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--- |
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license: cc-by-nc-4.0 |
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language: |
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- ro |
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base_model: |
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- OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09 |
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datasets: |
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- OpenLLM-Ro/ro_dpo_helpsteer |
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model-index: |
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- name: OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09 |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: RoMT-Bench |
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type: RoMT-Bench |
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metrics: |
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- name: Score |
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type: Score |
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value: 5.47 |
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- task: |
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type: text-generation |
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dataset: |
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name: RoCulturaBench |
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type: RoCulturaBench |
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metrics: |
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- name: Score |
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type: Score |
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value: 3.94 |
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- task: |
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type: text-generation |
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dataset: |
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name: Romanian_Academic_Benchmarks |
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type: Romanian_Academic_Benchmarks |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 48.27 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_arc_challenge |
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type: OpenLLM-Ro/ro_arc_challenge |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 46.66 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_mmlu |
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type: OpenLLM-Ro/ro_mmlu |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 54.45 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_winogrande |
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type: OpenLLM-Ro/ro_winogrande |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 63.73 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_hellaswag |
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type: OpenLLM-Ro/ro_hellaswag |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 49.33 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_gsm8k |
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type: OpenLLM-Ro/ro_gsm8k |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 34.98 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_truthfulqa |
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type: OpenLLM-Ro/ro_truthfulqa |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 40.45 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 96.45 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass |
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type: LaRoSeDa_multiclass |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 63.23 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary_finetuned |
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type: LaRoSeDa_binary_finetuned |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass_finetuned |
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type: LaRoSeDa_multiclass_finetuned |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO |
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type: WMT_EN-RO |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 20.73 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN |
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type: WMT_RO-EN |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 7.87 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO_finetuned |
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type: WMT_EN-RO_finetuned |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN_finetuned |
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type: WMT_RO-EN_finetuned |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD |
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type: XQuAD |
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metrics: |
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- name: Average exact_match |
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type: exact_match |
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value: 19.14 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD |
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type: XQuAD |
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metrics: |
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- name: Average f1 |
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type: f1 |
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value: 38.10 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_finetuned |
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type: XQuAD_finetuned |
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metrics: |
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- name: Average exact_match |
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type: exact_match |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_finetuned |
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type: XQuAD_finetuned |
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metrics: |
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- name: Average f1 |
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type: f1 |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: Average spearman |
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type: spearman |
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value: 69.38 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: Average pearson |
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type: pearson |
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value: 69.34 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_finetuned |
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type: STS_finetuned |
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metrics: |
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- name: Average spearman |
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type: spearman |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_finetuned |
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type: STS_finetuned |
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metrics: |
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- name: Average pearson |
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type: pearson |
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value: 0.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: RoMT-Bench |
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type: RoMT-Bench |
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metrics: |
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- name: First turn |
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type: Score |
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value: 5.92 |
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- name: Second turn |
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type: Score |
