license, base_model, language, library_name, tags, datasets

license	base_model	language	library_name	tags	datasets
apache-2.0	openeurollm/OLMo-3-7B-Instruct-SFT	en cs de es fi fr it sv	transformers	olmo sft multilingual european-languages dolci-translated continued-sft	allenai/Dolci-Instruct-SFT openeurollm/Dolci-Instruct-SFT-translated

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-instruct fork

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.