/Nexa-AI-4B-Instruct