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value: 5.03 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_arc_challenge |
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type: OpenLLM-Ro/ro_arc_challenge |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 48.84 |
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- name: 1-shot |
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type: accuracy |
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value: 46.27 |
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- name: 3-shot |
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type: accuracy |
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value: 44.64 |
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- name: 5-shot |
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type: accuracy |
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value: 45.76 |
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- name: 10-shot |
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type: accuracy |
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value: 46.62 |
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- name: 25-shot |
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type: accuracy |
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value: 47.81 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_mmlu |
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type: OpenLLM-Ro/ro_mmlu |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 52.47 |
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- name: 1-shot |
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type: accuracy |
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value: 54.40 |
|
- name: 3-shot |
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type: accuracy |
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value: 55.63 |
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- name: 5-shot |
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type: accuracy |
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value: 55.30 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_winogrande |
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type: OpenLLM-Ro/ro_winogrande |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 60.54 |
|
- name: 1-shot |
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type: accuracy |
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value: 63.54 |
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- name: 3-shot |
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type: accuracy |
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value: 63.46 |
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- name: 5-shot |
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type: accuracy |
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value: 67.40 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_hellaswag |
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type: OpenLLM-Ro/ro_hellaswag |
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metrics: |
|
- name: 0-shot |
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type: accuracy |
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value: 52.67 |
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- name: 1-shot |
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type: accuracy |
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value: 50.89 |
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- name: 3-shot |
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type: accuracy |
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value: 47.85 |
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- name: 5-shot |
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type: accuracy |
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value: 45.98 |
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- name: 10-shot |
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type: accuracy |
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value: 49.26 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_gsm8k |
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type: OpenLLM-Ro/ro_gsm8k |
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metrics: |
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- name: 1-shot |
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type: accuracy |
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value: 27.45 |
|
- name: 3-shot |
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type: accuracy |
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value: 36.32 |
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- name: 5-shot |
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type: accuracy |
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value: 41.17 |
|
- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
|
- name: 0-shot |
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type: macro-f1 |
|
value: 95.90 |
|
- name: 1-shot |
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type: macro-f1 |
|
value: 95.36 |
|
- name: 3-shot |
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type: macro-f1 |
|
value: 97.13 |
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- name: 5-shot |
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type: macro-f1 |
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value: 97.43 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass |
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type: LaRoSeDa_multiclass |
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metrics: |
|
- name: 0-shot |
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type: macro-f1 |
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value: 66.82 |
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- name: 1-shot |
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type: macro-f1 |
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value: 59.47 |
|
- name: 3-shot |
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type: macro-f1 |
|
value: 62.88 |
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- name: 5-shot |
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type: macro-f1 |
|
value: 63.77 |
|
- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO |
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type: WMT_EN-RO |
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metrics: |
|
- name: 0-shot |
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type: bleu |
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value: 8.00 |
|
- name: 1-shot |
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type: bleu |
|
value: 24.37 |
|
- name: 3-shot |
|
type: bleu |
|
value: 26.19 |
|
- name: 5-shot |
|
type: bleu |
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value: 24.36 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN |
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type: WMT_RO-EN |
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metrics: |
|
- name: 0-shot |
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type: bleu |
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value: 0.76 |
|
- name: 1-shot |
|
type: bleu |
|
value: 4.67 |
|
- name: 3-shot |
|
type: bleu |
|
value: 13.33 |
|
- name: 5-shot |
|
type: bleu |
|
value: 12.73 |
|
- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_EM |
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type: XQuAD_EM |
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metrics: |
|
- name: 0-shot |
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type: exact_match |
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value: 14.37 |
|
- name: 1-shot |
|
type: exact_match |
|
value: 19.08 |
|
- name: 3-shot |
|
type: exact_match |
|
value: 17.73 |
|
- name: 5-shot |
|
type: exact_match |
|
value: 25.38 |
|
- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_F1 |
