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yu-gi-oh-llm
ThanhNguyen111/yu-gi-oh-llm
yu-gi-oh-llm is a text generation model from ThanhNguyen111. Use it when you need the model to write or continue text. It is set up for custom. The card lists the license as mit.
A decoder-only Transformer trained from scratch in pure PyTorch to generate fictional Yu-Gi-Oh! card text.
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.pt24.7 MB · 99%
At a glance
- Task
- Text Generation
- Library
- custom
- License
- mit
- Model type
- yugioh-decoder-transformer
- Access
- Public
- Created
- Jul 9, 2026
- Updated
- Jul 10, 2026
- SHA
- 736f2e6f
Try a prompt
Model card
From the Hugging Face model README
Yu-Gi-Oh! Card Generator — Decoder-only Transformer from Scratch
A decoder-only Transformer trained from scratch in pure PyTorch to generate fictional Yu-Gi-Oh! card text.
Built as a learning project to understand LLM internals without HuggingFace Transformers. Architecture based on Attention Is All You Need (Vaswani et al., 2017)
Source code: https://github.com/conbopk/Yu-Gi-Oh-LLM
Model Architecture
| Property | Value |
|---|---|
| Architecture | Decoder-only Transformer |
| Embedding dim | 128 |
| Context length | 128 tokens |
| Attention heads | 4 |
| Feed-forward dim | 1024 |
| Decoder blocks | 3 |
| Total parameters | 2,106,624 |
| Positional encoding | Sinusoidal (fixed) |
| Activation | ReLU |
| Tokenizer | BPE (vocab size 4096) |
Training Details
| Property | Value |
|---|---|
| Dataset | hammadus/yugioh-full-card-database-index-august-1st-2025 |
| Training hardware | NVIDIA RTX 4050 6GB VRAM |
| Framework | PyTorch (CUDA cu126) |
| Best val loss | 1.2585 |
| Epochs trained | 1 |
| Total steps | 70,000 |
| Optimizer | Adam (β₁=0.9, β₂=0.98, ε=1e-9) |
| LR schedule | Warmup (2000 steps) + Inverse Square Root Decay |
| Batch size | 32 |
Sample Generated Cards
<card>
name: roar, the infer king
rarity: ultra rare
type: monster
sub_type: [spellcaster/effect]
attribute: fire
rank: level 8
attack: 2800
defense: 2800
description: cannot be normal summoned/set. must be special summoned by its
own effect. during your opponent's main phase (quick effect): you can target 1
"nekroz" monster you control; this turn, it can make a second attack during
each battle phase this turn. if this card attacks, it is changed to defense
position at the end of the battle phase.
</card>
<card>
name: gaia soul the fire kings
rarity: prismatic secret rare
type: spell
sub_type: quick-play
description: target 1 face-up monster your opponent controls (quick effect):
destroy it. you can only activate 1 "fire formation - tenki" per turn.
</card>
Usage
⚠️ This is a custom architecture. It cannot be loaded with
AutoModel.from_pretrained(). You need the source code from the GitHub repo.
Step 1: Clone the repo
git clone https://github.com/conbopk/Yu-Gi-Oh-LLM
cd Yu-Gi-Oh-LLM
uv sync
Step 2: Download the model weights
from huggingface_hub import hf_hub_download
# Download best_model.pt
model_path = hf_hub_download(
repo_id="ThanhNguyen111/yu-gi-oh-llm",
filename="best_model.pt",
local_dir="model/hf/",
)
# Download tokenizer
tokenizer_path = hf_hub_download(
repo_id="ThanhNguyen111/yu-gi-oh-llm",
filename="tokenizer-bpe.json",
local_dir="data/processed/yugioh/v001/",
)
Step 3: Update MODEL_PATH and run
# Edit scripts/inference.py: MODEL_PATH = "model/hf/best_model.pt"
uv run scripts/inference.py
# Open a booster pack
uv run scripts/open_pack.py
Files in this repo
| File | Description |
|---|---|
best_model.pt | Model weights + optimizer state + full config |
tokenizer-bpe.json | BPE tokenizer (HuggingFace tokenizers format) |
config.json | Model architecture config (human-readable) |
README.md | This file |
INFERENCE.md | Detailed inference guide |
License
MIT — free to use, modify, distribute.
- Task
- Text Generation
- Library
- custom
- Type
- yugioh-decoder-transformer
- License
- mit
- Languages
- en
- Created
- Jul 9, 2026
- Updated
- Jul 10, 2026