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Model: thelamapi/next-4b Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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- 'no'
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license: mit
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tags:
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- turkish
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- türkiye
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- english
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- ai
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- lamapi
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- gemma3
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- next
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- next-x1
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- efficient
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- text-generation
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- open-source
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- 4b
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- huggingface
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- large-language-model
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- llm
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- causal
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- transformer
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- artificial-intelligence
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- machine-learning
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- ai-research
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- natural-language-processing
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- language
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- multilingual
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- multimodal
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- nlp
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- finetuned
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- lightweight
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- creative
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- summarization
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- question-answering
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- chat
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- generative-ai
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- optimized
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- unsloth
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- trl
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- sft
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- chemistry
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- code
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- biology
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- finance
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- legal
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- music
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- art
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- state-of-the-art
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- climate
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- medical
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- agent
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- text-generation-inference
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- merge
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- dense
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pipeline_tag: image-text-to-text
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datasets:
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- mlabonne/FineTome-100k
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- ITCL/FineTomeOs
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- Gryphe/ChatGPT-4o-Writing-Prompts
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- dongguanting/ARPO-SFT-54K
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- GreenerPastures/All-Your-Base-Full
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- Gryphe/Opus-WritingPrompts
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- HuggingFaceH4/MATH-500
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- mlabonne/smoltalk-flat
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- mlabonne/natural_reasoning-formatted
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- OpenSPG/KAG-Thinker-training-dataset
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- uclanlp/Brief-Pro
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- CognitiveKernel/CognitiveKernel-Pro-SFT
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- SuperbEmphasis/Claude-4.0-DeepSeek-R1-RP-SFWish
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- QuixiAI/dolphin-r1
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- mlabonne/lmsys-arena-human-sft-55k
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library_name: transformers
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---
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<img src='assets/banner.png'>
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# 🚀 Next 4B (s330)
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### *Türkiye’s First Vision-Language Model — Efficient, Multimodal, and Reasoning-Focused*
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[](https://opensource.org/licenses/MIT)
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[]()
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[](https://huggingface.co/Lamapi/next-4b)
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[](https://discord.gg/XgH4EpyPD2)
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---
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## 📖 Overview
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**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:
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* Understanding and generating **text and image descriptions**.
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* Efficient reasoning and context-aware multimodal outputs.
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* Turkish support with multilingual capabilities.
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* Low-resource deployment using **8-bit quantization** for consumer-grade GPUs.
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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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---
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# Our Next 1B and Next 4B models are leading to all of the tiny models in benchmarks.
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<table>
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<thead>
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<tr>
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<th>Model</th>
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<th>MMLU (5-shot) %</th>
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<th>MMLU-Pro %</th>
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<th>GSM8K %</th>
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<th>MATH %</th>
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</tr>
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</thead>
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<tbody>
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<tr class="next">
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<td data-label="Model">Next 4B preview</td>
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<td data-label="MMLU (5-shot) %">84.6</td>
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<td data-label="MMLU-Pro %">66.9</td>
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<td data-label="GSM8K %">82.7</td>
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<td data-label="MATH %"><strong>70.5</strong></td>
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</tr>
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<tr class="next">
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<td data-label="Model">Next 1B</td>
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<td data-label="MMLU (5-shot) %"><strong>87.3</strong></td>
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<td data-label="MMLU-Pro %"><strong>69.2</strong></td>
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<td data-label="GSM8K %"><strong>90.5</strong></td>
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<td data-label="MATH %">70.1</td>
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</tr>
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<tr>
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<td data-label="Model">Qwen 3 0.6B</td>
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<td data-label="MMLU (5-shot) %">52.81</td>
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<td data-label="MMLU-Pro %">37.6</td>
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<td data-label="GSM8K %">60.7</td>
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<td data-label="MATH %">20.5</td>
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</tr>
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<tr>
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<td data-label="Model">Llama 3.2 1B</td>
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<td data-label="MMLU (5-shot) %">49.3</td>
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<td data-label="MMLU-Pro %">44.4</td>
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<td data-label="GSM8K %">11.9</td>
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<td data-label="MATH %">30.6</td>
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</tr>
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</tbody>
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</table>
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---
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# Also, our Next 14b model is leading to state-of-the-art models in some of the Benchmarks.
