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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/RoLlama3.1-8b-Instruct-2024-10-09 |
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datasets: |
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- OpenLLM-Ro/ro_dpo_helpsteer |
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model-index: |
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- name: OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2024-10-09 |
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results: |
|
- 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: 6.21 |
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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: 4.42 |
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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: 52.74 |
|
- 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: 44.84 |
|
- 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: 55.06 |
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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: 65.87 |
|
- 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: 58.67 |
|
- 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: 44.17 |
|
- 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: |
|
- name: Average accuracy |
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type: accuracy |
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value: 47.82 |
|
- task: |
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type: text-generation |
|
dataset: |
|
name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
|
- name: Average macro-f1 |
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type: macro-f1 |
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value: 96.10 |
|
- 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 |
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value: 55.37 |
|
- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary_finetuned |
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type: LaRoSeDa_binary_finetuned |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 0.00 |
|
- 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 |
|
type: macro-f1 |
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value: 0.00 |
|
- 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: 21.29 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN |
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type: WMT_RO-EN |
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metrics: |
|
- name: Average bleu |
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type: bleu |
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value: 21.86 |
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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 |
|
type: bleu |
|
value: 0.00 |
|
- 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: 0.00 |
|
- 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: 21.58 |
|
- 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: 36.54 |
|
- 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: |
|
- name: Average exact_match |
|
type: exact_match |
|
value: 0.00 |
|
- task: |
|
type: text-generation |
|
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: 0.00 |
|
- task: |
|
type: text-generation |
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dataset: |
|
name: STS |
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type: STS |
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metrics: |
|
- name: Average spearman |
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type: spearman |
|
value: 78.01 |
|
- 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: 77.98 |
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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 spearman |
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type: spearman |
|
value: 0.00 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_finetuned |
|
type: STS_finetuned |
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metrics: |
|
- name: Average pearson |
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type: pearson |
|
value: 0.00 |
|
- task: |
|
type: text-generation |
|
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: 6.74 |
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- name: Second turn |
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type: Score |
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value: 5.69 |
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- task: |
|
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: |
|
- name: 0-shot |
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type: accuracy |
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value: 41.82 |
|
- name: 1-shot |
|
type: accuracy |
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value: 43.70 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 45.33 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 46.10 |
|
- name: 10-shot |
|
type: accuracy |
|
value: 45.76 |
|
- name: 25-shot |
|
type: accuracy |
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value: 46.36 |
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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_mmlu |
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type: OpenLLM-Ro/ro_mmlu |
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metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 53.75 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 54.94 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 56.07 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 55.47 |
|
- task: |
|
type: text-generation |
|
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: 0-shot |
|
type: accuracy |
|
value: 64.40 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 66.22 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 65.75 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 67.09 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_hellaswag |
