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next-1b/README.md
ModelHub XC 4b2b52fd92 初始化项目,由ModelHub XC社区提供模型
Model: thelamapi/next-1b
Source: Original Platform
2026-06-22 22:37:06 +08:00

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---
language:
- tr
- ar
- af
- az
- es
- en
- el
- ro
- ru
- rm
- th
- uk
- uz
- pl
- pt
- fa
- sk
- sl
- da
- de
- nl
- fr
- fi
- ka
- hi
- hu
- hy
- ja
- kk
- kn
- ko
- ku
- ky
- la
- lb
- id
- is
- it
- zh
- cs
- vi
- be
- bg
- bs
- ne
- mn
license: mit
tags:
- turkish
- türkiye
- english
- ai
- lamapi
- gemma3
- next
- next-x1
- efficient
- text-generation
- open-source
- 1b
- huggingface
- large-language-model
- llm
- causal
- transformer
- artificial-intelligence
- machine-learning
- ai-research
- natural-language-processing
- nlp
- finetuned
- lightweight
- creative
- summarization
- question-answering
- chat-model
- generative-ai
- optimized-model
- unsloth
- trl
- sft
- chemistry
- biology
- finance
- legal
- music
- art
- code
- climate
- medical
- agent
- text-generation-inference
pipeline_tag: text-generation
datasets:
- mlabonne/FineTome-100k
- ITCL/FineTomeOs
- Gryphe/ChatGPT-4o-Writing-Prompts
- dongguanting/ARPO-SFT-54K
- GreenerPastures/All-Your-Base-Full
- Gryphe/Opus-WritingPrompts
- HuggingFaceH4/MATH-500
- mlabonne/smoltalk-flat
- mlabonne/natural_reasoning-formatted
- OpenSPG/KAG-Thinker-training-dataset
- uclanlp/Brief-Pro
- CognitiveKernel/CognitiveKernel-Pro-SFT
- SuperbEmphasis/Claude-4.0-DeepSeek-R1-RP-SFWish
- QuixiAI/dolphin-r1
- mlabonne/lmsys-arena-human-sft-55k
library_name: transformers
---
<img src='assets/banner.png'>
# 🚀 Next-1B (t416)
### *Lightweight, Efficient, and Türkiye-Focused AI*
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Language: English](https://img.shields.io/badge/Language-Multilingual-red.svg)]()
[![HuggingFace](https://img.shields.io/badge/🤗-Lamapi/Next--1B-orange.svg)](https://huggingface.co/Lamapi/next-1b)
[![Discord](https://cdn.modrinth.com/data/cached_images/e84c69448cbf878a167f996d63e1a253437fcea2.png)](https://discord.gg/XgH4EpyPD2)
---
## 📖 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
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
| 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:
```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))
```
<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
* 📧 **Email:** [lamapicontact@gmail.com](mailto:lamapicontact@gmail.com)
* 🤗 **HuggingFace:** [Lamapi](https://huggingface.co/Lamapi)
---
> **Next-1B** — Lightweight, **efficient, and reasoning-focused**, bringing **Turkeys AI forward** on low-resource hardware.
[![Follow on HuggingFace](https://img.shields.io/badge/Follow-HuggingFace-yellow?logo=huggingface)](https://huggingface.co/Lamapi)