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tang_poetry
yusiwen/tang_poetry
tang_poetry is a machine learning model from yusiwen. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
A character-level Transformer (GPT-like) trained on 全唐诗 (Complete Tang Poetry, ~57,000 poems).
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At a glance
- License
- mit
- Access
- Public
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026
- SHA
- 632eb75b
Model card
From the Hugging Face model README
唐诗生成模型 / Tang Poetry Generator
A character-level Transformer (GPT-like) trained on 全唐诗 (Complete Tang Poetry, ~57,000 poems).
Model Description
| Property | Value |
|---|---|
| Architecture | 6-layer Transformer decoder |
| Attention | 6-head MultiheadAttention |
| Embedding dim | 384 |
| Parameters | 17.5M |
| Context length | 128 characters |
| Vocab size | 8,879 (Chinese chars + punctuation) |
| Training data | ~3.9M chars from 全唐诗 (simplified Chinese) |
Quick Start
pip install torch zhconv
from inference import load_model
gen = load_model("poetry_model.pt")
print(gen("床前明月光", max_new_tokens=100))
Files
| File | Description |
|---|---|
poetry_model.pt | Model checkpoint (weights, optimizer, config, tokenizer) |
config.json | Hyperparameters |
tokenizer.json | Character vocabulary (stoi / itos) |
inference.py | Standalone inference script |
train_poetry.py | Full training script |
Training
Trained on Apple MPS (M4 MacBook Pro) with:
- Optimizer: AdamW (lr=5e-4)
- Schedule: 200-step linear warmup → cosine decay
- Gradient clipping: max_norm=1.0
- Dropout: 0.25
- Best validation loss: 4.51 (perplexity ~91)
Example Output
Seed: 大漠孤烟直
Generated:
大漠孤烟直，秋霜未落残。
后期殊灼得，前事有堆劳。
恩势已云懦，幽深不可逃。
王程未去绝，左右空踟蹰。
门径微月色，庭盘晓云初。
License
MIT
- License
- mit
- Languages
- zh
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026