Instructions to use Neura-Tech-AI/Neuron-27B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neura-Tech-AI/Neuron-27B-Thinking with Transformers:

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

pipe = pipeline("text-generation", model="Neura-Tech-AI/Neuron-27B-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Neura-Tech-AI/Neuron-27B-Thinking")
model = AutoModelForCausalLM.from_pretrained("Neura-Tech-AI/Neuron-27B-Thinking", 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=256)
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-27B-Thinking 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-27B-Thinking"
# 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-27B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Neura-Tech-AI/Neuron-27B-Thinking

SGLang

How to use Neura-Tech-AI/Neuron-27B-Thinking 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-27B-Thinking" \
    --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-27B-Thinking",
		"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-27B-Thinking" \
        --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-27B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Neura-Tech-AI/Neuron-27B-Thinking with Docker Model Runner:
```
docker model run hf.co/Neura-Tech-AI/Neuron-27B-Thinking
```

Neuron-27B-Thinking

File size: 2,872 Bytes

---
language:
- en
- hi
- zh
- es
- fr
- de
- ar
- ja
- ko
- pt
- ru
- it
- tr
- id
- vi
- th
- pl
- nl
- sv
- da
- no
- fi
- cs
- ro
- hu
- el
- he
- fa
- ur
- bn
- ta
- te
- mr
- gu
- pa
- ml
- kn
- uk
- bg
- sr
- hr
- sk
- sl
- ms
- tl
- sw
- af
tags:
- neuron
- neura-tech-ai
- 27B
- text-generation
- qwen3
- conversational
- reasoning
license: apache-2.0
base_model:
- Qwen/Qwen3.8-27B
pipeline_tag: text-generation
library_name: transformers
---

# 🧠 Neuron-27B-Thinking: The Official Intelligence of Neura Tech AI

**Neuron-27B-Thinking** is a high-performance Large Language Model (LLM) developed by **Neura Tech AI**. It is engineered as a powerful reasoning-focused assistant with strong multilingual capabilities and native thinking support.

---

## 🏢 Organization Identity

* **Company**: Neura Tech AI  
* **Project Name**: Neuron-27B-Thinking  

## 📊 Model Specifications

* **Architecture**: Causal Language Model (Optimized from Qwen3-27B)  
* **Parameters**: \~27 Billion  
* **Precision**: BF16 (Bfloat16)  
* **Context Window**: 262K tokens  
* **Format**: ChatML Compatible with native thinking tags  
* **License**: Apache-2.0  

## 🎯 Core Capabilities

* **Advanced Reasoning**: Strong performance on complex logical, mathematical, and multi-step problems.  
* **Native Thinking Mode**: Supports structured reasoning with `<think>` blocks for transparent thought process.  
* **Multilingual Proficiency**: Highly optimized for English, Hindi, Chinese, Spanish, French, German, Arabic, Japanese, Korean, Portuguese, Russian, etc.  
* **Instruction Following**: Precise adherence to complex user instructions.  
* **Creative Synthesis**: Excellent at generating technical content, explanations, and structured outputs.  

## 🛠️ Quick Start (Python)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Neura-Tech-AI/Neuron-27B-Thinking"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Who are you?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
```
## 📜 License

This model is released under the **Apache-2.0 License**. You are free to use, modify, and distribute it, provided you credit **Neura Tech AI** as the original developer.
Base model weights are derived from the **Qwen3.8 family**. Please respect the original Qwen license terms where applicable.

---

# © 2026 Neura Tech AI. All rights reserved.