75 lines
2.6 KiB
Markdown
75 lines
2.6 KiB
Markdown
---
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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 v2 (byte-premium-uniform, WSD)
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Iteration v2 of the byte-premium-uniform trilingual baseline. Same
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architecture, same tokenizer, same mixture as v1
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(`Shamima/babylm-2026-multilingual-uniform-100M`), but trained with a
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**Warmup-Stable-Decay** schedule rather than cosine, on the same 100M
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ref-token budget at the same per-step compute. The motivation: v1's cosine
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schedule decayed to 10% of peak by ~90% through training while loss was
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still falling; WSD holds at peak and only decays in the last 25%, which lets
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more of the budget land at productive learning rates.
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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: joint byte-level BPE 32 768 (same as v1; reused so the two are directly comparable)
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## Training
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- Data: BabyBabelLM 2026 100M tier (EN/NL/ZH); full corpora loaded in memory and shuffled
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- Mixture: byte-premium-uniform via deficit-driven selection (1/3 of *reference tokens* per language)
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- Optimiser: AdamW (β1=0.9, β2=0.95, wd=0.1)
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- LR: 6e-4 peak, **WSD schedule** (warmup 200 → constant peak → linear 25% decay tail to 6e-5)
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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: 100,016,896 byte-premium-adjusted reference tokens (= 1 epoch over the corpus)
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- Per-language epochs at this checkpoint: ~1.0 each (well within the BabyLM ≤10-epoch cap)
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## Revisions
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19 fast-eval branches: `chck_1M, chck_2M, …, chck_9M, chck_10M, chck_20M, …, chck_90M, chck_100M`.
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`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-v2
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bash scripts/zeroshot_model_fast_all.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M-v2
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```
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## Comparison vs v1
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See https://github.com/silvererudite/bb-lm-challenge-sub for the iteration
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log, scaffold, and ablation configs.
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## Citation
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```
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@misc{babylm-2026-uniform-v2,
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title = {BabyLM 2026 MultiLingual baseline v2 (WSD schedule)},
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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-v2}
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}
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```
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