Text Generation
Transformers
Safetensors
GGUF
gemma3_text
turkish
türkiye
english
ai
lamapi
gemma3
next
next-x1
efficient
open-source
1b
huggingface
large-language-model
llm
causal
transformer
artificial-intelligence
machine-learning
ai-research
natural-language-processing
nlp
finetuned
lightweight
creative
summarization
question-answering
chat-model
generative-ai
optimized-model
unsloth
trl
sft
chemistry
biology
finance
legal
music
art
code
climate
medical
agent
text-generation-inference
conversational
| language: | |
| - tr | |
| - ar | |
| - af | |
| - az | |
| - es | |
| - en | |
| - el | |
| - ro | |
| - ru | |
| - rm | |
| - th | |
| - uk | |
| - uz | |
| - pl | |
| - pt | |
| - fa | |
| - sk | |
| - sl | |
| - da | |
| - de | |
| - nl | |
| - fr | |
| - fi | |
| - ka | |
| - hi | |
| - hu | |
| - hy | |
| - ja | |
| - kk | |
| - kn | |
| - ko | |
| - ku | |
| - ky | |
| - la | |
| - lb | |
| - id | |
| - is | |
| - it | |
| - zh | |
| - cs | |
| - vi | |
| - be | |
| - bg | |
| - bs | |
| - ne | |
| - mn | |
| license: mit | |
| tags: | |
| - turkish | |
| - türkiye | |
| - english | |
| - ai | |
| - lamapi | |
| - gemma3 | |
| - next | |
| - next-x1 | |
| - efficient | |
| - text-generation | |
| - open-source | |
| - 1b | |
| - huggingface | |
| - large-language-model | |
| - llm | |
| - causal | |
| - transformer | |
| - artificial-intelligence | |
| - machine-learning | |
| - ai-research | |
| - natural-language-processing | |
| - nlp | |
| - finetuned | |
| - lightweight | |
| - creative | |
| - summarization | |
| - question-answering | |
| - chat-model | |
| - generative-ai | |
| - optimized-model | |
| - unsloth | |
| - trl | |
| - sft | |
| - chemistry | |
| - biology | |
| - finance | |
| - legal | |
| - music | |
| - art | |
| - code | |
| - climate | |
| - medical | |
| - agent | |
| - text-generation-inference | |
| pipeline_tag: text-generation | |
| datasets: | |
| - 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 | |
| library_name: transformers | |
| <img src='assets/banner.png'> | |
| # 🚀 Next-1B (t416) | |
| ### *Lightweight, Efficient, and Türkiye-Focused AI* | |
| [](https://opensource.org/licenses/MIT) | |
| []() | |
| [](https://huggingface.co/Lamapi/next-1b) | |
| --- | |
| ## 📖 Overview | |
| **Next-1B** is a **1-billion parameter causal language model** based on **Gemma 3**, designed for **efficiency, low-resource deployment, and reasoning-focused natural language understanding**. | |
| Key highlights: | |
| * Extremely **lightweight** — can run on consumer GPUs with low VRAM. | |
| * Optimized for **text reasoning, summarization, and creative generation**. | |
| * Supports **Turkish natively** while remaining multilingual. | |
| * Open-source and transparent for research and applications. | |
| Ideal for **developers, students, and organizations** needing **fast, reliable, and low-resource text-generation**. | |
| --- | |
| # Our Next 1B and Next 4B models are leading to all of the tiny models in benchmarks. | |
| <table> | |
| <thead> | |
| <tr> | |
| <th>Model</th> | |
| <th>MMLU (5-shot) %</th> | |
| <th>MMLU-Pro %</th> | |
| <th>GSM8K %</th> | |
| <th>MATH %</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="next"> | |
| <td data-label="Model">Next 4B preview</td> | |
| <td data-label="MMLU (5-shot) %">84.6</td> | |
| <td data-label="MMLU-Pro %">66.9</td> | |
| <td data-label="GSM8K %">82.7</td> | |
| <td data-label="MATH %"><strong>70.5</strong></td> | |
| </tr> | |
| <tr class="next"> | |
| <td data-label="Model">Next 1B <em>Version t327</em></td> | |
| <td data-label="MMLU (5-shot) %"><strong>87.3</strong></td> | |
| <td data-label="MMLU-Pro %"><strong>69.2</strong></td> | |
| <td data-label="GSM8K %"><strong>90.5</strong></td> | |
| <td data-label="MATH %">70.1</td> | |
| </tr> | |
| <tr> | |
| <td data-label="Model">Qwen 3 0.6B</td> | |
| <td data-label="MMLU (5-shot) %">52.81</td> | |
