---
license: mit
language:
- en
- nl
- zh
library_name: transformers
pipeline_tag: text-generation
tags:
- babylm
- babylm-2026
- multilingual
- llama
- pretrained-from-scratch
---

# BabyLM 2026 — MultiLingual track baseline (byte-premium-uniform)

A 110M-param Llama-style decoder pre-trained from scratch on the BabyBabelLM
trilingual corpus (English, Dutch, Chinese), under the BabyLM 2026
MultiLingual track rules: **100M reference tokens, byte-premium adjusted**,
≤10 epochs.

This is the *baseline* zero-point of our ablation grid. Subsequent runs vary
the mixture allocation (loss-weighted, simultaneous-bilingual, typological-bridge
curriculum, register-controlled) on top of an identical scaffold. The matching
ablation paper is in preparation.

## Architecture

- Llama (HF `LlamaForCausalLM`) — RoPE, RMSNorm, SwiGLU, no biases, tied embeddings
- 12 layers · 768 hidden · 12 heads · 2048 FFN
- 1024 sequence length
- 110,119,680 parameters

## Tokenizer

Joint byte-level BPE, 32,768 vocab, trained on a balanced 50M-char sample from
each of EN/NL/ZH. The same tokenizer is shared across all three languages (see
the data card for why a joint tokenizer is required: ZH is 6.8% Latin script).

## Training

- **Data:** `BabyLM-community/babylm-eng` + `babylm-nld` + `babylm-zho`
  (BabyBabelLM 2026 100M tier). Full corpora loaded in memory and shuffled
  (the Hub layout is category-clustered; streaming with reasonable buffers
  produces a biased sample).
- **Mixture:** byte-premium-uniform — equal share of *reference tokens* per
  language (1/3 each), achieved by deficit-driven selection, not uniform doc
  sampling (mean doc sizes differ across languages).
- **Optimizer:** AdamW (β₁=0.9, β₂=0.95, wd=0.1), lr 6e-4, cosine to 10%, 100-step warmup
- **Compute:** 4× NVIDIA A10G (23 GB), bf16, DDP, micro-batch 16 × grad-accum 2 (eff. batch 128 sequences = 131k tokens/step)
- **Tokens consumed at this checkpoint:** 100,000,000 byte-premium-adjusted reference tokens
- **Per-language epochs at this checkpoint:** ≈1.0 each (within the BabyLM ≤10-epoch cap)

## Revisions

The `chck_{N}M` revisions match the BabyLM eval pipeline's fast-eval naming:

```
chck_1M, chck_2M, ..., chck_9M, chck_10M, chck_20M, ..., chck_90M, chck_100M
```

Use `revision=chck_NM` to load any milestone. The default (`main`) is `chck_100M`.

## How to evaluate

```bash
git clone https://github.com/babylm-org/babylm-eval
cd babylm-eval/multilingual
bash scripts/zeroshot_model.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M
bash scripts/zeroshot_model_fast_all.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M
```

## Citation

```
@misc{babylm-2026-uniform,
  title  = {BabyLM 2026 MultiLingual baseline (byte-premium-uniform)},
  author = {Hossain, Shamima},
  year   = {2026},
  url    = {https://huggingface.co/Shamima/babylm-2026-multilingual-uniform-100M}
}
```

Companion repo with audit, scaffold, and ablation configs:
https://github.com/silvererudite/bb-lm-challenge-sub