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Model: thelamapi/next-1b Source: Original Platform
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README.md
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README.md
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---
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language:
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- tr
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- ar
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- af
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- az
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- es
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- en
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- el
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- ro
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- ru
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- rm
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- th
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- uk
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- uz
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- pl
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- pt
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- fa
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- sk
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- sl
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- da
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- de
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- nl
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- fr
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- fi
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- ka
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- hi
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- hu
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- hy
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- ja
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- kk
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- kn
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- ko
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- ky
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- la
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- id
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- be
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- ne
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license: mit
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tags:
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- turkish
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- türkiye
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- english
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- ai
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- lamapi
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- gemma3
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- next
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- next-x1
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- efficient
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- text-generation
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- open-source
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- 1b
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- huggingface
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- large-language-model
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- llm
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- causal
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- transformer
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- artificial-intelligence
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- machine-learning
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- ai-research
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- natural-language-processing
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- nlp
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- finetuned
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- lightweight
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- creative
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- summarization
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- question-answering
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- chat-model
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- generative-ai
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- optimized-model
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- unsloth
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- trl
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- sft
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- chemistry
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- biology
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- finance
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- legal
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- music
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- art
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- code
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- climate
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- medical
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- agent
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- text-generation-inference
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pipeline_tag: text-generation
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datasets:
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- mlabonne/FineTome-100k
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- ITCL/FineTomeOs
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- Gryphe/ChatGPT-4o-Writing-Prompts
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- dongguanting/ARPO-SFT-54K
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- GreenerPastures/All-Your-Base-Full
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- Gryphe/Opus-WritingPrompts
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- HuggingFaceH4/MATH-500
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- mlabonne/smoltalk-flat
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- mlabonne/natural_reasoning-formatted
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- OpenSPG/KAG-Thinker-training-dataset
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- uclanlp/Brief-Pro
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- CognitiveKernel/CognitiveKernel-Pro-SFT
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- SuperbEmphasis/Claude-4.0-DeepSeek-R1-RP-SFWish
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- QuixiAI/dolphin-r1
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- mlabonne/lmsys-arena-human-sft-55k
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library_name: transformers
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---
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<img src='assets/banner.png'>
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# 🚀 Next-1B (t416)
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### *Lightweight, Efficient, and Türkiye-Focused AI*
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[](https://opensource.org/licenses/MIT)
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[]()
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[](https://huggingface.co/Lamapi/next-1b)
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[](https://discord.gg/XgH4EpyPD2)
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---
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## 📖 Overview
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**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**.
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Key highlights:
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* Extremely **lightweight** — can run on consumer GPUs with low VRAM.
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* Optimized for **text reasoning, summarization, and creative generation**.
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* Supports **Turkish natively** while remaining multilingual.
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* Open-source and transparent for research and applications.
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Ideal for **developers, students, and organizations** needing **fast, reliable, and low-resource text-generation**.
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---
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## 🎯 Goals
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1. **Lightweight Efficiency:** Run smoothly on low-resource devices.
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2. **Reasoning-Focused:** Provide logical and coherent text outputs.
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3. **Accessibility:** Fully open-source with clear documentation.
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4. **Multilingual Adaptability:** Turkish-focused but supports other languages.
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---
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## ✨ Key Features
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| Feature | Description |
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| --------------------------- | --------------------------------------------------------------------- |
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| 🔋 Lightweight Architecture | Optimized for low VRAM usage; ideal for small GPUs or CPU deployment. |
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| 🇹🇷 Turkish & Multilingual | Handles complex Turkish prompts accurately. |
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| 🧠 Reasoning Capabilities | Logical chain-of-thought for question-answering and problem-solving. |
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| 📊 Consistent Outputs | Reliable and reproducible results across multiple runs. |
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| 🌍 Open Source | Transparent, research-friendly, and community-driven. |
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---
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## 📐 Model Specifications
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| Specification | Details |
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| ------------------ | ---------------------------------------------------------------------- |
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| Base Model | Gemma 3 |
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| Parameter Count | 1 Billion |
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| Architecture | Transformer, causal LLM |
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| Fine-Tuning Method | Instruction fine-tuning (SFT) with Turkish and multilingual datasets |
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| Optimizations | Quantization-ready (q8, f16, f32) |
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| Use Cases | Text generation, summarization, Q&A, creative writing, reasoning tasks |
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---
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## 🚀 Installation & Usage
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### Use the model:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "Lamapi/next-1b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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# Chat message
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messages = [
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{"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."},
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{"role": "user", "content": "Hello, how are you?"}
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]
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# Prepare input with Tokenizer
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt")
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# Output from the model
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output = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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<div style='width:700px;'>
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<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;'>
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Hello, how are you?
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</div>
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<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;'>
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I'm fine, thank you. How are you?
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</div>
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</div>
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---
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## 📄 License
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MIT License — free to use, modify, and distribute. Attribution appreciated.
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---
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## 📞 Contact & Support
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* 📧 **Email:** [lamapicontact@gmail.com](mailto:lamapicontact@gmail.com)
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* 🤗 **HuggingFace:** [Lamapi](https://huggingface.co/Lamapi)
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---
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> **Next-1B** — Lightweight, **efficient, and reasoning-focused**, bringing **Turkey’s AI forward** on low-resource hardware.
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[](https://huggingface.co/Lamapi)
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