language, license, tags, pipeline_tag, datasets, library_name

language	license	tags	pipeline_tag	datasets	library_name
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	mit	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	text-generation	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	transformers

🚀 Next-1B (t416)

Lightweight, Efficient, and Türkiye-Focused AI

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📖 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.

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🎯 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.

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✨ 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.

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📐 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

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🚀 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?

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📄 License

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

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📞 Contact & Support

• 📧 Email: lamapicontact@gmail.com
• 🤗 HuggingFace: Lamapi

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Next-1B — Lightweight, efficient, and reasoning-focused, bringing Turkey’s AI forward on low-resource hardware.