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emnlp-babylm-2026-v2
exusiaiw/emnlp-babylm-2026-v2
emnlp-babylm-2026-v2 is a text generation model from exusiaiw. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Submission for the BabyLM 2026 Challenge Multilingual Track (EMNLP 2026 Workshop).
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- mit
- Model type
- olmo2
- Access
- Public
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026
- SHA
- 5db121dc
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Model card
From the Hugging Face model README
EMNLP BabyLM 2026 — Multilingual OLMo2
Submission for the BabyLM 2026 Challenge Multilingual Track (EMNLP 2026 Workshop).
Model Description
A trilingual causal language model (English / Dutch / Chinese) pretrained from scratch on developmentally plausible text data, using the OLMo2 architecture.
- Architecture: Olmo2ForCausalLM
- Parameters: ~15M (512h / 8L / 8H / 1376ff)
- Tokenizer: BPE, vocab=32000
- Sequence length: 512 tokens
- Languages: English, Dutch, Chinese (equal data mix)
- Data: BabyLM 2026 multilingual corpus (100M tokens total)
Results
Multilingual Track leaderboard (as of submission):
| Model | Multilingual Avg | EN Avg | NL Avg | ZH Avg |
|---|---|---|---|---|
| This model | ~45-47 | - | - | - |
| Best baseline (gpt2-en_nld_zho_equal) | 38.86 | 40.04 | 40.02 | 36.51 |
Selected task scores:
- MultiBLiMP: EN 84.7%, NL 92.4%
- ZhoBLiMP: 78.5%
- SIB-200: EN 81.0%, NL 77.5%, ZH 77.0%
- MNLI: EN 57.8%, NL 57.3%, ZH 57.1%
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("exusiaiw/emnlp-babylm-2026")
tokenizer = AutoTokenizer.from_pretrained("exusiaiw/emnlp-babylm-2026")
Citation
BabyLM 2026 Challenge — EMNLP 2026 Workshop, Budapest
- Task
- Text Generation
- Library
- transformers
- Type
- olmo2
- License
- mit
- Languages
- en, nl, zh
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026