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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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- meta-llama/Meta-Llama-3-8B |
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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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model-index: |
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- name: OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28 |
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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: 5.15 |
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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: 3.71 |
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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: 50.56 |
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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: 44.70 |
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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: 52.19 |
|
- 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: 67.23 |
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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: 57.69 |
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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: 30.23 |
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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: 51.34 |
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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: 97.52 |
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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: 67.41 |
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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: 94.15 |
|
- 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: |
|
- name: Average macro-f1 |
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type: macro-f1 |
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value: 87.13 |
|
- 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: Average bleu |
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type: bleu |
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value: 24.01 |
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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: 27.36 |
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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: |
|
- name: Average bleu |
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type: bleu |
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value: 26.53 |
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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: |
|
- name: Average bleu |
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type: bleu |
|
value: 40.36 |
|
- 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: 39.43 |
|
- task: |
|
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: |
|
- name: Average f1 |
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type: f1 |
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value: 59.50 |
|
- task: |
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type: text-generation |
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dataset: |
|
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 |
|
value: 44.45 |
|
- task: |
|
type: text-generation |
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dataset: |
|
name: XQuAD_finetuned |
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type: XQuAD_finetuned |
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metrics: |
|
- name: Average f1 |
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type: f1 |
|
value: 59.76 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: STS |
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type: STS |
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metrics: |
|
- name: Average spearman |
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type: spearman |
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value: 77.20 |
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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: |
|
- name: Average pearson |
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type: pearson |
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value: 77.87 |
|
- 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: |
|
- name: Average spearman |
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type: spearman |
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value: 85.80 |
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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: |
|
- name: Average pearson |
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type: pearson |
|
value: 86.05 |
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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: First turn |
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type: Score |
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value: 6.03 |
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- name: Second turn |
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type: Score |
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value: 4.28 |
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- task: |
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type: text-generation |
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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 |
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type: accuracy |
|
value: 41.90 |
|
- name: 1-shot |
|
type: accuracy |
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value: 44.30 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 44.56 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 45.50 |
|
- name: 10-shot |
|
type: accuracy |
|
value: 46.10 |
|
- name: 25-shot |
|
type: accuracy |
|
value: 45.84 |
|
- 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: 0-shot |
|
type: accuracy |
|
value: 50.85 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 51.24 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 53.30 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 53.39 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_winogrande |
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type: OpenLLM-Ro/ro_winogrande |
|
metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 65.19 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 66.54 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 67.88 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 69.30 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_hellaswag |
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type: OpenLLM-Ro/ro_hellaswag |
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metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 56.12 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 57.37 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 57.92 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 58.18 |
|
- name: 10-shot |
|
type: accuracy |
|
value: 58.85 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: OpenLLM-Ro/ro_gsm8k |
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type: OpenLLM-Ro/ro_gsm8k |
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metrics: |
|
- name: 1-shot |
|
type: accuracy |
|
value: 29.42 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 30.02 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 31.24 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
|
metrics: |
|
- name: 0-shot |
|
type: macro-f1 |
|
value: 97.43 |
|
- name: 1-shot |
|
type: macro-f1 |
|
value: 96.60 |
|
- name: 3-shot |
|
type: macro-f1 |
|
value: 97.90 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 98.13 |
|
- 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 |
|
value: 63.77 |
|
- name: 1-shot |
|
type: macro-f1 |
|
value: 68.91 |
|
- name: 3-shot |
|
type: macro-f1 |
|
value: 66.36 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 70.61 |
|
- 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: 0-shot |
|
type: bleu |
|
value: 6.92 |
|
- name: 1-shot |
|
type: bleu |
|
value: 29.33 |
|
- name: 3-shot |
|
type: bleu |
|
value: 29.79 |
|
- name: 5-shot |
|
type: bleu |
|
value: 30.02 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: WMT_RO-EN |
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type: WMT_RO-EN |
|
metrics: |
|
- name: 0-shot |
|
type: bleu |
|
value: 4.50 |
|
- name: 1-shot |
|
type: bleu |
|
value: 30.30 |
|
- name: 3-shot |
|
type: bleu |
|
value: 36.96 |
|
- name: 5-shot |
|
type: bleu |
