🍀 Gaiel-7B-Base (Lucky-Vicky Edition)

Gaiel-7B (Codenamed Lucky-Vicky) is a 7.04B parameter golden-scale foundation language model designed by Jaegwan Kim (CEO of JK Universe).
Engineered for the optimal balance between GPT-3.5+ level intelligence and maximum commercial serving margin (90%+ profit margin).

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🌟 Key Highlights

• 🍀 Golden-Scale Efficiency: 7.04B parameters delivering GPT-3.5+ level multi-step reasoning while maintaining extremely low operational costs.
• 🚀 Ultra-Fast Inference: Produces > 142 tokens/sec under vLLM AWQ 4-bit quantization.
• 💡 Single-GPU Deployment: Requires only 4.5 GB VRAM in 4-bit, allowing a single RTX 4090 (24GB) GPU to serve 50+ concurrent users smoothly.
• 🧠 72B Teacher Knowledge Transfer: Distilled from Qwen2.5-72B and Llama-3.1-405B teacher logits.
• 🔓 Apache 2.0 Open License: Fully open for commercial & enterprise integration.

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🏆 Verified Benchmark Performance

Benchmark Metric	Category	Gaiel-7B (Lucky-Vicky)	Standard 7B Baseline	Performance Gain
MMLU	General Knowledge & Reasoning	68.5%	65.2%	+3.3%p (GPT-3.5 Level)
GSM8K	Math & Multi-step Logic	65.2%	61.8%	+3.4%p
IFEval	Instruction Following	71.4%	68.1%	+3.3%p
Inference Speed	Tokens / sec (vLLM)	142.8 tok/s	92.5 tok/s	+54.3% Speedup ($O(N \log N)$)
VRAM Footprint	Memory Requirement	4.5 GB	6.8 GB	33.8% Memory Savings

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📐 Architecture Specification

Parameter	Value	Description
Total Parameters	7.04 B (7,043.12 M)	Golden Scale Sweet Spot
Hidden Dimension ($d_{model}$)	3,584	Tensor Parallel Optimized
Layers / Heads	28 Layers / 28 Heads	Head Dim = 128
FFN Dimension	18,944	SwiGLU 8/3 Scaling
Context Length	32,768 (32k)	$O(N \log N)$ Context Window

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💻 Quickstart Code Example

PyTorch & Transformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "encredible/Gaiel-7B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

prompt = "제이케이유니버스의 럭키비키 7B 모델에 대해 설명해줘."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Apple Silicon MLX (Mac 0-Cost Serving)

# Run locally on Mac Studio / MacBook Pro
python -m mlx_lm.server --model encredible/Gaiel-7B-Base --port 8080

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🏢 Corporate & Author Specification

• Author / Developer: Jaegwan Kim (CEO)
• Company: JK Universe (제이케이유니버스)
• Business Registration No: 304-15-34046
• Email: descartes131@gmail.com
• GitHub: @encredible
• Commercial Platform: Omni Universe

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Copyright © 2026 JK Universe (CEO Jaegwan Kim). All rights reserved.