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drooryck/multilingual-macaroni-models

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Model Card

multilingual-macaroni-models

All 24 models from our BabyLM 2026 Multilingual-track study of code-switched pretraining curricula (English / Dutch / Chinese): 8 training conditions × 3 seeds (42/43/44), each a 12-layer GPT-2 (~98M params, 16k vocab, 1024 context) trained from scratch on the 100M byte-premium-adjusted-word budget.

Every model lives on its own branch (revision). The main branch holds only this card.

Condition (branch)	Trained on	Description
`switch`	`switch` corpus	Full mixed code-switching, shuffled ordering
`noswitch`	`noswitch`	Monolingual twin
`word`	`word`	Intrasentential (word-level) CS only
`sent`	`sent`	Sentence-level CS only
`par`	`par`	Parallel CS
`salad`	`salad`	Word-salad control
`curriculum`	`curriculum`	Staged CS curriculum (intra → sentence → mono) — the leaderboard submission
`curriculum_noswitch`	`curriculum_noswitch`	Monolingual twin, staged

Seeds: branch <condition> = seed 42; <condition>-s43, <condition>-s44 = seeds 43, 44.

Training data: `drooryck/multilingual-macaroni-corpus`. The curriculum (CS) model is the same weights as the leaderboard submission `drooryck/babylm-macaroni`, which additionally carries the 28 chck_*M learning-curve revisions.

Load a model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
REPO = "drooryck/multilingual-macaroni-models"
tok   = AutoTokenizer.from_pretrained(REPO, revision="curriculum")
model = AutoModelForCausalLM.from_pretrained(REPO, revision="curriculum-s43")  # seed 43

Recipe

GPT-2 (12L, 768d, 12 heads, 1024 ctx, 16k vocab); LR 5e-5, cosine-with-min-lr, warmup 0.01, batch 16, AdamW, 10 epochs over the corpus. See our paper for full details.