|
value: 37.70 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_EM |
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type: XQuAD_EM |
|
metrics: |
|
- name: 0-shot |
|
type: exact_match |
|
value: 4.45 |
|
- name: 1-shot |
|
type: exact_match |
|
value: 48.24 |
|
- name: 3-shot |
|
type: exact_match |
|
value: 52.03 |
|
- name: 5-shot |
|
type: exact_match |
|
value: 53.03 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_F1 |
|
type: XQuAD_F1 |
|
metrics: |
|
- name: 0-shot |
|
type: f1 |
|
value: 26.08 |
|
- name: 1-shot |
|
type: f1 |
|
value: 68.40 |
|
- name: 3-shot |
|
type: f1 |
|
value: 71.92 |
|
- name: 5-shot |
|
type: f1 |
|
value: 71.60 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_Spearman |
|
type: STS_Spearman |
|
metrics: |
|
- name: 1-shot |
|
type: spearman |
|
value: 77.76 |
|
- name: 3-shot |
|
type: spearman |
|
value: 76.72 |
|
- name: 5-shot |
|
type: spearman |
|
value: 77.12 |
|
- 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: |
|
- name: 1-shot |
|
type: pearson |
|
value: 77.83 |
|
- name: 3-shot |
|
type: pearson |
|
value: 77.64 |
|
- name: 5-shot |
|
type: pearson |
|
value: 78.13 |
|
|
|
--- |
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|
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# Model Card for Model ID |
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*Built with Meta Llama 3* |
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<!-- Provide a quick summary of what the model is/does. --> |
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RoLlama3 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 8B 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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- **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:** [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) |
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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) |
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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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|
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## Intended Use |
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### Intended Use Cases |
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RoLlama3 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/RoLlama3-8b-Instruct-2024-06-28") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28") |
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instruction = "Ce jocuri de societate pot juca cu prietenii mei?" |
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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, 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>Llama-3-8B-Instruct</td><td><center>50.62</center></td><td><center>43.69</center></td><td><center>52.04</center></td><td><center>59.33</center></td><td><center>53.19</center></td><td><center><strong>43.87</strong></center></td><td><center><strong>51.59</strong></center></td> |
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</tr> |
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<tr> |
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<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>50.56</em></center></td><td><center><em>44.70</em></center></td><td><center><em>52.19</em></center></td><td><center><em><strong>67.23</strong></em></center></td><td><center><em>57.69</em></center></td><td><center><em>30.23</em></center></td><td><center><em>51.34</em></center></td> |
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</tr> |
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<tr> |
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<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center><strong>52.21</strong></center></td><td><center><strong>47.94</strong></center></td><td><center><strong>53.50</strong></center></td><td><center>66.06</center></td><td><center><strong>59.72</strong></center></td><td><center>40.16</center></td><td><center>45.90</center></td> |
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</tr> |
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<tr> |
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<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>49.96</center></td><td><center>46.29</center></td><td><center>53.29</center></td><td><center>65.57</center></td><td><center>58.15</center></td><td><center>34.77</center></td><td><center>41.70</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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<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> |
|
<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> |
|
<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-3-8B-Instruct</td><td><center>95.88</center></td><td><center>56.21</center></td><td><center><strong>98.53</strong></center></td><td><center>86.19</center></td><td><center>18.88</center></td><td><center><strong>30.98</strong></center></td><td><center><strong>28.02</strong></center></td><td><center>40.28</center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em><strong>97.52</strong></em></center></td><td><center><em><strong>67.41</strong></em></center></td><td><center><em>94.15</em></center></td><td><center><em>87.13</em></center></td><td><center><em><strong>24.01</strong></em></center></td><td><center><em>27.36</em></center></td><td><center><em>26.53</em></center></td><td><center><em>40.36</em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>95.58</center></td><td><center>61.20</center></td><td><center>96.46</center></td><td><center><strong>87.26</strong></center></td><td><center>22.92</center></td><td><center>24.28</center></td><td><center>27.31</center></td><td><center><strong>40.52</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>97.48</center></td><td><center>54.00</center></td><td><center>-</center></td><td><center>-</center></td><td><center>22.09</center></td><td><center>23.00</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-3-8B-Instruct</td><td><center><strong>39.47</strong></center></td><td><center>58.67</center></td><td><center><strong>67.65</strong></center></td><td><center><strong>82.77</strong></center></td><td><center>73.04</center></td><td><center>72.36</center></td><td><center>83.49</center></td><td><center>84.06</center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>39.43</em></center></td><td><center><em><strong>59.50</strong></em></center></td><td><center><em>44.45</em></center></td><td><center><em>59.76</em></center></td><td><center><em>77.20</em></center></td><td><center><em>77.87</em></center></td><td><center><em>85.80</em></center></td><td><center><em>86.05</em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>18.89</center></td><td><center>31.79</center></td><td><center>50.84</center></td><td><center>65.18</center></td><td><center>77.60</center></td><td><center>76.86</center></td><td><center><strong>86.70</strong></center></td><td><center><strong>87.09</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>26.05</center></td><td><center>42.77</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>79.64</strong></center></td><td><center><strong>79.52</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>Llama-3-8B-Instruct</td><td><center><strong>5.96</strong></center></td><td><center>6.16</center></td><td><center><strong>5.76</strong></center></td><td><center>158/160</center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>5.15</em></center></td><td><center><em>6.03</em></center></td><td><center><em>4.28</em></center></td><td><center><em><strong>160/160</strong></em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>5.38</center></td><td><center>6.09</center></td><td><center>4.67</center></td><td><center><strong>160/160</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>5.87</center></td><td><center><strong>6.22</strong></center></td><td><center>5.49</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-3-8B-Instruct</td><td><center><strong>4.62</strong></center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>3.71</em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>3.81</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>4.40</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
|
|
## RoLlama3 Model Family |
|
|
|
| Model | Link | |
|
|--------------------|:--------:| |
|
|*RoLlama3-8b-Instruct-2024-06-28*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28) | |
|
|RoLlama3-8b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09) | |
|
|RoLlama3-8b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-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] --> |
