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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/RoLlama2-7b-Base |
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datasets: |
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- OpenLLM-Ro/ro_sft_alpaca |
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- OpenLLM-Ro/ro_sft_alpaca_gpt4 |
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- OpenLLM-Ro/ro_sft_dolly |
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- OpenLLM-Ro/ro_sft_selfinstruct_gpt4 |
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- OpenLLM-Ro/ro_sft_norobots |
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- OpenLLM-Ro/ro_sft_orca |
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- OpenLLM-Ro/ro_sft_camel |
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- OpenLLM-Ro/ro_sft_oasst |
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- OpenLLM-Ro/ro_sft_ultrachat |
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- OpenLLM-Ro/ro_sft_magpie_mt |
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- OpenLLM-Ro/ro_sft_magpie_reasoning |
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model-index: |
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- name: OpenLLM-Ro/RoLlama2-7b-Instruct-2025-04-23 |
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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: 4.97 |
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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: 4.56 |
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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: 45.51 |
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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: 45.7 |
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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: 40.36 |
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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: 63.26 |
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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_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: 60.25 |
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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: 18.02 |
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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: 45.48 |
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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: 97.6 |
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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: 60.22 |
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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: 27.21 |
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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: 22.15 |
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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: 47.39 |
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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: 65.77 |
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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: 59.05 |
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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: 56.45 |
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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: 5.56 |
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- name: Second turn |
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type: Score |
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value: 4.39 |
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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: 43.02 |
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- name: 1-shot |
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type: accuracy |
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value: 45.84 |
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- name: 3-shot |
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type: accuracy |
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value: 45.24 |
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- name: 5-shot |
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type: accuracy |
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value: 46.19 |
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- name: 10-shot |
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type: accuracy |
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value: 46.7 |
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- name: 25-shot |
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type: accuracy |
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value: 47.22 |
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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: 38.64 |
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- name: 1-shot |
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type: accuracy |
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value: 40.77 |
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- name: 3-shot |
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type: accuracy |
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value: 41.19 |
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- name: 5-shot |
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type: accuracy |
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value: 40.86 |
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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: 63.61 |
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- name: 1-shot |
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type: accuracy |
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value: 62.75 |
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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: 63.22 |
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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: 0-shot |
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type: accuracy |
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value: 59.79 |
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- name: 1-shot |
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type: accuracy |
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value: 59.62 |
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- name: 3-shot |
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type: accuracy |
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value: 60.12 |
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- name: 5-shot |
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type: accuracy |
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value: 60.71 |
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- name: 10-shot |
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type: accuracy |
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value: 61.01 |
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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: 6.14 |
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- name: 3-shot |
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type: accuracy |
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value: 22.52 |
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- name: 5-shot |
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type: accuracy |
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value: 25.4 |
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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: 0-shot |
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type: macro-f1 |
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value: 98.17 |
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- name: 1-shot |
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type: macro-f1 |
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value: 96.3 |
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- name: 3-shot |
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type: macro-f1 |
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value: 97.8 |
