BabyLM 2026 — Multilingual GPT-2 (MorPiece-16K)
Track: BabyLM 2026 Multilingual · Architecture: GPT-2 · Tokenizer: MorPiece 16K (multilingual)
A single, shared-weight GPT-2 language model trained on a balanced trilingual corpus of English, Dutch, and Chinese (100 M byte-premium-adjusted words) for the
BabyLM 2026 Challenge multilingual track. The model never receives an explicit language identifier: language identity is implicit in the shared multilingual vocabulary and the model's learned representations.
Model Details
Architecture
| Hyperparameter | Value |
|---|
| Architecture | GPT-2 (GPT2LMHeadModel) |
Hidden size (n_embd) | 768 |
Layers (n_layer) | 12 |
Attention heads (n_head) | 12 |
Context length (seq_length) | 512 |
| Dropout | 0.1 |
| Tied embeddings | ✓ |
| Parameters | ~117 M |
Tokenizer
The model uses MorPiece (v1.4+), a morphologically-aware split-based tokenizer that in this model adopts the --boundary-discovery option (to deal with ZHO); the
training starts re-ordering sentences by length, only relying on strong punctuation and line endings. Each split is based on Yang's Sufficiency Principle. The vocabulary (MoP_16K_multilingual) contains 16 000 tokens jointly trained on all three languages.
- Repository: cristianochesi/morpiece
- Vocabulary size: 16 000
- Special tokens:
<s> (BOS, id=1), </s> (EOS, id=2), <unk> (id=?), <pad> (id=3), <mask>
Training Data
A curated, cleaned multilingual corpus of English (eng), Dutch (nld), and Chinese (zho), totalling approximately 100 M byte-premium-adjusted (English-equivalent) words. Languages are sampled with weights proportional to their byte premiums (BP: eng=1.000, nld=1.052, zho=0.936) to balance information-content exposure across languages.
| Language | Byte Premium | Sampling weight |
|---|
| English (eng) | 1.000 | 1.000 |
| Dutch (nld) | 1.052 | 1.052 |
| Chinese (zho) | 0.936 | 0.936 |
The byte-premium adjustment follows Arnett, Chang & Bergen (SIGUL 2024): English-equivalent content = raw UTF-8 bytes ÷ byte premium, ensuring that the budget milestones (checkpoint_<N>M_words) correspond to the BabyLM multilingual track's denomination.
Training Procedure
| Hyperparameter | Value |
|---|
| Regimen | baseline (non-overlapping windows) |
| Batch size | 16 sequences |
| Gradient accumulation steps | 4 (effective batch = 64 seq × 512 tok = 32 768 tokens/step) |
| Peak learning rate | 3 × 10⁻⁴ |
| Minimum learning rate | 3 × 10⁻⁵ |
| LR schedule | Cosine decay with linear warmup |
| Warmup | 1% of total optimizer steps |
| Weight decay | 0.1 |
| β₁ / β₂ | 0.9 / 0.999 |
| Gradient clipping | 1.0 |
| AMP precision | bfloat16 |
| Epochs | 10 passes over each language corpus |
| Budget milestones | BabyLM schedule up to 1 000 M words |
| Optimizer | AdamW (fused when available) |
Intermediate checkpoints are saved at BabyLM standard word-budget milestones (1, 2, 3 … 10, 20, 30 … 100, 200 … 1 000 M English-equivalent words) as checkpoint_<N>M_words/ directories, each loadable directly with AutoModelForCausalLM.from_pretrained.
Hardware & Software
- Framework: PyTorch 2.9.1 + HuggingFace Transformers
- CUDA 12.8, conda environment
env_py3_12_torch2_91_CUDA_12_8
- Trainer:
train_multilingual.py (cristianochesi/babylm-2026)
Usage
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3tokenizer = AutoTokenizer.from_pretrained("NeTS-IUSSPavia/babylm2026-ml-gpt2-mop16k")
4model = AutoModelForCausalLM.from_pretrained("NeTS-IUSSPavia/babylm2026-ml-gpt2-mop16k")
5
6# English
7prompt = "The child looked at"
8inputs = tokenizer(prompt, return_tensors="pt")
9output = model.generate(**inputs, max_new_tokens=30)
10print(tokenizer.decode(output[0]))
11
12# Dutch
13prompt_nl = "Het kind keek naar"
14inputs = tokenizer(prompt_nl, return_tensors="pt")
15output = model.generate(**inputs, max_new_tokens=30)
16print(tokenizer.decode(output[0]))
17
18# Chinese
19prompt_zh = "孩子看着"
20inputs = tokenizer(prompt_zh, return_tensors="pt")
21output = model.generate(**inputs, max_new_tokens=30)
22print(tokenizer.decode(output[0]))
No language identifier is needed. The model infers the language from the input sequence.
Evaluation
This model is evaluated under the BabyLM 2026 multilingual track pipeline. Standard evaluation tasks include:
- BLiMP / BLiMP-NL / BLiMP-ZH — syntactic minimal-pair acceptability
- (Super)GLUE / GLUE-NL — downstream NLU benchmarks
- Perplexity on held-out multilingual test sets
Results will be updated here upon completion of the shared task evaluation.
Limitations
- The model is trained on a small, child-scale corpus (≤100 M words per language) and is not intended for production NLP applications.
- Performance on low-frequency phenomena will be limited relative to large-scale LMs.
- No explicit language control is available; mixing languages within a single prompt may produce unpredictable continuations.
- Chinese output quality may differ from English/Dutch due to the lower byte premium and the shared BPE tokenizer's segmentation behaviour for scriptio continua.
Citation
If you use this model or the associated training code, please cite:
1@misc{chesi2026babylm,
2 author = {Chesi, Cristiano and {NeTS Lab}},
3 title = {{BabyLM 2026 Multilingual GPT-2 (MorPiece-16K)}},
4 year = {2026},
5 howpublished = {\url{https://huggingface.co/NeTS-IUSSPavia/babylm2026-ml-gpt2-mop16k}},
6 note = {Submission to the BabyLM 2026 Challenge, Multilingual Track. IUSS Pavia -- NeTS Lab.}
7}
Please also cite the MorPiece tokenizer and the BabyLM shared task:
1@misc{chesi2024morpiece,
2 author = {Chesi, Cristiano and {NeTS Lab @ IUSS}},
3 title = {{MorPiece: A Morphologically-Aware Tokenizer Based on Yang's Tolerance Principle}},
4 year = {2024},
5 howpublished = {\url{https://github.com/cristianochesi/morpiece}}
6}
Model Card Contact
Cristiano Chesi — NeTS Lab, IUSS Pavia
nets.iusspavia.it