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type: OpenLLM-Ro/ro_hellaswag |
|
metrics: |
|
- name: 0-shot |
|
type: accuracy |
|
value: 57.25 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 58.00 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 59.23 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 59.30 |
|
- name: 10-shot |
|
type: accuracy |
|
value: 59.56 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: OpenLLM-Ro/ro_gsm8k |
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type: OpenLLM-Ro/ro_gsm8k |
|
metrics: |
|
- name: 1-shot |
|
type: accuracy |
|
value: 36.47 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 45.94 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 50.11 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: LaRoSeDa_binary |
|
type: LaRoSeDa_binary |
|
metrics: |
|
- name: 0-shot |
|
type: macro-f1 |
|
value: 93.11 |
|
- name: 1-shot |
|
type: macro-f1 |
|
value: 96.06 |
|
- name: 3-shot |
|
type: macro-f1 |
|
value: 97.53 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 97.70 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: LaRoSeDa_multiclass |
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type: LaRoSeDa_multiclass |
|
metrics: |
|
- name: 0-shot |
|
type: macro-f1 |
|
value: 65.61 |
|
- name: 1-shot |
|
type: macro-f1 |
|
value: 55.73 |
|
- name: 3-shot |
|
type: macro-f1 |
|
value: 46.33 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 53.82 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: WMT_EN-RO |
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type: WMT_EN-RO |
|
metrics: |
|
- name: 0-shot |
|
type: bleu |
|
value: 6.89 |
|
- name: 1-shot |
|
type: bleu |
|
value: 26.62 |
|
- name: 3-shot |
|
type: bleu |
|
value: 25.70 |
|
- name: 5-shot |
|
type: bleu |
|
value: 25.94 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: WMT_RO-EN |
|
type: WMT_RO-EN |
|
metrics: |
|
- name: 0-shot |
|
type: bleu |
|
value: 2.16 |
|
- name: 1-shot |
|
type: bleu |
|
value: 16.65 |
|
- name: 3-shot |
|
type: bleu |
|
value: 33.41 |
|
- name: 5-shot |
|
type: bleu |
|
value: 35.22 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_EM |
|
type: XQuAD_EM |
|
metrics: |
|
- name: 0-shot |
|
type: exact_match |
|
value: 8.99 |
|
- name: 1-shot |
|
type: exact_match |
|
value: 35.88 |
|
- name: 3-shot |
|
type: exact_match |
|
value: 31.26 |
|
- name: 5-shot |
|
type: exact_match |
|
value: 10.17 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: XQuAD_F1 |
|
type: XQuAD_F1 |
|
metrics: |
|
- name: 0-shot |
|
type: f1 |
|
value: 20.00 |
|
- name: 1-shot |
|
type: f1 |
|
value: 59.41 |
|
- name: 3-shot |
|
type: f1 |
|
value: 48.41 |
|
- name: 5-shot |
|
type: f1 |
|
value: 18.33 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_Spearman |
|
type: STS_Spearman |
|
metrics: |
|
- name: 1-shot |
|
type: spearman |
|
value: 78.10 |
|
- name: 3-shot |
|
type: spearman |
|
value: 77.81 |
|
- name: 5-shot |
|
type: spearman |
|
value: 78.11 |
|
- task: |
|
type: text-generation |
|
dataset: |
|
name: STS_Pearson |
|
type: STS_Pearson |
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metrics: |
|
- name: 1-shot |
|
type: pearson |
|
value: 78.30 |
|
- name: 3-shot |
|
type: pearson |
|
value: 77.58 |
|
- name: 5-shot |
|
type: pearson |
|
value: 78.06 |
|
|
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--- |
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|
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# Model Card for Model ID |
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*Built with Meta Llama 3.1* |
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This model points/is identical to [RoLlama3.1-8b-Instruct-DPO-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2024-10-09). |
|
|
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
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RoLlama3.1 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned 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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|
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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:** [RoLlama3.1-8b-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09) |
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- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory |
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- **Paper:** https://arxiv.org/abs/2406.18266 |
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|
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## Intended Use |
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### Intended Use Cases |
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RoLlama3.1 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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|
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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/RoLlama3.1-8b-Instruct-DPO") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO") |
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instruction = "Ce jocuri de societate pot juca cu prietenii mei?" |
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chat = [ |
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{"role": "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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|
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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> |
|
<tr> |
|
<td>Llama-3.1-8B-Instruct</td><td><center>49.87</center></td><td><center>42.86</center></td><td><center>53.73</center></td><td><center>59.71</center></td><td><center>56.82</center></td><td><center>35.56</center></td><td><center><strong>50.54</strong></center></td> |
|
</tr> |
|
<tr> |
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<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center><strong>53.03</strong></center></td><td><center><strong>47.69</strong></center></td><td><center>54.57</center></td><td><center>65.84</center></td><td><center><strong>59.94</strong></center></td><td><center><strong>44.30</strong></center></td><td><center>45.82</center></td> |
