308 lines
11 KiB
Markdown
308 lines
11 KiB
Markdown
---
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language: tr
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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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- 1b
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- 270m
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- finetune
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- gguf
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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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- 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-model
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- generative-ai
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- optimized-model
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- unsloth
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- trl
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- sft
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- chemistry
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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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- code
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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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pipeline_tag: text-generation
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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-270M (xt330)
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### *Lightweight, Efficient, and Türkiye-Focused AI*
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[](https://opensource.org/licenses/MIT)
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[]()
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[](https://huggingface.co/Lamapi/next-270m)
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[](https://discord.gg/XgH4EpyPD2)
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---
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<style>
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table { width:fit-content; border-collapse:separate; border-spacing:0 3px;font-family:system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;background:rgba(15,22,32,0.4);border-radius:16px;padding: 10px; border:none;transition:.2s all ease;}
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thead th { text-align:center; padding:4px 10px; font-size:13px; text-transform:uppercase; color:rgb(200,200,200);border:none; }
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tbody tr { transition: transform 0.18s ease, box-shadow 0.18s ease; border:none !important;transition:.2s all ease;border-radius:16px;background:rgba(0, 0, 0, 0.38);}
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tbody .turkish:hover {box-shadow:0 6px 15px rgba(0, 0, 0, 0.27);scale:1.01;background:rgba(80, 38, 38, 0.6);}
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tbody .next:hover {box-shadow:0 6px 15px rgba(0, 0, 0, 0.27);scale:1.02;background: rgba(0, 59, 225, 1)}
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tbody tr:hover { box-shadow:0 0px 15px rgba(102, 102, 102, 0.13); background:rgba(139, 139, 139, 0.16)}
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td { padding:8px 10px;border:0px transparent !important;outline:transparent !important; text-align:center; }
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td:first-child { font-weight:600;text-align:left }
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/* tbody .turkish td { background: rgba(255, 0, 0, 0.2) !important; color:rgb(200,200,200); font-weight:400;border:0px !important; scale:1.0; } */
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/* tbody .next td { background: rgba(0, 89, 255, 0.49)!important; color:rgb(200,200,200); font-weight:600;border:0px !important; scale:1.00;outline:none;border:none !important;} */
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.next{
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background: rgba(0, 89, 255, 0.49);
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}
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.turkish{
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background:rgba(51, 34, 34, 0.64);
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}
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tbody tr td:first-child { border-top-left-radius:12px; border-bottom-left-radius:12px; }
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tbody tr td:last-child { border-top-right-radius:12px; border-bottom-right-radius:12px; } strong{
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font-size:16px;font-weight:700;
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}
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em{opacity:0.7;font-size:11px !important;}
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</style>
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## 📖 Overview
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**Next-270M** is a **270-million parameter causal language model** based on **Gemma 3**, designed for **efficiency, low-resource deployment, and reasoning-focused natural language understanding**.
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Key highlights:
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* Extremely **lightweight** — can run on consumer GPUs with low VRAM.
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* Optimized for **text reasoning, summarization, and creative generation**.
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* Supports **Turkish natively** while remaining multilingual.
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* Open-source and transparent for research and applications.
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Ideal for **developers, students, and organizations** needing **fast, reliable, and low-resource text-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 <em>Version s325</em></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 <em>Version t327</em></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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<tr class="turkish">
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<td data-label="Model">Kumru 7B <em>not verified</em></td>
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<td data-label="MMLU (5-shot) %">30.7</td>
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<td data-label="MMLU-Pro %">28.6</td>
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<td data-label="GSM8K %">15.38</td>
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<td data-label="MATH %">6.4</td>
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</tr>
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</tbody>
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</table>
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---
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# Also, our Next Z1 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 data-label="Model">Next Z1 <em>Version l294</em></td>
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<td data-label="MMLU (5-shot) %"><strong>97.3</strong></td>
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<td data-label="MMLU-Pro %"><strong>94.2</strong></td>
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<td data-label="GSM8K %">97.7</td>
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<td data-label="MATH %">93.2</td>
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</tr>
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<tr class="next">
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<td data-label="Model">Next Z1 <em>Version l294</em> (no tool)</td>
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<td data-label="MMLU (5-shot) %">94.7</td>
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<td data-label="MMLU-Pro %">90.1</td>
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<td data-label="GSM8K %">94.5</td>
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<td data-label="MATH %">88.7</td>
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</tr>
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<tr>
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<td data-label="Model">GPT 5</td>
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<td data-label="MMLU (5-shot) %">92.5</td>
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<td data-label="MMLU-Pro %">87.0</td>
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<td data-label="GSM8K %"><strong>98.4</strong></td>
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<td data-label="MATH %"><strong>96.0</strong></td>
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</tr>
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<tr>
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<td data-label="Model">Claude Opus 4.1 (Thinking)</td>
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<td data-label="MMLU (5-shot) %">~92.0</td>
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<td data-label="MMLU-Pro %">87.8</td>
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<td data-label="GSM8K %">84.7</td>
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<td data-label="MATH %">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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## 🎯 Goals
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1. **Lightweight Efficiency:** Run smoothly on low-resource devices.
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2. **Reasoning-Focused:** Provide logical and coherent text outputs.
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3. **Accessibility:** Fully open-source with clear documentation.
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4. **Multilingual Adaptability:** Turkish-focused but supports other languages.
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---
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## ✨ Key Features
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| Feature | Description |
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| --------------------------- | --------------------------------------------------------------------- |
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| 🔋 Lightweight Architecture | Optimized for low VRAM usage; ideal for small GPUs or CPU deployment. |
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| 🇹🇷 Turkish & Multilingual | Handles complex Turkish prompts accurately. |
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| 🧠 Reasoning Capabilities | Logical chain-of-thought for question-answering and problem-solving. |
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| 📊 Consistent Outputs | Reliable and reproducible results across multiple runs. |
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| 🌍 Open Source | Transparent, research-friendly, and community-driven. |
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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 | 270 Million |
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| Architecture | Transformer, causal LLM |
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| Fine-Tuning Method | Instruction fine-tuning (SFT) with Turkish and multilingual datasets |
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| Optimizations | Quantization-ready (q8, f16, f32) |
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| Use Cases | Text generation, summarization, Q&A, creative writing, reasoning tasks |
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---
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## 🚀 Installation & Usage
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### Use the model:
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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-270m"
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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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## 📄 License
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MIT License — free to use, modify, and distribute. Attribution 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-270M** — Lightweight, **efficient, and reasoning-focused**, bringing **Turkey’s AI forward** on low-resource hardware.
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[](https://huggingface.co/Lamapi) |