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type: XQuAD_F1 |
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metrics: |
|
- name: 0-shot |
|
type: f1 |
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value: 33.52 |
|
- name: 1-shot |
|
type: f1 |
|
value: 37.27 |
|
- name: 3-shot |
|
type: f1 |
|
value: 35.77 |
|
- name: 5-shot |
|
type: f1 |
|
value: 45.84 |
|
- task: |
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type: text-generation |
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dataset: |
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name: STS_Spearman |
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type: STS_Spearman |
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metrics: |
|
- name: 1-shot |
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type: spearman |
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value: 54.50 |
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- name: 3-shot |
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type: spearman |
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value: 74.93 |
|
- name: 5-shot |
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type: spearman |
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value: 78.70 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_Pearson |
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type: STS_Pearson |
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metrics: |
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- name: 1-shot |
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type: pearson |
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value: 54.91 |
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- name: 3-shot |
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type: pearson |
|
value: 74.98 |
|
- name: 5-shot |
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type: pearson |
|
value: 78.13 |
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|
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--- |
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|
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model points/is identical to [RoGemma-7b-Instruct-DPO-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09). |
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RoGemma is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned instruct 7B model**. Links to other models can be found at the bottom of this page. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants. |
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- **Developed by:** OpenLLM-Ro |
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<!-- - **Funded by [optional]:** [More Information Needed] --> |
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<!-- - **Shared by [optional]:** [More Information Needed] --> |
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<!-- - **Model type:** [More Information Needed] --> |
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- **Language(s):** Romanian |
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- **License:** cc-by-nc-4.0 |
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- **Finetuned from model:** [RoGemma-7b-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09) |
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- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory |
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- **Paper:** https://arxiv.org/abs/2406.18266 |
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## Intended Use |
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### Intended Use Cases |
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RoGemma is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma-7b-Instruct-DPO") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma-7b-Instruct-DPO") |
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instruction = "Ce jocuri de societate pot juca cu prietenii mei?" |
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chat = [ |
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{"role": "user", "content": instruction}, |
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] |
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="") |
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inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
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outputs = model.generate(input_ids=inputs, max_new_tokens=128) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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|
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## Academic Benchmarks |
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|
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<table> |
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<tbody> |
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<tr> |
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<td><strong>Model</strong></td> |
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<td><strong><center>Average</center></strong></td> |
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<td><strong><center>ARC</center></strong></td> |
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<td><strong><center>MMLU</center></strong></td> |
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<td><strong><center>Winogrande</center></strong></td> |
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<td><strong><center>Hellaswag</center></strong></td> |
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<td><strong><center>GSM8k</center></strong></td> |
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<td><strong><center>TruthfulQA</center></strong></td> |
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</tr> |
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<tr> |
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<td>gemma-1.1-7b-it</td><td><center>41.44</center></td><td><center>40.32</center></td><td><center>47.22</center></td><td><center>55.01</center></td><td><center>47.03</center></td><td><center>9.50</center></td><td><center>49.58</center></td> |
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</tr> |
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<tr> |
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<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>53.41</strong></center></td><td><center><strong>52.44</strong></center></td><td><center>54.44</center></td><td><center><strong>69.36</strong></center></td><td><center><strong>61.96</strong></center></td><td><center>31.06</center></td><td><center><strong>51.23</strong></center></td> |
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</tr> |
|
<tr> |
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<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>50.48</center></td><td><center>52.01</center></td><td><center>52.37</center></td><td><center>66.97</center></td><td><center>56.34</center></td><td><center>25.98</center></td><td><center>49.18</center></td> |
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</tr> |
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<tr> |
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<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>48.27</em></center></td><td><center><em>46.66</em></center></td><td><center><em><strong>54.45</strong></em></center></td><td><center><em>63.73</em></center></td><td><center><em>49.33</em></center></td><td><center><em><strong>34.98</strong></em></center></td><td><center><em>40.45</em></center></td> |
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</tr> |
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</tbody> |
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</table> |
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|
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## Downstream tasks |
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|
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<table> |
