Instructions to use Neura-Tech-AI/Nexa-AI-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neura-Tech-AI/Nexa-AI-4B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Neura-Tech-AI/Nexa-AI-4B-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/Nexa-AI-4B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Neura-Tech-AI/Nexa-AI-4B-Instruct")
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/Nexa-AI-4B-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/Nexa-AI-4B-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/Nexa-AI-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Neura-Tech-AI/Nexa-AI-4B-Instruct

SGLang

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

Docker Model Runner
How to use Neura-Tech-AI/Nexa-AI-4B-Instruct with Docker Model Runner:
```
docker model run hf.co/Neura-Tech-AI/Nexa-AI-4B-Instruct
```

Nexa-AI-4B-Instruct

A collaborative open-source large language model developed by Neura Tech AI and Lumina AI.

Overview

Nexa-AI-4B-Instruct is an instruction-tuned large language model built on top of Qwen/Qwen3-4B-Instruct-2507.

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:

General conversation
Instruction following
Coding
Mathematics
Logical reasoning
Tool calling & AI agents
Multilingual understanding (including English, Hindi, Chinese, and more)

Base Model

Base Model: Qwen/Qwen3-4B-Instruct-2507

We sincerely thank the Qwen Team for releasing the Qwen3 model family under the Apache 2.0 License, which made this project possible.

Developers

Project: Nexa AI

Developed by:

Neura Tech AI
Lumina AI

Model Details

Model Name: Nexa-AI-4B-Instruct
Base Model: Qwen/Qwen3-4B-Instruct-2507
Architecture: Transformer Decoder
Parameters: ~4 Billion
Context Length: 262,144 Tokens (Inherited from the base model)
License: Apache-2.0 (Base model license)

Features

High-quality instruction following
Coding assistance
Mathematical reasoning
Agent & tool calling support
Multilingual capabilities
Long-context understanding
Fine-tuned alignment for helpful responses

Performance

	GPT-4.1-nano-2025-04-14	Qwen3-30B-A3B Non-Thinking	Qwen3-4B Non-Thinking	Nexa-AI-4B-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	76.8
Aider-Polyglot	9.8	24.4	13.8	12.9
Alignment
IFEval	74.5	83.7	81.2	83.4
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	69.0
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.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

F16

Model tree for Neura-Tech-AI/Nexa-AI-4B-Instruct

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(1830)

this model

Neura-Tech-AI
/

Nexa-AI-4B-Instruct