Lamapi/next-8b

🧠 Next 8B (m427)

Türkiye’s Compact Reasoning AI — Logical, Analytical, and Efficient

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📖 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).

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⚡ 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

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📊 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

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🚀 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))

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🧩 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.

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📐 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

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🎯 Ideal Use Cases

• Compact Analytical Chatbots
• Research Assistance (scientific/legal)
• Education & Tutoring
• Code & Algorithm Design
• Decision Support Systems

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💡 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.

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📄 License

Licensed under MIT License — free for commercial and non-commercial use.

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📞 Contact & Support

• 📧 Email: [email protected]
• 🤗 HuggingFace: Lamapi

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Next 8B — compact reasoning-capable AI, blending logical depth, analytical efficiency, and lightweight reliability.