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- name: 5-shot |
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type: macro-f1 |
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value: 98.13 |
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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: 0-shot |
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type: macro-f1 |
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value: 49.8 |
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- name: 1-shot |
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type: macro-f1 |
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value: 56.03 |
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- name: 3-shot |
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type: macro-f1 |
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value: 65.33 |
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- name: 5-shot |
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type: macro-f1 |
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value: 69.7 |
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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: 0-shot |
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type: bleu |
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value: 19.34 |
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- name: 1-shot |
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type: bleu |
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value: 29.89 |
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- name: 3-shot |
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type: bleu |
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value: 29.99 |
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- name: 5-shot |
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type: bleu |
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value: 29.62 |
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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: 0-shot |
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type: bleu |
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value: 2.29 |
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- name: 1-shot |
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type: bleu |
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value: 14.74 |
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- name: 3-shot |
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type: bleu |
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value: 34.82 |
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- name: 5-shot |
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type: bleu |
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value: 36.75 |
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- 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: |
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- name: 0-shot |
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type: exact_match |
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value: 42.86 |
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- name: 1-shot |
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type: exact_match |
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value: 47.82 |
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- name: 3-shot |
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type: exact_match |
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value: 48.32 |
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- name: 5-shot |
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type: exact_match |
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value: 50.59 |
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- 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: |
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- name: 0-shot |
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type: f1 |
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value: 63.66 |
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- name: 1-shot |
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type: f1 |
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value: 65.27 |
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- name: 3-shot |
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type: f1 |
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value: 66.04 |
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- name: 5-shot |
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type: f1 |
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value: 68.12 |
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- 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: |
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- name: 1-shot |
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type: spearman |
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value: 54.51 |
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- name: 3-shot |
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type: spearman |
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value: 60.98 |
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- name: 5-shot |
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type: spearman |
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value: 61.65 |
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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: 54.35 |
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- name: 3-shot |
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type: pearson |
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value: 57.88 |
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- name: 5-shot |
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type: pearson |
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value: 57.13 |
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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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RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **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 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:** [RoLlama2-7b-Base](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base) |
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- **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_ultrachat), [RoMagpiePro](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_mt), [RoMagpieReasoning](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_reasoning) |
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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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RoLlama2 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/RoLlama2-7b-Instruct-2025-04-23") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Instruct-2025-04-23") |
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instruction = "Care este cel mai înalt vârf muntos din România?" |
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chat = [ |
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{"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."}, |
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{"role": "user", "content": instruction}, |
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] |
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prompt = tokenizer.apply_chat_template(chat, tokenize=False) |
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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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## Academic Benchmarks |
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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>Llama-2-7b-chat</td><td><center>36.84</center></td><td><center>37.03</center></td><td><center>33.80</center></td><td><center>55.87</center></td><td><center>45.36</center></td><td><center>4.90</center></td><td><center>44.09</center></td> |
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</tr> |
