language, license, tags, pipeline_tag, datasets, library_name
| language | license | tags | pipeline_tag | datasets | library_name | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| tr | mit |
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text-generation |
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transformers |
🚀 Next-270M (xt330)
Lightweight, Efficient, and Türkiye-Focused AI
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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.
| Model | MMLU (5-shot) % | MMLU-Pro % | GSM8K % | MATH % |
|---|---|---|---|---|
| Next 4B preview Version s325 | 84.6 | 66.9 | 82.7 | 70.5 |
| Next 1B Version t327 | 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 |
| Kumru 7B not verified | 30.7 | 28.6 | 15.38 | 6.4 |
Also, our Next Z1 model is leading to state-of-the-art models in some of the Benchmarks.
| Model | MMLU (5-shot) % | MMLU-Pro % | GSM8K % | MATH % |
|---|---|---|---|---|
| Next Z1 Version l294 | 97.3 | 94.2 | 97.7 | 93.2 |
| Next Z1 Version l294 (no tool) | 94.7 | 90.1 | 94.5 | 88.7 |
| GPT 5 | 92.5 | 87.0 | 98.4 | 96.0 |
| Claude Opus 4.1 (Thinking) | ~92.0 | 87.8 | 84.7 | 95.4 |
🎯 Goals
- Lightweight Efficiency: Run smoothly on low-resource devices.
- Reasoning-Focused: Provide logical and coherent text outputs.
- Accessibility: Fully open-source with clear documentation.
- 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 | 270 Million |
| 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:
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))
Hello, how are you?
I'm fine, thank you. How are you?
📄 License
MIT License — free to use, modify, and distribute. Attribution appreciated.
📞 Contact & Support
- 📧 Email: lamapicontact@gmail.com
- 🤗 HuggingFace: Lamapi
Next-270M — Lightweight, efficient, and reasoning-focused, bringing Turkey’s AI forward on low-resource hardware.
