Instructions to use drooryck/multilingual-macaroni-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drooryck/multilingual-macaroni-models with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="drooryck/multilingual-macaroni-models")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("drooryck/multilingual-macaroni-models", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use drooryck/multilingual-macaroni-models with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drooryck/multilingual-macaroni-models"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drooryck/multilingual-macaroni-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/drooryck/multilingual-macaroni-models

SGLang

How to use drooryck/multilingual-macaroni-models with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "drooryck/multilingual-macaroni-models" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drooryck/multilingual-macaroni-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "drooryck/multilingual-macaroni-models" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drooryck/multilingual-macaroni-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use drooryck/multilingual-macaroni-models with Docker Model Runner:
```
docker model run hf.co/drooryck/multilingual-macaroni-models
```

multilingual-macaroni-models

This repository contains the models from the paper Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergence. Code and training scripts are available at GitHub.

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

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.

Downloads last month: -; Downloads are not tracked for this model. How to track

Paper for drooryck/multilingual-macaroni-models

Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergence

Paper • 2609.30535 • Published 5 days ago