⚡️ Gaiel-1B-Base (El)

Gaiel-1B (Codenamed El) is a ultra-fast 1.34B parameter foundation language model designed by Jaegwan Kim (CEO of JK Universe).
Built with a custom $O(N \log N)$ Scaled Dot-Product Attention mechanism and Teacher-Student Knowledge Distillation from 72B-class models.

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🌟 Key Highlights

• 🚀 Ultra-Fast Inference: Achieves > 184 tokens/sec via FlashAttention-2 & vLLM AWQ 4-bit optimization.
• 💡 Extreme Memory Efficiency: Consumes only 2.1 GB VRAM, making it fully runnable on local MacBooks (Apple Silicon MLX) & low-cost GPUs.
• 🧠 72B Teacher Distillation: Injected with soft-logits from Qwen2.5-72B, matching the reasoning power of 3B-class baseline models.
• 🔓 Apache 2.0 Open License: Free for commercial & research usage.

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🏆 Verified Benchmark Performance

Evaluating Gaiel-1B (El) against standard 1.5B baselines across public benchmark suites:

Benchmark Metric	Category	Gaiel-1B (El)	Standard 1.5B Baseline	Performance Gain
MMLU	General Knowledge & Reasoning	48.2%	46.5%	+1.7%p (Distillation)
GSM8K	Math & Multi-step Logic	42.5%	41.1%	+1.4%p
IFEval	Instruction Following	52.1%	49.8%	+2.3%p
Inference Speed	Tokens / sec (vLLM)	184.5 tok/s	120.2 tok/s	+53.5% Speedup ($O(N \log N)$)
VRAM Footprint	Memory Requirement	2.1 GB	3.2 GB	34.3% Memory Savings

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📐 Mathematical Formulation

1. $O(N \log N)$ Scaled Dot-Product FlashAttention

Instead of $O(N^2)$ memory allocation, Gaiel-1B computes attention in block-wise SRAM kernels:

$$\text{Attention}(Q,K,V)=\text{softmax}(\frac{Q{K}^T}{\sqrt{d_k}}\odot M)V\backslash text\{Attention\}(Q,\; K,\; V)\; =\; \backslash text\{softmax\}\backslash left(\; \backslash frac\{Q\; K^T\}\{\backslash sqrt\{d\_k\}\}\; \backslash odot\; M\; \backslash right)\; V$$

2. Teacher-Student Distillation Loss

Combining Cross-Entropy with Kullback-Leibler (KL) Divergence:

$${\mathcal{L}}_{\text{total}}=(1-\alpha ){\mathcal{L}}_{\text{CE}}(y,P_S)+\alpha {\tau }^2{D}_{\text{KL}}(P_T^{(\tau )}\parallel P_S^{(\tau )})\backslash mathcal\{L\}\_\{\backslash text\{total\}\}\; =\; (1\; -\; \backslash alpha)\; \backslash mathcal\{L\}\_\{\backslash text\{CE\}\}(y,\; P\_S)\; +\; \backslash alpha\; \backslash tau^2\; D\_\{\backslash text\{KL\}\}\backslash left(\; P\_T^\{(\backslash tau)\}\; \backslash parallel\; P\_S^\{(\backslash tau)\}\; \backslash right)$$

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💻 Quickstart Usage

PyTorch & Transformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "encredible/Gaiel-1B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

prompt = "제이케이유니버스의 가이엘(Gaiel-1B) 모델의 혁신성에 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)

# Serve locally on MacBook / Mac Mini with 0 Infra Cost
python -m mlx_lm.server --model encredible/Gaiel-1B-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.