GPT-2 Minangkabau WikiMulti
GPT-2-small model pretrained from scratch on the Minangkabau portion of WikiMulti.
Dataset
- Dataset:
dehanalkautsar/WikiMulti - File:
20260801_mi_wiki.parquet - Training field:
text
Architecture
Standard GPT-2-small architecture:
- Layers: 12
- Hidden size: 768
- Attention heads: 12
- Maximum context: 1024
- Vocabulary size: 50257
All GPT-2 weights were initialized from scratch.
No pretrained GPT-2 model weights were used.
Tokenizer
A separate Byte-Level BPE tokenizer was trained from scratch using only the Minangkabau training split.
The original English GPT-2 vocabulary and BPE merge rules were not used.
Vocabulary size: 50257
Training
- GPU: NVIDIA RTX A6000
- Precision: FP16
- TF32: True
- Maximum epochs: 50
- Batch size per GPU: 8
- Gradient accumulation: 4
- Sequence length: 1024
- Learning rate: 5e-05
- Scheduler: linear
- Validation frequency: every 500 optimizer steps
- Early stopping patience: 999
- Early stopping threshold: 0.0
- Random seed: 42
Training was stopped after validation loss failed to improve for 999 consecutive evaluation rounds, unless the maximum epoch ceiling was reached first.
Best Model
Best validation loss:
1.672029733657837
Final best-model validation loss:
1.672029733657837
Perplexity:
5.3229610329466235