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<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>45.71</center></td><td><center>43.66</center></td><td><center>39.70</center></td><td><center><strong>70.34</strong></center></td><td><center>57.36</center></td><td><center><strong>18.78</strong></center></td><td><center>44.44</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-10-09</td><td><center>44.50</center></td><td><center>44.73</center></td><td><center>40.39</center></td><td><center>63.67</center></td><td><center>59.12</center></td><td><center>13.29</center></td><td><center>45.78</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama2-7b-Instruct-2025-04-23</em></td><td><center><em>45.51</em></center></td><td><center><em>45.70</em></center></td><td><center><em>40.36</em></center></td><td><center><em>63.26</em></center></td><td><center><em>60.25</em></center></td><td><center><em>18.02</em></center></td><td><center><em>45.48</em></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>43.20</center></td><td><center>44.24</center></td><td><center>38.39</center></td><td><center>62.57</center></td><td><center>59.20</center></td><td><center>15.72</center></td><td><center>39.07</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2025-04-23</td><td><center><strong>46.77</strong></center></td><td><center><strong>48.16</strong></center></td><td><center><strong>41.38</strong></center></td><td><center>64.15</center></td><td><center><strong>61.37</strong></center></td><td><center>18.35</center></td><td><center><strong>47.20</strong></center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
## Downstream tasks |
|
|
|
|
|
|
|
|
<table> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td></td> |
|
|
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td> |
|
|
<td colspan="4"><center><strong>WMT</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>Binary<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>Binary<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
|
|
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td> |
|
|
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
|
|
<td><center><strong>RO-EN<br>(Bleu)</strong></center> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Llama-2-7b-chat</td><td><center>87.78</center></td><td><center>52.81</center></td><td><center>97.27</center></td><td><center>82.02</center></td><td><center>15.55</center></td><td><center><strong>28.53</strong></center></td><td><center>19.99</center></td><td><center>31.48</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>97.48</center></td><td><center><strong>65.26</strong></center></td><td><center><strong>98.83</strong></center></td><td><center><strong>87.28</strong></center></td><td><center><strong>27.38</strong></center></td><td><center>10.32</center></td><td><center>27.59</center></td><td><center><strong>40.13</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-10-09</td><td><center>97.66</center></td><td><center>62.41</center></td><td><center>97.97</center></td><td><center>60.89</center></td><td><center>27.13</center></td><td><center>19.39</center></td><td><center><strong>27.63</strong></center></td><td><center>39.75</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama2-7b-Instruct-2025-04-23</em></td><td><center><em>97.60</em></center></td><td><center><em>60.22</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>27.21</em></center></td><td><center><em>22.15</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>97.31</center></td><td><center>60.56</center></td><td><center>-</center></td><td><center>-</center></td><td><center>26.56</center></td><td><center>21.68</center></td><td><center>-</center></td><td><center>-</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2025-04-23</td><td><center><strong>97.77</strong></center></td><td><center>65.21</center></td><td><center>-</center></td><td><center>-</center></td><td><center>25.48</center></td><td><center>22.75</center></td><td><center>-</center></td><td><center>-</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>Llama-2-7b-chat</td><td><center>32.35</center></td><td><center>54.00</center></td><td><center><strong>60.34</strong></center></td><td><center><strong>75.98</strong></center></td><td><center>32.56</center></td><td><center>31.99</center></td><td><center>74.08</center></td><td><center>72.64</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>44.52</center></td><td><center>64.75</center></td><td><center>54.96</center></td><td><center>70.20</center></td><td><center>65.50</center></td><td><center><strong>67.79</strong></center></td><td><center>84.44</center></td><td><center>84.76</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-10-09</td><td><center>45.71</center></td><td><center>65.08</center></td><td><center>59.24</center></td><td><center>74.25</center></td><td><center>59.69</center></td><td><center>57.16</center></td><td><center><strong>84.66</strong></center></td><td><center><strong>85.07</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama2-7b-Instruct-2025-04-23</em></td><td><center><em><strong>47.39</strong></em></center></td><td><center><em><strong>65.77</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>59.05</em></center></td><td><center><em>56.45</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>35.78</center></td><td><center>59.31</center></td><td><center>-</center></td><td><center>-</center></td><td><center>61.22</center></td><td><center>58.41</center></td><td><center>-</center></td><td><center>-</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2025-04-23</td><td><center>38.28</center></td><td><center>60.88</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>66.76</strong></center></td><td><center>64.72</center></td><td><center>-</center></td><td><center>-</center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
|
|
|
## Romanian 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>Llama-2-7b-chat</td><td><center>1.08</center></td><td><center>1.44</center></td><td><center>0.73</center></td><td><center>45/160</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>3.86</center></td><td><center>4.67</center></td><td><center>3.04</center></td><td><center><strong>160/160</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-10-09</td><td><center>4.43</center></td><td><center>4.92</center></td><td><center>3.94</center></td><td><center><strong>160/160</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama2-7b-Instruct-2025-04-23</em></td><td><center><em>4.97</em></center></td><td><center><em>5.56</em></center></td><td><center><em>4.39</em></center></td><td><center><em><strong>160/160</strong></em></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>4.61</center></td><td><center>5.15</center></td><td><center>4.06</center></td><td><center><strong>160/160</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2025-04-23</td><td><center><strong>5.55</strong></center></td><td><center><strong>5.84</strong></center></td><td><center><strong>5.26</strong></center></td><td><center><strong>160/160</strong></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>Llama-2-7b-chat</td><td><center>1.21</center></td><td><center>33/100</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>3.77</center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-2024-10-09</td><td><center>4.08</center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama2-7b-Instruct-2025-04-23</em></td><td><center><em>4.56</em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>4.80</center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama2-7b-Instruct-DPO-2025-04-23</td><td><center><strong>5.24</strong></center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
## RoLlama2 Model Family |
|
|
|
|
|
| Model | Link | |
|
|
|--------------------|:--------:| |
|
|
|RoLlama2-7b-Base-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14) | |
|
|
|RoLlama2-7b-Instruct-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-05-14) | |
|
|
|RoLlama2-7b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09) | |
|
|
|*RoLlama2-7b-Instruct-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2025-04-23) | |
|
|
|RoLlama2-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-DPO-2024-10-09) | |
|
|
|RoLlama2-7b-Instruct-DPO-2025-04-23| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-DPO-2025-04-23) | |
|
|
|
|
|
|
|
|
|
|
|
## 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:** |
|
|
|
|
|
[More Information Needed] --> |