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<tbody> |
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<tr> |
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<td></td> |
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<td colspan="4"><center><strong>LaRoSeDa</strong></center></td> |
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<td colspan="4"><center><strong>WMT</strong></center></td> |
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</tr> |
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<tr> |
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<td></td> |
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<td colspan="2"><center><strong>Few-shot</strong></center></td> |
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<td colspan="2"><center><strong>Finetuned</strong></center></td> |
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<td colspan="2"><center><strong>Few-shot</strong></center></td> |
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<td colspan="2"><center><strong>Finetuned</strong></center></td> |
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</tr> |
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<tr> |
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<td><strong>Model</strong></td> |
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<td><center><strong>Binary<br>(Macro F1)</strong></center></td> |
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<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
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<td><center><strong>Binary<br>(Macro F1)</strong></center></td> |
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<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
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<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
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<td><center><strong>RO-EN<br>(Bleu)</strong></center></td> |
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<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
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<td><center><strong>RO-EN<br>(Bleu)</strong></center> |
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</tr> |
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<tr> |
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<td>gemma-1.1-7b-it</td><td><center>87.54</center></td><td><center>51.48</center></td><td><center>83.87</center></td><td><center>85.61</center></td><td><center>17.96</center></td><td><center><strong>27.74</strong></center></td><td><center>25.48</center></td><td><center>36.11</center></td> |
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</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>97.86</strong></center></td><td><center><strong>65.70</strong></center></td><td><center>98.43</center></td><td><center><strong>87.17</strong></center></td><td><center><strong>27.91</strong></center></td><td><center>23.08</center></td><td><center><strong>27.99</strong></center></td><td><center><strong>39.51</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>86.96</center></td><td><center>56.72</center></td><td><center><strong>98.80</strong></center></td><td><center>85.81</center></td><td><center>24.45</center></td><td><center>14.20</center></td><td><center>25.96</center></td><td><center>39.07</center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>96.45</em></center></td><td><center><em>63.23</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>20.73</em></center></td><td><center><em>7.87</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
<table> |
|
<tbody> |
|
<tr> |
|
<td></td> |
|
<td colspan="4"><center><strong>XQuAD</strong></center></td> |
|
<td colspan="4"><center><strong>STS</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td></td> |
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><strong>Model</strong></td> |
|
<td><center><strong>(EM)</strong></center></td> |
|
<td><center><strong>(F1)</strong></center></td> |
|
<td><center><strong>(EM)</strong></center></td> |
|
<td><center><strong>(F1)</strong></center></td> |
|
<td><center><strong>(Spearman)</strong></center></td> |
|
<td><center><strong>(Pearson)</strong></center></td> |
|
<td><center><strong>(Spearman)</strong></center></td> |
|
<td><center><strong>(Pearson)</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>gemma-1.1-7b-it</td><td><center><strong>42.10</strong></center></td><td><center><strong>62.30</strong></center></td><td><center><strong>60.34</strong></center></td><td><center><strong>77.40</strong></center></td><td><center>49.10</center></td><td><center>50.23</center></td><td><center>83.43</center></td><td><center>83.64</center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>17.75</center></td><td><center>28.11</center></td><td><center>52.02</center></td><td><center>68.43</center></td><td><center><strong>73.96</strong></center></td><td><center><strong>75.16</strong></center></td><td><center>86.45</center></td><td><center>86.31</center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>26.03</center></td><td><center>41.58</center></td><td><center>46.72</center></td><td><center>60.79</center></td><td><center>73.23</center></td><td><center>71.58</center></td><td><center><strong>88.42</strong></center></td><td><center><strong>88.45</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>19.14</em></center></td><td><center><em>38.10</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>69.38</em></center></td><td><center><em>69.34</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
## MT-Bench |
|
|
|
<<table> |
|
<tbody> |
|
<tr> |
|
<td><strong>Model</strong></td> |
|
<td><strong><center>Average</center></strong></td> |
|
<td><strong><center>1st turn</center></strong></td> |
|
<td><strong><center>2nd turn</center></strong></td> |
|
<td><strong><center>Answers in Ro</center></strong></td> |
|
</tr> |
|
<tr> |
|
<td>gemma-1.1-7b-it</td><td><center>4.83</center></td><td><center>5.11</center></td><td><center>4.55</center></td><td><center><strong>160/160</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>5.26</center></td><td><center><strong>5.92</strong></center></td><td><center>4.60</center></td><td><center><strong>160/160</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>5.24</center></td><td><center>5.55</center></td><td><center>4.94</center></td><td><center><strong>160/160</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>5.47</strong></em></center></td><td><center><em><strong>5.92</strong></em></center></td><td><center><em><strong>5.03</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
## RoCulturaBench |
|
|
|
<table> |
|
<tbody> |
|
<tr> |
|
<td><strong>Model</strong></td> |
|
<td><strong><center>Average</center></strong></td> |
|
<td><strong><center>Answers in Ro</center></strong></td> |
|
</tr> |
|
<tr> |
|
<td>gemma-1.1-7b-it</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>3.26</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>3.51</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>3.94</strong></em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
## RoGemma Model Family |
|
|
|
| Model | Link | |
|
|--------------------|:--------:| |
|
|RoGemma-7b-Instruct-2024-06-28| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-06-28) | |
|
|RoGemma-7b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09) | |
|
|*RoGemma-7b-Instruct-DPO-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09) | |
|
|
|
|
|
## Citation |
|
|
|
``` |
|
@misc{masala2024vorbecstiromanecsterecipetrain, |
|
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, |
|
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea}, |
|
year={2024}, |
|
eprint={2406.18266}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL}, |
|
url={https://arxiv.org/abs/2406.18266}, |
|
} |
|
``` |
|
<!-- **APA:** |
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[More Information Needed] --> |
