Instructions to use Neura-Tech-AI/Neuron-V2-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neura-Tech-AI/Neuron-V2-4B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Neura-Tech-AI/Neuron-V2-4B-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/Neuron-V2-4B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Neura-Tech-AI/Neuron-V2-4B-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-V2-4B-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/Neuron-V2-4B-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/Neuron-V2-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Neura-Tech-AI/Neuron-V2-4B-Instruct

SGLang

How to use Neura-Tech-AI/Neuron-V2-4B-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/Neuron-V2-4B-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/Neuron-V2-4B-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/Neuron-V2-4B-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/Neuron-V2-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Neura-Tech-AI/Neuron-V2-4B-Instruct with Docker Model Runner:
```
docker model run hf.co/Neura-Tech-AI/Neuron-V2-4B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Neuron-V2-4B-Instruct

A multilingual instruction-tuned large language model developed by Neura Tech AI.

Overview

Neuron-V2-4B-Instruct is an instruction-tuned language model built on top of Qwen/Qwen3-4B-Instruct-2507.

The goal of Neuron is to provide a powerful open-source AI assistant capable of natural conversations, coding assistance, reasoning, multilingual understanding, and long-context processing.

Developer

Developed by

Neura Tech AI

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.

Model Details

Model Name: Neuron-V2-4B-Instruct
Developer: Neura Tech AI
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

Features

Instruction Following
Chat Assistant
Coding Assistance
Mathematical Reasoning
Logical Reasoning
Long Context Support
Tool Calling Support
Multilingual Understanding
Creative Writing
General Knowledge
Open-source

Supported Languages

Neuron inherits multilingual capabilities from the Qwen3 base model and supports many languages, including:

English
Hindi
Chinese
Japanese
Korean
French
German
Spanish
Italian
Portuguese
Russian
Arabic
Turkish
Vietnamese
Thai
Indonesian
Malay
Bengali
Tamil
Telugu
Marathi
Gujarati
Punjabi
Urdu
Persian (Farsi)

and many more.

Benchmark Results

	GPT-4.1-nano-2025-04-14	Qwen3-30B-A3B Non-Thinking	Qwen3-4B Non-Thinking	Neuron-V2-4B-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.

Intended Use

Neuron-V2-4B-Instruct is suitable for:

AI Assistants
Chatbots
Coding
Education
Research
Content Writing
Translation
Reasoning Tasks
Tool Calling
General NLP Applications

License

This model is released under the Apache-2.0 License inherited from the base model.

Please also comply with the Qwen model license and usage guidelines.

Acknowledgements

This project is built upon the excellent Qwen3 model family released by the Qwen Team.

We sincerely thank the Qwen Team and the open-source AI community for making this project possible.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Neura-Tech-AI/Neuron-V2-4B-Instruct

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(1841)

this model

Quantizations

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