初始化项目，由ModelHub XC社区提供模型

Model: openeurollm/OLMo-3-7B-Dolci-Translated-A-75EN
Source: Original Platform

README.md

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