📖 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.

Also, our Next 14b model is leading to state-of-the-art models in some of the Benchmarks.

🎯 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

📐 Model Specifications

🚀 Installation & Usage

Use the model:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Lamapi/next-1b"
tokenizer = AutoTokenizer.frompretrained(modelid)
model = AutoModelForCausalLM.frompretrained(modelid)

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.applychattemplate(messages, tokenize=False, addgenerationprompt=True)
inputs = tokenizer(prompt, return_tensors="pt")

Output from the model
output = model.generate(inputs, maxnewtokens=50)
print(tokenizer.decode(output[0], skipspecialtokens=True))

📄 License

MIT License — free to use, modify, and distribute. Attribution appreciated.

📞 Contact & Support