|
--- |
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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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- google/gemma-2-9b-it |
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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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model-index: |
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- name: OpenLLM-Ro/RoGemma2-9b-Instruct-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: |
|
- name: Score |
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type: Score |
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value: 6.08 |
|
- 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: 4.20 |
|
- 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: 57.06 |
|
- 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: 56.20 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_mmlu |
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type: OpenLLM-Ro/ro_mmlu |
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metrics: |
|
- name: Average accuracy |
|
type: accuracy |
|
value: 62.98 |
|
- task: |
|
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: |
|
- name: Average accuracy |
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type: accuracy |
|
value: 71.00 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
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 |
|
type: accuracy |
|
value: 60.52 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_gsm8k |
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type: OpenLLM-Ro/ro_gsm8k |
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metrics: |
|
- name: Average accuracy |
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type: accuracy |
|
value: 37.86 |
|
- 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 |
|
value: 53.77 |
|
- task: |
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type: text-generation |
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dataset: |
|
name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
|
- name: Average macro-f1 |
|
type: macro-f1 |
|
value: 96.19 |
|
- 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: Average macro-f1 |
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type: macro-f1 |
|
value: 62.49 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: LaRoSeDa_binary_finetuned |
|
type: LaRoSeDa_binary_finetuned |
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metrics: |
|
- name: Average macro-f1 |
|
type: macro-f1 |
|
value: 98.93 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: LaRoSeDa_multiclass_finetuned |
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type: LaRoSeDa_multiclass_finetuned |
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metrics: |
|
- name: Average macro-f1 |
|
type: macro-f1 |
|
value: 88.33 |
|
- task: |
|
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: Average bleu |
|
type: bleu |
|
value: 25.74 |
|
- 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: Average bleu |
|
type: bleu |
|
value: 23.16 |
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- task: |
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type: text-generation |
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dataset: |
|
name: WMT_EN-RO_finetuned |
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type: WMT_EN-RO_finetuned |
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metrics: |
|
- name: Average bleu |
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type: bleu |
|
value: 28.43 |
|
- task: |
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type: text-generation |
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dataset: |
|
name: WMT_RO-EN_finetuned |
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type: WMT_RO-EN_finetuned |
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metrics: |
|
- name: Average bleu |
|
type: bleu |
|
value: 40.94 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD |
|
type: XQuAD |
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metrics: |
|
- name: Average exact_match |
|
type: exact_match |
|
value: 51.37 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD |
|
type: XQuAD |
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metrics: |
|
- name: Average f1 |
|
type: f1 |
|
value: 70.74 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_finetuned |
|
type: XQuAD_finetuned |
|
metrics: |
|
- name: Average exact_match |
|
type: exact_match |
|
value: 50.00 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_finetuned |
|
type: XQuAD_finetuned |
|
metrics: |
|
- name: Average f1 |
|
type: f1 |
|
value: 64.10 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS |
|
type: STS |
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metrics: |
|
- name: Average spearman |
|
type: spearman |
|
value: 77.15 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: STS |
|
type: STS |
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metrics: |
|
- name: Average pearson |
|
type: pearson |
|
value: 77.10 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_finetuned |
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type: STS_finetuned |
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metrics: |
|
- name: Average spearman |
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type: spearman |
|
value: 89.45 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_finetuned |
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type: STS_finetuned |
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metrics: |
|
- name: Average pearson |
|
type: pearson |
|
value: 89.89 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: RoMT-Bench |
|
type: RoMT-Bench |
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metrics: |
|
- name: First turn |
|
type: Score |
|
value: 6.78 |
|
- name: Second turn |
|
type: Score |
|
value: 5.39 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_arc_challenge |
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type: OpenLLM-Ro/ro_arc_challenge |
