---
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.