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<table>
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<thead>
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<tr>
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<th>Model</th>
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<th>MMLU (5-shot) %</th>
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<th>MMLU-Pro %</th>
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<th>GSM8K %</th>
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<th>MATH %</th>
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</tr>
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</thead>
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<tbody>
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<tr class="next">
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<td><strong>Next 14B (Thinking)</strong></td>
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<td><strong>94.6</strong></td>
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<td><strong>93.2</strong></td>
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<td><strong>98.8</strong></td>
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<td>92.7</td>
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</tr>
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<tr>
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<td>Next 12B</td>
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<td>92.7</td>
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<td>84.4</td>
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<td>95.3</td>
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<td>87.2</td>
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</tr>
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<tr>
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<td>GPT-5</td>
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<td>92.5</td>
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<td>87.0</td>
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<td>98.4</td>
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<td><strong>96.0</strong></td>
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</tr>
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<tr>
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<td>Claude Opus 4.1 (Thinking)</td>
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<td>~92.0</td>
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<td>87.8</td>
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<td>84.7</td>
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<td>95.4</td>
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</tr>
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</tbody>
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</table>
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---
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## 🚀 Installation & Usage
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### Use with vision:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor
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from PIL import Image
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import torch
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model_id = "Lamapi/next-4b"
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model = AutoModelForCausalLM.from_pretrained(model_id)
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processor = AutoProcessor.from_pretrained(model_id) # For vision.
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Read image
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image = Image.open("image.jpg")
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# Create a message in chat format
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messages = [
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{"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."}]},
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{
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"role": "user","content": [{"type": "image", "image": image},
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{"type": "text", "text": "Who is in this image?"}
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]
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}
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]
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# Prepare input with Tokenizer
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = processor(text=prompt, images=[image], return_tensors="pt")
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# Output from the model
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output = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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<div style='width:700px;'>
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<img src='/Lamapi/next-4b/resolve/main/assets/image.jpg' style='height:192px;border-radius:16px;margin-left:225px;'>
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<div style='background-color:rgba(0,140,255,0.5);border-radius:16px;border-bottom-right-radius:0px;padding:3px 10px;width:fit-content;max-width:400px;margin-left:250px;margin-top:-25px;margin-bottom:10px;'>
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Who is in this image?
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</div>
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<div style='background-color:rgba(42,42,40,0.7);border-radius:16px;border-bottom-left-radius:0px;padding:3px 10px;width:fit-content;max-width:400px;'>
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The image shows <strong>Mustafa Kemal Atatürk</strong>, the founder and first President of the Republic of Turkey.
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</div>
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</div>
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### Use without vision:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "Lamapi/next-4b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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# Chat message
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messages = [
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{"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."},
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{"role": "user", "content": "Hello, how are you?"}
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]
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# Prepare input with Tokenizer
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt")
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# Output from the model
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output = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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<div style='width:700px;'>
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<div style='background-color:rgba(0,140,255,0.5);border-radius:16px;border-bottom-right-radius:0px;padding:3px 10px;width:fit-content;max-width:400px;margin-left:250px;margin-top:-15px;margin-bottom:10px;'>
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Hello, how are you?
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</div>
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<div style='background-color:rgba(42,42,40,0.7);border-radius:16px;border-bottom-left-radius:0px;padding:3px 10px;width:fit-content;max-width:400px;'>
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I'm fine, thank you. How are you?
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</div>
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</div>
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---
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## 🎯 Goals
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1. **Multimodal Intelligence:** Understand and reason over images and text.
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2. **Efficiency:** Run on modest GPUs using 8-bit quantization.
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3. **Accessibility:** Open-source availability for research and applications.
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4. **Cultural Relevance:** Optimized for Turkish language and context while remaining multilingual.
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---
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## ✨ Key Features
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| Feature | Description |
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| --------------------------------- | ----------------------------------------------------------------------- |
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| 🔋 Efficient Architecture | Optimized for low VRAM; supports 8-bit quantization for consumer GPUs. |
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| 🖼️ Vision-Language Capable | Understands images, captions them, and performs visual reasoning tasks. |
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| 🇹🇷 Multilingual & Turkish-Ready | Handles complex Turkish text with high accuracy. |
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| 🧠 Advanced Reasoning | Supports logical and analytical reasoning for both text and images. |
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| 📊 Consistent & Reliable Outputs | Reproducible responses across multiple runs. |
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| 🌍 Open Source | Transparent, community-driven, and research-friendly. |
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---
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## 📐 Model Specifications
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| Specification | Details |
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| ------------------ | ---------------------------------------------------------------------------------- |
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| Base Model | Gemma 3 |
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| Parameter Count | 4 Billion |
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| Architecture | Transformer, causal LLM + Vision Encoder |
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| Fine-Tuning Method | Instruction & multimodal fine-tuning (SFT) on Turkish and multilingual datasets |
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| Optimizations | Q8_0, F16, F32 quantizations for low VRAM and high VRAM usage |
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| Modalities | Text & Image |
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| Use Cases | Image captioning, multimodal QA, text generation, reasoning, creative storytelling |
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---
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## 📄 License
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This project is licensed under the **MIT License** — free to use, modify, and distribute. Attribution is appreciated.
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---
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## 📞 Contact & Support
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* 📧 **Email:** [lamapicontact@gmail.com](mailto:lamapicontact@gmail.com)
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* 🤗 **HuggingFace:** [Lamapi](https://huggingface.co/Lamapi)
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---
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> **Next 4B** — Türkiye’s **first vision-language AI**, combining **multimodal understanding, reasoning, and efficiency**.
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[](https://huggingface.co/Lamapi)
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