Next-1B (t416)
Lightweight, Efficient, and Türkiye-Focused AI
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.
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 |
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:
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))
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
Next-1B — Lightweight, efficient, and reasoning-focused, bringing Turkey’s AI forward on low-resource hardware.
