🚀 Next 12B (m200)

Türkiye's Advanced Vision-Language Model — High Performance, Multimodal, and Enterprise-Ready

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📖 Overview

Next 12B is a 12-billion parameter multimodal Vision-Language Model (VLM) based on Gemma 3, fine-tuned to deliver exceptional performance in both text and image understanding. This is Türkiye's most advanced open-source vision-language model, designed for:

• Superior understanding and generation of text and image descriptions.
• Advanced reasoning and context-aware multimodal outputs.
• Professional-grade Turkish support with extensive multilingual capabilities.
• Enterprise-ready deployment with optimized quantization options.

This model is ideal for enterprises, researchers, and organizations who need a state-of-the-art multimodal AI capable of complex visual understanding, advanced reasoning, and creative generation.

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Next 12B sets new standards for medium-sized models across all major benchmarks.

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🚀 Installation & Usage

Use with vision:

from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor
from PIL import Image
import torch

model_id = "Lamapi/next-12b"

model = AutoModelForCausalLM.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id) # For vision.
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Read image
image = Image.open("image.jpg")

# Create a message in chat format
messages = [
  {"role": "system","content": [{"type": "text", "text": "You are Next-X1, a smart and concise AI assistant trained by Lamapi. Always respond in the user's language. Proudly made in Turkey."}]},

  {
      "role": "user","content": [{"type": "image", "image": image},
      {"type": "text", "text": "Who is in this image?"}
    ]
  }
]

# Prepare input with Tokenizer
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image], return_tensors="pt")

# Output from the model
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Who is in this image? The image shows Mustafa Kemal Atatürk, the founder and first President of the Republic of Turkey.

Use without vision:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Lamapi/next-12b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Chat message
messages = [
    {"role": "system", "content": "You are Next-X1, a smart and concise AI assistant trained by Lamapi. Always respond in the user's language. Proudly made in Turkey."},
    {"role": "user", "content": "Hello, how are you?"}
]

# Prepare input with Tokenizer
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")

# Output from the model
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Hello, how are you? I'm fine, thank you. How are you?

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🎯 Goals

1. Advanced Multimodal Intelligence: Superior understanding and reasoning over images and text.
2. Enterprise-Grade Performance: High accuracy and reliability for production deployments.
3. Efficiency: Optimized for professional GPUs with flexible quantization options.
4. Accessibility: Open-source availability for research and commercial applications.
5. Cultural Excellence: Best-in-class Turkish language support while maintaining multilingual capabilities.

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✨ Key Features

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📐 Model Specifications

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💡 Performance Highlights

• MMLU Excellence: 91.8% on MMLU benchmark, demonstrating comprehensive knowledge across diverse domains
• Mathematical Prowess: 81.2% on MATH benchmark, excelling in complex mathematical reasoning
• Problem Solving: 94.3% on GSM8K, showcasing superior word problem solving capabilities
• Professional Reasoning: 78.4% on MMLU-Pro, handling advanced professional-level questions

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🎨 Use Cases

• Enterprise Content Generation: High-quality multilingual content creation
• Advanced Visual Analysis: Detailed image understanding and description
• Educational Applications: Complex tutoring and explanation systems
• Research Assistance: Literature review and data analysis
• Creative Writing: Story generation and creative content
• Technical Documentation: Code documentation and technical writing
• Customer Support: Multilingual customer service automation
• Data Extraction: Visual document processing and information extraction

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📄 License

This project is licensed under the MIT License — free to use, modify, and distribute for commercial and non-commercial purposes. Attribution is appreciated.

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📞 Contact & Support

• 📧 Email: lamapicontact@gmail.com
• 🤗 HuggingFace: Lamapi

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Next 12B — Türkiye's most advanced vision-language AI, combining state-of-the-art multimodal understanding, superior reasoning, and enterprise-grade reliability.