next-4b/README.md

language, license, tags, pipeline_tag, datasets, library_name

language	license	tags	pipeline_tag	datasets	library_name
tr en de ka el ku es sl sk af da nl fa fi fr ga hi hu hy ja kg kk ko ky la lb id it is za zh zu cs vi be bg bs ne mn rm ro ru te th tk tt uk uz ug pl pt no	mit	turkish türkiye english ai lamapi gemma3 next next-x1 efficient text-generation open-source 4b huggingface large-language-model llm causal transformer artificial-intelligence machine-learning ai-research natural-language-processing language multilingual multimodal nlp finetuned lightweight creative summarization question-answering chat generative-ai optimized unsloth trl sft chemistry code biology finance legal music art state-of-the-art climate medical agent text-generation-inference merge dense	image-text-to-text	mlabonne/FineTome-100k ITCL/FineTomeOs Gryphe/ChatGPT-4o-Writing-Prompts dongguanting/ARPO-SFT-54K GreenerPastures/All-Your-Base-Full Gryphe/Opus-WritingPrompts HuggingFaceH4/MATH-500 mlabonne/smoltalk-flat mlabonne/natural_reasoning-formatted OpenSPG/KAG-Thinker-training-dataset uclanlp/Brief-Pro CognitiveKernel/CognitiveKernel-Pro-SFT SuperbEmphasis/Claude-4.0-DeepSeek-R1-RP-SFWish QuixiAI/dolphin-r1 mlabonne/lmsys-arena-human-sft-55k	transformers

🚀 Next 4B (s330)

Türkiye’s First Vision-Language Model — Efficient, Multimodal, and Reasoning-Focused

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📖 Overview

Next 4B is a 4-billion parameter multimodal Vision-Language Model (VLM) based on Gemma 3, fine-tuned to handle both text and images efficiently. It is Türkiye’s first open-source vision-language model, designed for:

• Understanding and generating text and image descriptions.
• Efficient reasoning and context-aware multimodal outputs.
• Turkish support with multilingual capabilities.
• Low-resource deployment using 8-bit quantization for consumer-grade GPUs.

This model is ideal for researchers, developers, and organizations who need a high-performance multimodal AI capable of visual understanding, reasoning, and creative generation.

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Our Next 1B and Next 4B models are leading to all of the tiny models in benchmarks.

Model	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
Next 4B preview	84.6	66.9	82.7	70.5
Next 1B	87.3	69.2	90.5	70.1
Qwen 3 0.6B	52.81	37.6	60.7	20.5
Llama 3.2 1B	49.3	44.4	11.9	30.6

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Also, our Next 14b model is leading to state-of-the-art models in some of the Benchmarks.

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
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

Use with vision:

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

model_id = "Lamapi/next-4b"

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-4b"
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. Multimodal Intelligence: Understand and reason over images and text.
2. Efficiency: Run on modest GPUs using 8-bit quantization.
3. Accessibility: Open-source availability for research and applications.
4. Cultural Relevance: Optimized for Turkish language and context while remaining multilingual.

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✨ Key Features

Feature	Description
🔋 Efficient Architecture	Optimized for low VRAM; supports 8-bit quantization for consumer GPUs.
🖼️ Vision-Language Capable	Understands images, captions them, and performs visual reasoning tasks.
🇹🇷 Multilingual & Turkish-Ready	Handles complex Turkish text with high accuracy.
🧠 Advanced Reasoning	Supports logical and analytical reasoning for both text and images.
📊 Consistent & Reliable Outputs	Reproducible responses across multiple runs.
🌍 Open Source	Transparent, community-driven, and research-friendly.

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📐 Model Specifications

Specification	Details
Base Model	Gemma 3
Parameter Count	4 Billion
Architecture	Transformer, causal LLM + Vision Encoder
Fine-Tuning Method	Instruction & multimodal fine-tuning (SFT) on Turkish and multilingual datasets
Optimizations	Q8_0, F16, F32 quantizations for low VRAM and high VRAM usage
Modalities	Text & Image
Use Cases	Image captioning, multimodal QA, text generation, reasoning, creative storytelling

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📄 License

This project is licensed under the MIT License — free to use, modify, and distribute. Attribution is appreciated.

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📞 Contact & Support

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

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Next 4B — Türkiye’s first vision-language AI, combining multimodal understanding, reasoning, and efficiency.