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metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 53.30 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 54.93 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 57.07 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 57.33 |
|
- name: 10-shot |
|
type: accuracy |
|
value: 57.16 |
|
- name: 25-shot |
|
type: accuracy |
|
value: 57.41 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_mmlu |
|
type: OpenLLM-Ro/ro_mmlu |
|
metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 59.20 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 62.47 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 64.97 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 65.30 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_winogrande |
|
type: OpenLLM-Ro/ro_winogrande |
|
metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 68.67 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 71.03 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 71.90 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 72.38 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_hellaswag |
|
type: OpenLLM-Ro/ro_hellaswag |
|
metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 62.29 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 63.12 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 61.34 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 55.62 |
|
- name: 10-shot |
|
type: accuracy |
|
value: 60.25 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_gsm8k |
|
type: OpenLLM-Ro/ro_gsm8k |
|
metrics: |
|
- name: 1-shot |
|
type: accuracy |
|
value: 36.77 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 32.83 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 43.97 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: LaRoSeDa_binary |
|
type: LaRoSeDa_binary |
|
metrics: |
|
- name: 0-shot |
|
type: macro-f1 |
|
value: 92.63 |
|
- name: 1-shot |
|
type: macro-f1 |
|
value: 95.86 |
|
- name: 3-shot |
|
type: macro-f1 |
|
value: 98.03 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 98.23 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: LaRoSeDa_multiclass |
|
type: LaRoSeDa_multiclass |
|
metrics: |
|
- name: 0-shot |
|
type: macro-f1 |
|
value: 38.51 |
|
- name: 1-shot |
|
type: macro-f1 |
|
value: 69.70 |
|
- name: 3-shot |
|
type: macro-f1 |
|
value: 71.38 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 70.37 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: WMT_EN-RO |
|
type: WMT_EN-RO |
|
metrics: |
|
- name: 0-shot |
|
type: bleu |
|
value: 11.87 |
|
- name: 1-shot |
|
type: bleu |
|
value: 29.30 |
|
- name: 3-shot |
|
type: bleu |
|
value: 30.80 |
|
- name: 5-shot |
|
type: bleu |
|
value: 30.99 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: WMT_RO-EN |
|
type: WMT_RO-EN |
|
metrics: |
|
- name: 0-shot |
|
type: bleu |
|
value: 1.03 |
|
- name: 1-shot |
|
type: bleu |
|
value: 22.25 |
|
- name: 3-shot |
|
type: bleu |
|
value: 32.75 |
|
- name: 5-shot |
|
type: bleu |
|
value: 36.61 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_EM |
|
type: XQuAD_EM |
|
metrics: |
|
- name: 0-shot |
|
type: exact_match |
|
value: 52.60 |
|
- name: 1-shot |
|
type: exact_match |
|
value: 52.94 |
|
- name: 3-shot |
|
type: exact_match |
|
value: 49.66 |
|
- name: 5-shot |
|
type: exact_match |
|
value: 50.25 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_F1 |
|
type: XQuAD_F1 |
|
metrics: |
|
- name: 0-shot |
|
type: f1 |
|
value: 71.11 |
|
- name: 1-shot |
|
type: f1 |
|
value: 71.67 |
|
- name: 3-shot |
|
type: f1 |
|
value: 69.03 |
|
- name: 5-shot |
|
type: f1 |
|
value: 71.15 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_Spearman |
|
type: STS_Spearman |
|
metrics: |
|
- name: 1-shot |
|
type: spearman |
|
value: 78.03 |
|
- name: 3-shot |
|
type: spearman |
|
value: 81.53 |
|
- name: 5-shot |
|
type: spearman |
|
value: 71.88 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_Pearson |
|
type: STS_Pearson |
|
metrics: |
|
- name: 1-shot |
|
type: pearson |
|
value: 79.09 |
|
- name: 3-shot |
|
type: pearson |
|
value: 80.89 |
|
- name: 5-shot |
|
type: pearson |
|
value: 71.33 |
|
|
|
|
|
--- |
|
|
|
# Model Card for Model ID |
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|
|
<!-- Provide a quick summary of what the model is/does. --> |
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RoGemma2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 9B model**. Links to other models can be found at the bottom of this page. |
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|
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## Model Details |
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|
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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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|
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- **Developed by:** OpenLLM-Ro |
|
<!-- - **Funded by [optional]:** [More Information Needed] --> |
|
<!-- - **Shared by [optional]:** [More Information Needed] --> |
|
<!-- - **Model type:** [More Information Needed] --> |
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- **Language(s):** Romanian |
|
- **License:** cc-by-nc-4.0 |
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- **Finetuned from model:** [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) |
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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) |
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|
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### Model Sources |
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|
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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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|
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## Intended Use |
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|
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### Intended Use Cases |
