OLMo-3-7B Dolci-Translated A-75EN
Continued-SFT of openeurollm/OLMo-3-7B-Instruct-SFT on a $75/25$ English:EU
mixture, the headline configuration of the paper Translate, Replay, Mix:
Exploring Multilingual Post-Training for Low-Resource European Languages.
- Qualitative completions viewer: https://ferreirafabio.github.io/olmo3-multilingual-dolci-sft-progression/
Recipe
| Base checkpoint | openeurollm/OLMo-3-7B-Instruct-SFT (our reproduction at parity with allenai/OLMo-3-7B-Instruct-SFT) |
| English half (Dolci replay) | allenai/Dolci-Instruct-SFT, 75% of the mixture |
| EU half (Dolci-Translated) | openeurollm/Dolci-Instruct-SFT-translated, 25% of the mixture, 7 EU languages translated with gemma-3-27b-it |
| EU languages | cs, de, es, fi, fr, it, sv |
| Total samples | 2.87M (2,152,112 en + 717,370 EU) |
| Final step | 3998 |
| Chat template | olmo (inherited from base) |
Training configuration
- Optimiser: AdamW, $\beta_1=0.9$, $\beta_2=0.95$
- Peak learning rate: $8\times10^{-5}$, linear warm-up + cosine decay
- Effective batch size: ${\sim}1$M tokens per step
- Sequence length: 32,768
- Precision: BF16
- DeepSpeed ZeRO stage 2
- Hardware: 8 × NVIDIA H200 SXM (HoreKa), $2\times4$ topology
- Training framework: OLMo-core via the
open-instructfork
Evaluation
Bradley-Terry Elo (Qwen3.5-27B judge, LMArena BT implementation, 500 battles/language, 100 bootstrap resamples):
| Metric | A-75EN (this checkpoint) | Baseline (openeurollm/OLMo-3-7B-Instruct-SFT) |
|---|---|---|
| Overall Elo | $789 \pm 7$ | $762 \pm 7$ |
| English Elo | $\mathbf{950 \pm 14}$ | $954 \pm 16$ |
| Non-English Elo | $\mathbf{755 \pm 8}$ | $697 \pm 9$ |
Per-language Elo (cs / de / es / fi / fr / it / sv):
| en | cs | de | es | fi | fr | it | sv |
|---|---|---|---|---|---|---|---|
| $950 \pm 14$ | $714 \pm 19$ | $690 \pm 24$ | $746 \pm 18$ | $732 \pm 44$ | $743 \pm 17$ | $\mathbf{820 \pm 15}$ | $722 \pm 35$ |
A-75EN preserves English Elo within CI of baseline and improves on every EU language except Swedish, with the largest gain on Italian. Full per-language breakdown and the comparison to A-25EN are in the paper, Tables 2 and 3.
Intermediate checkpoints
Training-step revisions (step500, step1500, step2500, step3500) are
available as HF git revisions of this repo (loaded via revision="step1500")
and back the qualitative completions viewer at https://ferreirafabio.github.io/olmo3-multilingual-dolci-sft-progression/.
How to load
from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-75EN")
model = AutoModelForCausalLM.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-75EN", torch_dtype="bfloat16")
# tok.chat_template is set; use tok.apply_chat_template(...) directly
Citation
Please cite the paper and the OLMo-3 family if you use this checkpoint.