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thelamapi/next-270m

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🚀 Next-270M (xt330)

Lightweight, Efficient, and Türkiye-Focused AI

![License: MIT](https://opensource.org/licenses/MIT) ![Language: English]() ![HuggingFace](https://huggingface.co/Lamapi/next-270m) ![Discord](https://discord.gg/XgH4EpyPD2)

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📖 Overview

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.

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 <em>Version s325</em></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> <tr class="turkish"> <td data-label="Model">Kumru 7B <em>not verified</em></td> <td data-label="MMLU (5-shot) %">30.7</td> <td data-label="MMLU-Pro %">28.6</td> <td data-label="GSM8K %">15.38</td> <td data-label="MATH %">6.4</td> </tr> </tbody> </table>

Also, our Next Z1 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 data-label="Model">Next Z1 <em>Version l294</em></td> <td data-label="MMLU (5-shot) %"><strong>97.3</strong></td> <td data-label="MMLU-Pro %"><strong>94.2</strong></td> <td data-label="GSM8K %">97.7</td> <td data-label="MATH %">93.2</td> </tr> <tr class="next"> <td data-label="Model">Next Z1 <em>Version l294</em> (no tool)</td> <td data-label="MMLU (5-shot) %">94.7</td> <td data-label="MMLU-Pro %">90.1</td> <td data-label="GSM8K %">94.5</td> <td data-label="MATH %">88.7</td> </tr> <tr> <td data-label="Model">GPT 5</td> <td data-label="MMLU (5-shot) %">92.5</td> <td data-label="MMLU-Pro %">87.0</td> <td data-label="GSM8K %"><strong>98.4</strong></td> <td data-label="MATH %"><strong>96.0</strong></td> </tr> <tr> <td data-label="Model">Claude Opus 4.1 (Thinking)</td> <td data-label="MMLU (5-shot) %">~92.0</td> <td data-label="MMLU-Pro %">87.8</td> <td data-label="GSM8K %">84.7</td> <td data-label="MATH %">95.4</td> </tr> </tbody> </table>

🎯 Goals

  1. 1.Lightweight Efficiency: Run smoothly on low-resource devices.
  2. 2.Reasoning-Focused: Provide logical and coherent text outputs.
  3. 3.Accessibility: Fully open-source with clear documentation.
  4. 4.Multilingual Adaptability: Turkish-focused but supports other languages.

✨ Key Features

FeatureDescription
🔋 Lightweight ArchitectureOptimized for low VRAM usage; ideal for small GPUs or CPU deployment.
🇹🇷 Turkish & MultilingualHandles complex Turkish prompts accurately.
🧠 Reasoning CapabilitiesLogical chain-of-thought for question-answering and problem-solving.
📊 Consistent OutputsReliable and reproducible results across multiple runs.
🌍 Open SourceTransparent, research-friendly, and community-driven.

📐 Model Specifications

SpecificationDetails
Base ModelGemma 3
Parameter Count270 Million
ArchitectureTransformer, causal LLM
Fine-Tuning MethodInstruction fine-tuning (SFT) with Turkish and multilingual datasets
OptimizationsQuantization-ready (q8, f16, f32)
Use CasesText 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-270m"
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

Next-270M — Lightweight, efficient, and reasoning-focused, bringing Turkey’s AI forward on low-resource hardware.

![Follow on HuggingFace](https://huggingface.co/Lamapi)