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RoGemma2 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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|
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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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|
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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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|
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tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09") |
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|
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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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|
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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 |
|
|
|
<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> |
|
<tr> |
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<td>gemma-2-9b-it</td><td><center>56.22</center></td><td><center>50.33</center></td><td><center><strong>64.01</strong></center></td><td><center>64.88</center></td><td><center><strong>63.11</strong></center></td><td><center>41.95</center></td><td><center>53.03</center></td> |
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</tr> |
|
<tr> |
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<td><em>RoGemma2-9b-Instruct-2024-10-09</em></td><td><center><em>57.06</em></center></td><td><center><em><strong>56.20</strong></em></center></td><td><center><em>62.98</em></center></td><td><center><em><strong>71.00</strong></em></center></td><td><center><em>60.52</em></center></td><td><center><em>37.86</em></center></td><td><center><em><strong>53.77</strong></em></center></td> |
|
</tr> |
|
<tr> |
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<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center><strong>59.08</strong></center></td><td><center>54.10</center></td><td><center>63.41</center></td><td><center>70.02</center></td><td><center>59.35</center></td><td><center><strong>57.24</strong></center></td><td><center>50.39</center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
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## Downstream tasks |
|
|
|
<table> |
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<tbody> |
|
<tr> |
|
<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> |
|
<tr> |
|
<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> |
|
<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> |
|
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
|
<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> |
|
<tr> |
|
<td>gemma-2-9b-it</td><td><center>90.82</center></td><td><center>52.51</center></td><td><center><strong>98.97</strong></center></td><td><center>86.02</center></td><td><center>19.97</center></td><td><center><strong>28.94</strong></center></td><td><center>27.94</center></td><td><center><strong>41.61</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma2-9b-Instruct-2024-10-09</em></td><td><center><em>96.19</em></center></td><td><center><em>62.49</em></center></td><td><center><em>98.93</em></center></td><td><center><em><strong>88.33</strong></em></center></td><td><center><em>25.74</em></center></td><td><center><em>23.16</em></center></td><td><center><em><strong>28.43</strong></em></center></td><td><center><em>40.94</em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center><strong>97.74</strong></center></td><td><center><strong>67.40</strong></center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>27.32</strong></center></td><td><center>15.96</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>gemma-2-9b-it</td><td><center>37.56</center></td><td><center>57.48</center></td><td><center><strong>71.09</strong></center></td><td><center><strong>84.78</strong></center></td><td><center>71.39</center></td><td><center>71.73</center></td><td><center>89.07</center></td><td><center>89.29</center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma2-9b-Instruct-2024-10-09</em></td><td><center><em><strong>51.37</strong></em></center></td><td><center><em><strong>70.74</strong></em></center></td><td><center><em>50.00</em></center></td><td><center><em>64.10</em></center></td><td><center><em>77.15</em></center></td><td><center><em>77.10</em></center></td><td><center><em><strong>89.45</strong></em></center></td><td><center><em><strong>89.89</strong></em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>32.42</center></td><td><center>58.68</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>80.82</strong></center></td><td><center><strong>81.50</strong></center></td><td><center>-</center></td><td><center>-</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-2-9b-it</td><td><center><strong>7.50</strong></center></td><td><center><strong>7.91</strong></center></td><td><center><strong>7.09</strong></center></td><td><center>159/160</center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma2-9b-Instruct-2024-10-09</em></td><td><center><em>6.08</em></center></td><td><center><em>6.78</em></center></td><td><center><em>5.39</em></center></td><td><center><em><strong>160/160</strong></em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>6.77</center></td><td><center>7.24</center></td><td><center>6.30</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>gemma-2-9b-it</td><td><center><strong>5.68</strong></center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoGemma2-9b-Instruct-2024-10-09</em></td><td><center><em>4.20</em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>4.83</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
## RoGemma2 Model Family |
|
|
|
| Model | Link | |
|
|--------------------|:--------:| |
|
|*RoGemma2-9b-Instruct-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09) | |
|
|RoGemma2-9b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-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:** |
|
|
|
[More Information Needed] --> |
