96 lines
3.6 KiB
Markdown
96 lines
3.6 KiB
Markdown
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---
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license: apache-2.0
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base_model: openeurollm/OLMo-3-7B-Instruct-SFT
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language:
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- en
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- cs
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- de
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- es
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- fi
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- fr
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- it
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- sv
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library_name: transformers
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tags:
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- olmo
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- sft
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- multilingual
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- european-languages
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- dolci-translated
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- continued-sft
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datasets:
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- allenai/Dolci-Instruct-SFT
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- openeurollm/Dolci-Instruct-SFT-translated
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---
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# OLMo-3-7B Dolci-Translated A-75EN
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Continued-SFT of `openeurollm/OLMo-3-7B-Instruct-SFT` on a $75/25$ English:EU
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mixture, the headline configuration of the paper *Translate, Replay, Mix:
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Exploring Multilingual Post-Training for Low-Resource European Languages*.
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- Qualitative completions viewer: https://ferreirafabio.github.io/olmo3-multilingual-dolci-sft-progression/
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## Recipe
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| | |
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|---|---|
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| **Base checkpoint** | `openeurollm/OLMo-3-7B-Instruct-SFT` (our reproduction at parity with `allenai/OLMo-3-7B-Instruct-SFT`) |
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| **English half (Dolci replay)** | `allenai/Dolci-Instruct-SFT`, 75% of the mixture |
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| **EU half (Dolci-Translated)** | `openeurollm/Dolci-Instruct-SFT-translated`, 25% of the mixture, 7 EU languages translated with `gemma-3-27b-it` |
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| **EU languages** | cs, de, es, fi, fr, it, sv |
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| **Total samples** | 2.87M (2,152,112 en + 717,370 EU) |
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| **Final step** | 3998 |
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| **Chat template** | `olmo` (inherited from base) |
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## Training configuration
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- Optimiser: AdamW, $\beta_1=0.9$, $\beta_2=0.95$
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- Peak learning rate: $8\times10^{-5}$, linear warm-up + cosine decay
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- Effective batch size: ${\sim}1$M tokens per step
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- Sequence length: 32,768
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- Precision: BF16
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- DeepSpeed ZeRO stage 2
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- Hardware: 8 × NVIDIA H200 SXM (HoreKa), $2\times4$ topology
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- Training framework: OLMo-core via the `open-instruct` fork
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## Evaluation
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Bradley-Terry Elo (Qwen3.5-27B judge, LMArena BT implementation, 500
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battles/language, 100 bootstrap resamples):
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| Metric | A-75EN (this checkpoint) | Baseline (`openeurollm/OLMo-3-7B-Instruct-SFT`) |
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|---------------------|--------------------------|-------------------------------------------------|
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| Overall Elo | $789 \pm 7$ | $762 \pm 7$ |
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| English Elo | $\mathbf{950 \pm 14}$ | $954 \pm 16$ |
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| Non-English Elo | $\mathbf{755 \pm 8}$ | $697 \pm 9$ |
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Per-language Elo (cs / de / es / fi / fr / it / sv):
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| en | cs | de | es | fi | fr | it | sv |
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|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
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| $950 \pm 14$ | $714 \pm 19$ | $690 \pm 24$ | $746 \pm 18$ | $732 \pm 44$ | $743 \pm 17$ | $\mathbf{820 \pm 15}$ | $722 \pm 35$ |
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A-75EN preserves English Elo within CI of baseline and improves on every EU
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language except Swedish, with the largest gain on Italian. Full per-language
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breakdown and the comparison to A-25EN are in the paper, Tables 2 and 3.
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## Intermediate checkpoints
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Training-step revisions (`step500`, `step1500`, `step2500`, `step3500`) are
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available as HF git revisions of this repo (loaded via `revision="step1500"`)
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and back the qualitative completions viewer at https://ferreirafabio.github.io/olmo3-multilingual-dolci-sft-progression/.
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## How to load
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tok = AutoTokenizer.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-75EN")
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model = AutoModelForCausalLM.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-75EN", torch_dtype="bfloat16")
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# tok.chat_template is set; use tok.apply_chat_template(...) directly
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```
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## Citation
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Please cite the paper and the OLMo-3 family if you use this checkpoint.
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