OLMo-3-7B Dolci-Translated A-25EN (matched compute)
Continued-SFT of openeurollm/OLMo-3-7B-Instruct-SFT on a $25/75$ English:EU
mixture, the EU-leaning configuration of the paper Translate, Replay, Mix:
Exploring Multilingual Post-Training for Low-Resource European Languages. This
is the matched-compute variant: both A-75EN and A-25EN consume the same
$2.87$M samples × $2$ epochs at the same $1$M-token batch size; A-25EN takes
5398 steps to A-75EN's 3998 because the gemma-translated EU text packs more
tokens per sample.
- Qualitative completions viewer (this checkpoint's progression is on the right): 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, 25% of the mixture |
| EU half (Dolci-Translated) | openeurollm/Dolci-Instruct-SFT-translated, 75% of the mixture, 7 EU languages translated with gemma-3-27b-it |
| EU languages | cs, de, es, fi, fr, it, sv |
| Total samples | 4.62M (1,155,000 en + 3,465,000 EU) |
| Final step | 5398 (matched-compute variant) |
| Chat template | olmo (inherited from base) |
Training configuration
Identical to openeurollm/OLMo-3-7B-Dolci-Translated-A-75EN: AdamW, peak LR
$8\times10^{-5}$, batch ${\sim}1$M tokens, seq_len 32k, BF16, DeepSpeed ZeRO 2,
$8\times$H200 SXM. Only the English:EU ratio differs.
Evaluation
Bradley-Terry Elo (Qwen3.5-27B judge, 500 battles/language, 100 bootstraps):
| Metric | A-25EN (this checkpoint) | Baseline (openeurollm/OLMo-3-7B-Instruct-SFT) |
|---|---|---|
| Overall Elo | $782 \pm 6$ | $762 \pm 7$ |
| English Elo | $913 \pm 16$ | $954 \pm 16$ |
| Non-English Elo | $\mathbf{742 \pm 8}$ | $697 \pm 9$ |
Per-language Elo (cs / de / es / fi / fr / it / sv):
| en | cs | de | es | fi | fr | it | sv |
|---|---|---|---|---|---|---|---|
| $913 \pm 16$ | $\mathbf{745 \pm 15}$ | $\mathbf{701 \pm 23}$ | $\mathbf{763 \pm 18}$ | $\mathbf{813 \pm 33}$ | $\mathbf{756 \pm 17}$ | $801 \pm 15$ | $756 \pm 33$ |
A-25EN dominates the EU columns (best on cs, de, es, fi, fr) at the cost of $41$ English Elo points. Pick this checkpoint when the deployment is EU-facing and a moderate English regression is acceptable.
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-25EN")
model = AutoModelForCausalLM.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN", torch_dtype="bfloat16")
Citation
Please cite the paper and the OLMo-3 family if you use this checkpoint.