Shamima

Shamima

babylm-2026-multilingual-v3-quality-filter

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more
Container

Run this model inference with full control and performance in your environment.

Learn more

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

README

License: mit

Training

  • Schedule: WSD (warmup 200 → constant 6e-4 → linear last 25% to 6e-5)
  • Total compute consumed: 500 M effective tokens (5× v2's 100 M)
  • Per-language epochs: EN 2.13, NL 4.29, ZH 3.35 — within the ≤10 cap
  • 4× NVIDIA A10G, bf16, DDP, eff. batch 131 K tokens/step
  • 23,295 steps · 8.6 hours wallclock

Revisions

main is chck_400M (the largest fast-eval checkpoint we saved). Available revisions: chck_1M, chck_2M, chck_3M, chck_4M, chck_5M, chck_6M, chck_7M, chck_8M, chck_9M, chck_10M, chck_20M, chck_30M, chck_40M, chck_50M, chck_60M, chck_70M, chck_80M, chck_90M, chck_100M, chck_200M, chck_300M, chck_400M.

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-v3-quality-filter

Companion repo (audit, scaffold, ablation configs, iteration log): https://github.com/silvererudite/bb-lm-challenge-sub

Model provider

Shamima

Shamima

Model tree

Base

this model

Modalities

Input

Text

Output

Text

Pricing

Dedicated Endpoints

View details

Supported Functionality

Model APIs

Dedicated Endpoints

Container

More information

Explore FriendliAI today