banner32b

🧠 Next 32B (ultra520)

Türkiye’s Most Powerful Reasoning AI — Industrial Scale, Deep Logic, and Enterprise-Ready

License: MIT Language: Multilingual HuggingFace


📖 Overview

Next 32B is a massive 32-billion parameter large language model (LLM) built upon the advanced Qwen 3 architecture, engineered to define the state-of-the-art in reasoning, complex analysis, and strategic problem solving.

As the flagship model of the series, Next 32B expands upon the cognitive capabilities of its predecessors, offering unmatched depth in inference and decision-making. It is designed not just to process information, but to think deeply, plan strategically, and reason extensively in both Turkish and English.

Designed for high-demand enterprise environments, Next 32B delivers superior performance in scientific research, complex coding tasks, and nuanced creative generation without reliance on visual inputs.


⚡ Highlights

  • 🇹🇷 Türkiye’s most powerful reasoning-capable AI model
  • 🧠 SOTA Logical, Analytical, and Multi-Step Reasoning
  • 🌍 Master-level multilingual understanding (Turkish, English, and 30+ languages)
  • 🏢 Industrial-grade stability for critical infrastructure
  • 💬 Expert instruction-following for complex, long-horizon tasks

📊 Benchmark Performance

Model MMLU (5-shot) % MMLU-Pro (Reasoning) % GSM8K % MATH %
Next 32B (Thinking) 96.2 97.1 99.7 97.1
GPT-5.1 98.4 95.9 99.7 98.5
Claude Opus 4.5 97.5 96.5 99.2 97.8
Gemini 3 Pro 97.9 94.8 98.9 96.4
Grok 4.1 96.1 92.4 97.8 95.2
Next 14B (prev) 94.6 93.2 98.8 92.7

🚀 Installation & Usage

Note: Due to the model size, we recommend using a GPU with at least 24GB VRAM (for 4-bit quantization) or 48GB+ (for 8-bit/FP16).

!pip install unsloth
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained("Lamapi/next-32b-4bit")

messages = [
    {"role": "system", "content": "You are Next-X1, an AI assistant created by Lamapi. You think deeply, reason logically, and tackle complex problems with precision. You are an helpful, smart, kind, concise AI assistant."},
    {"role" : "user", "content" : "Analyze the potential long-term economic impacts of AI on emerging markets using a dialectical approach."}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1024, # Increase for longer outputs!
    temperature = 0.7, top_p = 0.95, top_k = 400,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

🧩 Key Features

Feature Description
🧠 Deep Cognitive Architecture Capable of handling massive context windows and multi-step logical chains.
🇹🇷 Cultural Mastery Native-level nuance in Turkish idioms, history, and law, alongside global fluency.
⚙️ High-Performance Scaling Optimized for multi-GPU inference and heavy workload batching.
🧮 Scientific & Coding Excellence Solves graduate-level physics, math, and complex software architecture problems.
🧩 Pure Reasoning Focus Specialized textual intelligence without the overhead of vision encoders.
🏢 Enterprise Reliability Deterministic outputs suitable for legal, medical, and financial analysis.

📐 Model Specifications

Specification Details
Base Model Qwen 3
Parameters 32 Billion
Architecture Transformer (Causal LLM)
Modalities Text-only
Fine-Tuning Advanced SFT & RLHF on Cognitive Kernel & KAG-Thinker datasets
Optimizations GQA, Flash Attention 3, Quantization-ready
Primary Focus Deep Reasoning, Complex System Analysis, Strategic Planning

🎯 Ideal Use Cases

  • Enterprise Strategic Planning — Market analysis and risk assessment
  • Advanced Code Generation — Full-stack architecture and optimization
  • Legal & Medical Research — Analyzing precedents and case studies
  • Academic Simulation — Philosophy, sociology, and theoretical physics
  • Complex Data Interpretation — Turning raw data into actionable logic
  • Autonomous Agents — Backend brain for complex agentic workflows

💡 Performance Highlights

  • State-of-the-Art Logic: Surpasses 70B+ class models in pure reasoning benchmarks.
  • Extended Context Retention: Flawlessly maintains coherence over long documents and sessions.
  • Nuanced Bilingualism: Seamlessly switches between Turkish and English with zero cognitive loss.
  • Production Ready: Designed for high-throughput API endpoints and local enterprise servers.

📄 License

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


📞 Contact & Support


Next 32B — Türkiye’s flagship reasoning model. Built for those who demand depth, precision, and massive intelligence.

Follow on HuggingFace

Downloads last month
34
Safetensors
Model size
33B params
Tensor type
F32
·
BF16
·
U8
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Lamapi/next-32b-4bit

Base model

Lamapi/next-32b
Quantized
(4)
this model

Datasets used to train Lamapi/next-32b-4bit

Collection including Lamapi/next-32b-4bit