Instructions to use Shamima/babylm-2026-multilingual-uniform-100M-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shamima/babylm-2026-multilingual-uniform-100M-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Shamima/babylm-2026-multilingual-uniform-100M-v2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shamima/babylm-2026-multilingual-uniform-100M-v2")
model = AutoModelForCausalLM.from_pretrained("Shamima/babylm-2026-multilingual-uniform-100M-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Shamima/babylm-2026-multilingual-uniform-100M-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shamima/babylm-2026-multilingual-uniform-100M-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shamima/babylm-2026-multilingual-uniform-100M-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Shamima/babylm-2026-multilingual-uniform-100M-v2

SGLang

How to use Shamima/babylm-2026-multilingual-uniform-100M-v2 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 "Shamima/babylm-2026-multilingual-uniform-100M-v2" \
    --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": "Shamima/babylm-2026-multilingual-uniform-100M-v2",
		"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 "Shamima/babylm-2026-multilingual-uniform-100M-v2" \
        --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": "Shamima/babylm-2026-multilingual-uniform-100M-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Shamima/babylm-2026-multilingual-uniform-100M-v2 with Docker Model Runner:
```
docker model run hf.co/Shamima/babylm-2026-multilingual-uniform-100M-v2
```

BabyLM 2026 — MultiLingual track baseline v2 (byte-premium-uniform, WSD)

Iteration v2 of the byte-premium-uniform trilingual baseline. Same architecture, same tokenizer, same mixture as v1 (Shamima/babylm-2026-multilingual-uniform-100M), but trained with a Warmup-Stable-Decay schedule rather than cosine, on the same 100M ref-token budget at the same per-step compute. The motivation: v1's cosine schedule decayed to 10% of peak by ~90% through training while loss was still falling; WSD holds at peak and only decays in the last 25%, which lets more of the budget land at productive learning rates.

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 (same as v1; reused so the two are directly comparable)

Training

Data: BabyBabelLM 2026 100M tier (EN/NL/ZH); full corpora loaded in memory and shuffled
Mixture: byte-premium-uniform via deficit-driven selection (1/3 of reference tokens per language)
Optimiser: AdamW (β1=0.9, β2=0.95, wd=0.1)
LR: 6e-4 peak, WSD schedule (warmup 200 → constant peak → linear 25% decay tail to 6e-5)
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: 100,016,896 byte-premium-adjusted reference tokens (= 1 epoch over the corpus)
Per-language epochs at this checkpoint: ~1.0 each (well within the BabyLM ≤10-epoch cap)

Revisions

19 fast-eval branches: chck_1M, chck_2M, …, chck_9M, chck_10M, chck_20M, …, chck_90M, chck_100M. main is chck_100M.

How to evaluate

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-v2
bash scripts/zeroshot_model_fast_all.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M-v2

Comparison vs v1

See https://github.com/silvererudite/bb-lm-challenge-sub for the iteration log, scaffold, and ablation configs.

Citation

@misc{babylm-2026-uniform-v2,
  title  = {BabyLM 2026 MultiLingual baseline v2 (WSD schedule)},
  author = {Hossain, Shamima},
  year   = {2026},
  url    = {https://huggingface.co/Shamima/babylm-2026-multilingual-uniform-100M-v2}
}

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Safetensors

Model size

0.1B params

Tensor type

F32