| <td data-label="MMLU-Pro %">37.6</td> | |
| <td data-label="GSM8K %">60.7</td> | |
| <td data-label="MATH %">20.5</td> | |
| </tr> | |
| <tr> | |
| <td data-label="Model">Llama 3.2 1B</td> | |
| <td data-label="MMLU (5-shot) %">49.3</td> | |
| <td data-label="MMLU-Pro %">44.4</td> | |
| <td data-label="GSM8K %">11.9</td> | |
| <td data-label="MATH %">30.6</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| --- | |
| # Also, our Next 14b model is leading to state-of-the-art models in some of the Benchmarks. | |
| <table> | |
| <thead> | |
| <tr> | |
| <th>Model</th> | |
| <th>MMLU (5-shot) %</th> | |
| <th>MMLU-Pro %</th> | |
| <th>GSM8K %</th> | |
| <th>MATH %</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr class="next"> | |
| <td><strong>Next 14B (Thinking)</strong></td> | |
| <td><strong>94.6</strong></td> | |
| <td><strong>93.2</strong></td> | |
| <td><strong>98.8</strong></td> | |
| <td>92.7</td> | |
| </tr> | |
| <tr> | |
| <td>Next 12B</td> | |
| <td>92.7</td> | |
| <td>84.4</td> | |
| <td>95.3</td> | |
| <td>87.2</td> | |
| </tr> | |
| <tr> | |
| <td>GPT-5</td> | |
| <td>92.5</td> | |
| <td>87.0</td> | |
| <td>98.4</td> | |
| <td><strong>96.0</strong></td> | |
| </tr> | |
| <tr> | |
| <td>Claude Opus 4.1 (Thinking)</td> | |
| <td>~92.0</td> | |
| <td>87.8</td> | |
| <td>84.7</td> | |
| <td>95.4</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| --- | |
| ## 🎯 Goals | |
| 1. **Lightweight Efficiency:** Run smoothly on low-resource devices. | |
| 2. **Reasoning-Focused:** Provide logical and coherent text outputs. | |
| 3. **Accessibility:** Fully open-source with clear documentation. | |
| 4. **Multilingual Adaptability:** Turkish-focused but supports other languages. | |
| --- | |
| ## ✨ Key Features | |
| | Feature | Description | | |
| | --------------------------- | --------------------------------------------------------------------- | | |
| | 🔋 Lightweight Architecture | Optimized for low VRAM usage; ideal for small GPUs or CPU deployment. | | |
| | 🇹🇷 Turkish & Multilingual | Handles complex Turkish prompts accurately. | | |
| | 🧠 Reasoning Capabilities | Logical chain-of-thought for question-answering and problem-solving. | | |
| | 📊 Consistent Outputs | Reliable and reproducible results across multiple runs. | | |
| | 🌍 Open Source | Transparent, research-friendly, and community-driven. | | |
| --- | |
| ## 📐 Model Specifications | |
| | Specification | Details | | |
| | ------------------ | ---------------------------------------------------------------------- | | |
| | Base Model | Gemma 3 | | |
| | Parameter Count | 1 Billion | | |
| | Architecture | Transformer, causal LLM | | |
| | Fine-Tuning Method | Instruction fine-tuning (SFT) with Turkish and multilingual datasets | | |
| | Optimizations | Quantization-ready (q8, f16, f32) | | |
| | Use Cases | Text generation, summarization, Q&A, creative writing, reasoning tasks | | |
| --- | |
| ## 🚀 Installation & Usage | |
| ### Use the model: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| model_id = "Lamapi/next-1b" | |
| 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)) | |
| ``` | |
| <div style='width:700px;'> | |
| <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;'> | |
| Hello, how are you? | |
| </div> | |
| <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;'> | |
| I'm fine, thank you. How are you? | |
| </div> | |
| </div> | |
| --- | |
| ## 📄 License | |
| MIT License — free to use, modify, and distribute. Attribution appreciated. | |
| --- | |
| ## 📞 Contact & Support | |
| * 📧 **Email:** [lamapicontact@gmail.com](mailto:lamapicontact@gmail.com) | |
| * 🤗 **HuggingFace:** [Lamapi](https://huggingface.co/Lamapi) | |
| --- | |
| > **Next-1B** — Lightweight, **efficient, and reasoning-focused**, bringing **Turkey’s AI forward** on low-resource hardware. | |
| [](https://huggingface.co/Lamapi) |