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URL: https://huggingface.co/thelamapi/next-1b

⇱ thelamapi/next-1b Β· Hugging Face


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πŸš€ Next-1B (t416)

Lightweight, Efficient, and TΓΌrkiye-Focused AI

πŸ‘ License: MIT
πŸ‘ HuggingFace
πŸ‘ Discord


πŸ“– 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.


🎯 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
πŸ”‹ Lightweight Architecture Optimized for low VRAM usage; ideal for small GPUs or CPU deployment.
πŸ‡ΉπŸ‡· Turkish & Multilingual Handles complex Turkish prompts accurately.
🧠 Reasoning Capabilities Logical chain-of-thought for question-answering and problem-solving.
πŸ“Š Consistent Outputs Reliable and reproducible results across multiple runs.
🌍 Open Source Transparent, research-friendly, and community-driven.

πŸ“ 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

πŸš€ 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?

πŸ“„ License

MIT License β€” free to use, modify, and distribute. Attribution appreciated.


πŸ“ž Contact & Support


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

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