88 lines
3.0 KiB
Markdown
88 lines
3.0 KiB
Markdown
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---
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license: mit
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language:
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- en
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- nl
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- zh
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- babylm
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- babylm-2026
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- multilingual
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- llama
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- pretrained-from-scratch
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---
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# BabyLM 2026 — MultiLingual track baseline (byte-premium-uniform)
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A 110M-param Llama-style decoder pre-trained from scratch on the BabyBabelLM
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trilingual corpus (English, Dutch, Chinese), under the BabyLM 2026
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MultiLingual track rules: **100M reference tokens, byte-premium adjusted**,
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≤10 epochs.
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This is the *baseline* zero-point of our ablation grid. Subsequent runs vary
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the mixture allocation (loss-weighted, simultaneous-bilingual, typological-bridge
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curriculum, register-controlled) on top of an identical scaffold. The matching
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ablation paper is in preparation.
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## Architecture
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- Llama (HF `LlamaForCausalLM`) — RoPE, RMSNorm, SwiGLU, no biases, tied embeddings
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- 12 layers · 768 hidden · 12 heads · 2048 FFN
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- 1024 sequence length
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- 110,119,680 parameters
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## Tokenizer
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Joint byte-level BPE, 32,768 vocab, trained on a balanced 50M-char sample from
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each of EN/NL/ZH. The same tokenizer is shared across all three languages (see
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the data card for why a joint tokenizer is required: ZH is 6.8% Latin script).
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## Training
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- **Data:** `BabyLM-community/babylm-eng` + `babylm-nld` + `babylm-zho`
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(BabyBabelLM 2026 100M tier). Full corpora loaded in memory and shuffled
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(the Hub layout is category-clustered; streaming with reasonable buffers
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produces a biased sample).
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- **Mixture:** byte-premium-uniform — equal share of *reference tokens* per
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language (1/3 each), achieved by deficit-driven selection, not uniform doc
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sampling (mean doc sizes differ across languages).
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- **Optimizer:** AdamW (β₁=0.9, β₂=0.95, wd=0.1), lr 6e-4, cosine to 10%, 100-step warmup
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- **Compute:** 4× NVIDIA A10G (23 GB), bf16, DDP, micro-batch 16 × grad-accum 2 (eff. batch 128 sequences = 131k tokens/step)
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- **Tokens consumed at this checkpoint:** 100,000,000 byte-premium-adjusted reference tokens
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- **Per-language epochs at this checkpoint:** ≈1.0 each (within the BabyLM ≤10-epoch cap)
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## Revisions
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The `chck_{N}M` revisions match the BabyLM eval pipeline's fast-eval naming:
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```
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chck_1M, chck_2M, ..., chck_9M, chck_10M, chck_20M, ..., chck_90M, chck_100M
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```
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Use `revision=chck_NM` to load any milestone. The default (`main`) is `chck_100M`.
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## How to evaluate
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```bash
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git clone https://github.com/babylm-org/babylm-eval
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cd babylm-eval/multilingual
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bash scripts/zeroshot_model.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M
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bash scripts/zeroshot_model_fast_all.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M
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```
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## Citation
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```
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@misc{babylm-2026-uniform,
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title = {BabyLM 2026 MultiLingual baseline (byte-premium-uniform)},
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author = {Hossain, Shamima},
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year = {2026},
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url = {https://huggingface.co/Shamima/babylm-2026-multilingual-uniform-100M}
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}
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```
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Companion repo with audit, scaffold, and ablation configs:
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https://github.com/silvererudite/bb-lm-challenge-sub
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