|
</tr> |
|
<tr> |
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<td><em>RoLlama3.1-8b-Instruct-DPO-2024-10-09</em></td><td><center><em>52.74</em></center></td><td><center><em>44.84</em></center></td><td><center><em><strong>55.06</strong></em></center></td><td><center><em><strong>65.87</strong></em></center></td><td><center><em>58.67</em></center></td><td><center><em>44.17</em></center></td><td><center><em>47.82</em></center></td> |
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</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
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## Downstream tasks |
|
|
|
<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> |
|
<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> |
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<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
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<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
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<td><center><strong>RO-EN<br>(Bleu)</strong></center></td> |
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<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
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<td><center><strong>RO-EN<br>(Bleu)</strong></center> |
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</tr> |
|
<tr> |
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<td>Llama-3.1-8B-Instruct</td><td><center>95.74</center></td><td><center>59.49</center></td><td><center><strong>98.57</strong></center></td><td><center>82.41</center></td><td><center>19.01</center></td><td><center><strong>27.77</strong></center></td><td><center><strong>29.02</strong></center></td><td><center>39.80</center></td> |
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</tr> |
|
<tr> |
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>94.56</center></td><td><center><strong>60.10</strong></center></td><td><center>95.12</center></td><td><center><strong>87.53</strong></center></td><td><center><strong>21.88</strong></center></td><td><center>23.99</center></td><td><center>28.27</center></td><td><center><strong>40.44</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>96.10</strong></em></center></td><td><center><em>55.37</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>21.29</em></center></td><td><center><em>21.86</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
<table> |
|
<tbody> |
|
<tr> |
|
<td></td> |
|
<td colspan="4"><center><strong>XQuAD</strong></center></td> |
|
<td colspan="4"><center><strong>STS</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td></td> |
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><strong>Model</strong></td> |
|
<td><center><strong>(EM)</strong></center></td> |
|
<td><center><strong>(F1)</strong></center></td> |
|
<td><center><strong>(EM)</strong></center></td> |
|
<td><center><strong>(F1)</strong></center></td> |
|
<td><center><strong>(Spearman)</strong></center></td> |
|
<td><center><strong>(Pearson)</strong></center></td> |
|
<td><center><strong>(Spearman)</strong></center></td> |
|
<td><center><strong>(Pearson)</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>Llama-3.1-8B-Instruct</td><td><center><strong>44.96</strong></center></td><td><center><strong>64.45</strong></center></td><td><center><strong>69.50</strong></center></td><td><center><strong>84.31</strong></center></td><td><center>72.11</center></td><td><center>71.64</center></td><td><center>84.59</center></td><td><center>84.96</center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>13.59</center></td><td><center>23.56</center></td><td><center>49.41</center></td><td><center>62.93</center></td><td><center>75.89</center></td><td><center>76.00</center></td><td><center><strong>86.86</strong></center></td><td><center><strong>87.05</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2024-10-09</em></td><td><center><em>21.58</em></center></td><td><center><em>36.54</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>78.01</strong></em></center></td><td><center><em><strong>77.98</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
## MT-Bench |
|
|
|
<table> |
|
<tbody> |
|
<tr> |
|
<td><strong>Model</strong></td> |
|
<td><strong><center>Average</center></strong></td> |
|
<td><strong><center>1st turn</center></strong></td> |
|
<td><strong><center>2nd turn</center></strong></td> |
|
<td><strong><center>Answers in Ro</center></strong></td> |
|
</tr> |
|
<tr> |
|
<td>Llama-3.1-8B-Instruct</td><td><center>5.69</center></td><td><center>5.85</center></td><td><center>5.53</center></td><td><center><strong>160/160</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>5.42</center></td><td><center>5.95</center></td><td><center>4.89</center></td><td><center><strong>160/160</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>6.21</strong></em></center></td><td><center><em><strong>6.74</strong></em></center></td><td><center><em><strong>5.69</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
## RoCulturaBench |
|
|
|
<table> |
|
<tbody> |
|
<tr> |
|
<td><strong>Model</strong></td> |
|
<td><strong><center>Average</center></strong></td> |
|
<td><strong><center>Answers in Ro</center></strong></td> |
|
</tr> |
|
<tr> |
|
<td>Llama-3.1-8B-Instruct</td><td><center>3.54</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>3.55</center></td><td><center><strong>100/100</strong></center></td> |
|
</tr> |
|
<tr> |
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>4.42</strong></em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
## RoLlama3.1 Model Family |
|
|
|
| Model | Link | |
|
|--------------------|:--------:| |
|
|RoLlama3.1-8b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09) | |
|
|*RoLlama3.1-8b-Instruct-DPO-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-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:** |
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[More Information Needed] --> |
