README.md

---
language:
- tr
- en
- de
- es
- fr
- ru
- zh
- ja
- ko
license: mit
tags:
- turkish
- türkiye
- reasoning
- ai
- lamapi
- gemma3
- next
- next-x1
- text-generation
- open-source
- 14b
- large-language-model
- llm
- transformer
- artificial-intelligence
- machine-learning
- nlp
- multilingual
- instruction-tuned
- chat
- generative-ai
- optimized
- trl
- sft
- cognitive
- analytical
- enterprise
pipeline_tag: text-generation
datasets:
- mlabonne/FineTome-100k
- CognitiveKernel/CognitiveKernel-Pro-SFT
- OpenSPG/KAG-Thinker-training-dataset
- Gryphe/ChatGPT-4o-Writing-Prompts
- QuixiAI/dolphin-r1
- uclanlp/Brief-Pro
library_name: transformers
---

<img src='assets/banner.png'>

# 🧠 Next 14B (l310)

### *Türkiye’s First Reasoning-Capable AI Model — Logical, Analytical, and Enterprise-Ready*

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Language: Multilingual](https://img.shields.io/badge/Language-Multilingual-red.svg)]()
[![HuggingFace](https://img.shields.io/badge/🤗-Lamapi/Next--14B-orange.svg)](https://huggingface.co/Lamapi/next-14b)

---

## 📖 Overview

**Next 14B** is a **14-billion parameter large language model (LLM)** built upon **Qwen 3 architecture**, trained to achieve **superior reasoning and analytical capabilities**.  
It is **Türkiye’s first reasoning-capable AI model**, designed to think, infer, and make decisions — **not just respond**.

Unlike vision-based models, **Next 14B focuses on pure cognitive performance**, mastering complex problem solving, abstract logic, and human-level understanding in both **Turkish and English**.

---

## ⚡ Highlights

- 🇹🇷 **Türkiye’s first reasoning-capable AI model**
- 🧠 **Advanced logical, analytical, and inferential reasoning**
- 🌍 **High multilingual understanding (Turkish, English, and beyond)**
- 🏢 **Enterprise-grade stability and consistency**
- 💬 **Instruction-tuned for dialogue, problem solving, and analysis**

---

## 📊 Benchmark Performance

<table>
  <thead>
    <tr>
      <th>Model</th>
      <th>MMLU (5-shot) %</th>
      <th>MMLU-Pro %</th>
      <th>GSM8K %</th>
      <th>MATH %</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td><strong>Next 14B (Thinking)</strong></td>
      <td><strong>94.6</strong></td>
      <td><strong>93.2</strong></td>
      <td><strong>98.8</strong></td>
      <td>92.7</td>
    </tr>
    <tr>
      <td>Next 12B</td>
      <td>92.7</td>
      <td>84.4</td>
      <td>95.3</td>
      <td>87.2</td>
    </tr>
    <tr class="next">
      <td>Next 8B (Thinking)</td>
      <td>91.0</td>
      <td>88.5</td>
      <td>96.2</td>
      <td>88.0</td>
    </tr>
    <tr>
      <td>GPT-5</td>
      <td>92.5</td>
      <td>87.0</td>
      <td>98.4</td>
      <td><strong>96.0</strong></td>
    </tr>
    <tr>
      <td>Claude Opus 4.1 (Thinking)</td>
      <td>~92.0</td>
      <td>87.8</td>
      <td>84.7</td>
      <td>95.4</td>
    </tr>
  </tbody>
</table>

---

## 🚀 Installation & Usage

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

model_id = "Lamapi/next-14b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

messages = [
    {"role": "system", "content": "You are Next-X1, a reasoning-capable AI assistant created by Lamapi. You think deeply, reason logically, and always answer concisely. Proudly made in Turkey."},
    {"role": "user", "content": "Explain why the sky appears blue using logical reasoning."}
]

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

outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
````

---

## 🧩 Key Features

| Feature                                       | Description                                                                    |
| --------------------------------------------- | ------------------------------------------------------------------------------ |
| 🧠 **Advanced Reasoning**                     | Excels in abstract logic, critical thinking, and long-form analysis.           |
| 🇹🇷 **Cultural & Multilingual Intelligence** | Deep Turkish understanding, alongside fluent English and 30+ languages.        |
| ⚙️ **Optimized for Efficiency**               | Available in quantized formats (Q8_0, Q4_K_M, FP16).                           |
| 🧮 **Mathematical & Analytical Skill**        | Performs exceptionally in structured problem solving and scientific reasoning. |
| 🧩 **Non-Vision Architecture**                | Focused purely on cognitive and linguistic understanding.                      |
| 🏢 **Enterprise Reliability**                 | Consistent, interpretable outputs for professional use cases.                  |

---

## 📐 Model Specifications

| Specification     | Details                                                            |
| ----------------- | ------------------------------------------------------------------ |
| **Base Model**    | Qwen 3                                                             |
| **Parameters**    | 14 Billion                                                         |
| **Architecture**  | Transformer (Causal LLM)                                           |
| **Modalities**    | Text-only                                                          |
| **Fine-Tuning**   | Instruction-tuned and reinforced with cognitive reasoning datasets |
| **Optimizations** | Quantization-ready, FP16 support                                   |
| **Primary Focus** | Reasoning, logic, decision-making, and language understanding      |

---

## 🎯 Ideal Use Cases

* **Analytical Chatbots** for business and enterprise logic
* **Research Assistance** — scientific, legal, or data-heavy reasoning
* **Education & Tutoring** — explain concepts step-by-step
* **Creative Writing** — coherent story logic and worldbuilding
* **Code & Algorithm Design** — reasoning-based code generation
* **Decision Support Systems** — scenario evaluation and inference

---

## 💡 Performance Highlights

* **Superior Reasoning:** Outperforms previous-generation 12B models in logic-based benchmarks.
* **Robust Mathematical Understanding:** Handles symbolic reasoning and complex equations.
* **Consistent Long-Context Memory:** Capable of tracking context across multi-turn conversations.
* **Professional Reliability:** Built for critical enterprise and research applications.

---

## 📄 License

Licensed under the **MIT License** — free for commercial and non-commercial use. Attribution is appreciated.

---

## 📞 Contact & Support

* 📧 **Email:** [[email protected]](mailto:[email protected])
* 🤗 **HuggingFace:** [Lamapi](https://huggingface.co/Lamapi)

---

> **Next 14B** — Türkiye’s first *reasoning-capable* large language model, combining **logical depth**, **analytical intelligence**, and **enterprise reliability**.

[![Follow on HuggingFace](https://img.shields.io/badge/Follow-HuggingFace-yellow?logo=huggingface)](https://huggingface.co/Lamapi)

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