# π Next-1B (t416)
### *Lightweight, Efficient, and TΓΌrkiye-Focused AI*
[](https://opensource.org/licenses/MIT)
[]()
[](https://huggingface.co/Lamapi/next-1b)
[](https://discord.gg/XgH4EpyPD2)
---
## π 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** β 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**.
---
## π― 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.
---
## β¨ Key Features
| Feature | Description |
| --------------------------- | --------------------------------------------------------------------- |
| π Lightweight Architecture | Optimized for low VRAM usage; ideal for small GPUs or CPU deployment. |
| πΉπ· Turkish & Multilingual | Handles complex Turkish prompts accurately. |
| π§ Reasoning Capabilities | Logical chain-of-thought for question-answering and problem-solving. |
| π Consistent Outputs | Reliable and reproducible results across multiple runs. |
| π Open Source | Transparent, research-friendly, and community-driven. |
---
## π 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 |
---
## π Installation & Usage
### Use the model:
```python
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))
```