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Lamapi / next-8b
Model's Last Updated: November 13 2025
text-generation
Introduction of next-8b
Model Details of next-8b
🧠 Next 8B (m427)
Türkiye’s Compact Reasoning AI — Logical, Analytical, and Efficient
📖 Overview
Next 8B is an 8-billion parameter large language model (LLM) built on Qwen 3 architecture , optimized for reasoning and analytical performance . It’s Türkiye’s reasoning-capable compact AI , designed to think, infer, and solve problems efficiently.
Focused purely on cognitive tasks , it excels in problem-solving, abstract logic, and multilingual understanding (Turkish, English, and more).
⚡ Highlights
- 🇹🇷 Türkiye’s compact reasoning AI
- 🧠 Logical, analytical, and inferential reasoning
- 🌍 Multilingual support (Turkish, English, 30+ languages)
- ⚡ Lightweight and efficient
- 💬 Instruction-tuned for dialogue, tutoring, and analysis
📊 Benchmark Performance
| Model | MMLU (5-shot) % | MMLU-Pro % | GSM8K % | MATH % |
|---|---|---|---|---|
| Next 14B (Thinking) | 94.6 | 93.2 | 98.8 | 92.7 |
| Next 12B | 92.7 | 84.4 | 95.3 | 87.2 |
| Next 8B (Thinking) | 91.0 | 88.5 | 96.2 | 88.0 |
| GPT-5 | 92.5 | 87.0 | 98.4 | 96.0 |
| Claude Opus 4.1 (Thinking) | ~92.0 | 87.8 | 84.7 | 95.4 |
🚀 Installation & Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "Lamapi/next-8b"
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 logically, reason efficiently, and answer concisely."},
{"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 |
|---|---|
| 🧠 Efficient Reasoning | Strong in abstract logic, critical thinking, and structured problem-solving. |
| 🇹🇷 Multilingual Intelligence | Deep Turkish understanding with 30+ language support. |
| ⚡ Lightweight & Optimized | Quantized formats (Q8_0, Q4_K_M, FP16) for efficiency. |
| 🧮 Mathematical & Analytical Skill | Handles structured reasoning and moderate complexity problems. |
| 🧩 Non-Vision Architecture | Focused on text-based cognitive tasks. |
| 🏢 Reliable & Consistent | Predictable outputs suitable for professional use. |
📐 Model Specifications
| Specification | Details |
|---|---|
| Base Model | Qwen 3 |
| Parameters | 8 Billion |
| Architecture | Transformer (Causal LLM) |
| Modalities | Text-only |
| Fine-Tuning | Instruction-tuned with reasoning datasets |
| Optimizations | Quantization-ready, FP16 support |
| Primary Focus | Reasoning, logic, decision-making, and language understanding |
🎯 Ideal Use Cases
- Compact Analytical Chatbots
- Research Assistance (scientific/legal)
- Education & Tutoring
- Code & Algorithm Design
- Decision Support Systems
💡 Performance Highlights
- Efficient Reasoning: Compact yet powerful logical reasoning.
- Good Mathematical Understanding: Handles structured problems reliably.
- Lightweight & Fast: Ideal for resource-conscious environments.
- Consistent Outputs: Professional-grade reliability in smaller footprint.
📄 License
Licensed under MIT License — free for commercial and non-commercial use.
📞 Contact & Support
- 📧 Email: [email protected]
- 🤗 HuggingFace: Lamapi
Next 8B — Türkiye’s compact reasoning-capable AI, blending logical depth , analytical efficiency , and lightweight reliability .
Runs of Lamapi next-8b on huggingface.co
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