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--- |
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license: llama2 |
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language: |
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- ro |
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base_model: meta-llama/Llama-2-7b-hf |
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model-index: |
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- name: OpenLLM-Ro/RoLlama2-7b-Base |
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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: 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: 38.03 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_arc_challenge |
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type: OpenLLM-Ro/ro_arc_challenge |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 37.95 |
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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: 27.22 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_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: 59.29 |
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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: 57.22 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_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: 2.53 |
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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: 44.00 |
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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: 83.25 |
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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: 61.04 |
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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: 98.97 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass_finetuned |
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type: LaRoSeDa_multiclass_finetuned |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 87.72 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO |
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type: WMT_EN-RO |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 10.01 |
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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: 13.03 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO_finetuned |
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type: WMT_EN-RO_finetuned |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 27.85 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN_finetuned |
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type: WMT_RO-EN_finetuned |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 39.30 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD |
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type: XQuAD |
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metrics: |
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- name: Average exact_match |
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type: exact_match |
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value: 30.15 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD |
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type: XQuAD |
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metrics: |
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- name: Average f1 |
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type: f1 |
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value: 47.03 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_finetuned |
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type: XQuAD_finetuned |
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metrics: |
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- name: Average exact_match |
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type: exact_match |
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value: 67.06 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_finetuned |
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type: XQuAD_finetuned |
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metrics: |
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- name: Average f1 |
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type: f1 |
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value: 79.96 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: Average spearman |
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type: spearman |
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value: 7.89 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: Average pearson |
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type: pearson |
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value: 7.98 |
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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: |
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- name: Average spearman |
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type: spearman |
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value: 71.75 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_finetuned |
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type: STS_finetuned |
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metrics: |
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- name: Average pearson |
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type: pearson |
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value: 71.99 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_arc_challenge |
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type: OpenLLM-Ro/ro_arc_challenge |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 35.56 |
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- name: 1-shot |
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type: accuracy |
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value: 36.42 |
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- name: 3-shot |
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type: accuracy |
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value: 38.56 |
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- name: 5-shot |
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type: accuracy |
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value: 38.39 |
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- name: 10-shot |
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type: accuracy |
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value: 39.07 |
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- name: 25-shot |
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type: accuracy |
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value: 39.67 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_mmlu |
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type: OpenLLM-Ro/ro_mmlu |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 25.82 |
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- name: 1-shot |
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type: accuracy |
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value: 25.48 |
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- name: 3-shot |
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type: accuracy |
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value: 27.61 |
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- name: 5-shot |
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type: accuracy |
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value: 29.96 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_winogrande |
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type: OpenLLM-Ro/ro_winogrande |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 58.72 |
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- name: 1-shot |
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type: accuracy |
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value: 58.88 |
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- name: 3-shot |
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type: accuracy |
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value: 60.38 |
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- name: 5-shot |
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type: accuracy |
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value: 59.19 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_hellaswag |
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type: OpenLLM-Ro/ro_hellaswag |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 55.85 |
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- name: 1-shot |
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type: accuracy |
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value: 57.06 |
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- name: 3-shot |
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type: accuracy |
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value: 57.52 |
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- name: 5-shot |
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type: accuracy |
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value: 57.89 |
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- name: 10-shot |
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type: accuracy |
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value: 57.79 |
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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: 0-shot |
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type: accuracy |
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value: 0.00 |
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- name: 1-shot |
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type: accuracy |
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value: 2.96 |
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- name: 3-shot |
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type: accuracy |
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value: 4.62 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
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- name: 0-shot |
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type: macro-f1 |
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value: 42.78 |
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- name: 1-shot |
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type: macro-f1 |
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value: 98.00 |
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- name: 3-shot |
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type: macro-f1 |
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value: 95.13 |
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- name: 5-shot |
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type: macro-f1 |
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value: 97.07 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass |
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type: LaRoSeDa_multiclass |
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metrics: |
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- name: 0-shot |
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type: macro-f1 |
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value: 46.41 |
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- name: 1-shot |
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type: macro-f1 |
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value: 67.36 |
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- name: 3-shot |
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type: macro-f1 |
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value: 65.16 |
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- name: 5-shot |
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type: macro-f1 |
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value: 65.23 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO |
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type: WMT_EN-RO |
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metrics: |
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- name: 0-shot |
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type: bleu |
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value: 4.45 |
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- name: 1-shot |
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type: bleu |
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value: 8.61 |
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- name: 3-shot |
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type: bleu |
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value: 12.25 |
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- name: 5-shot |
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type: bleu |
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value: 14.73 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN |
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type: WMT_RO-EN |
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metrics: |
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- name: 0-shot |
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type: bleu |
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value: 1.29 |
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- name: 1-shot |
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type: bleu |
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value: 10.78 |
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- name: 3-shot |
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type: bleu |
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value: 16.82 |
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- name: 5-shot |
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type: bleu |
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value: 23.24 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_EM |
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type: XQuAD_EM |
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metrics: |
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- name: 0-shot |
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type: exact_match |
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value: 5.29 |
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- name: 1-shot |
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type: exact_match |
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value: 33.95 |
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- name: 3-shot |
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type: exact_match |
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value: 39.24 |
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- name: 5-shot |
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type: exact_match |
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value: 42.10 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_F1 |
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type: XQuAD_F1 |
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metrics: |
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- name: 0-shot |
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type: f1 |
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value: 16.17 |
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- name: 1-shot |
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type: f1 |
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value: 51.84 |
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- name: 3-shot |
|
type: f1 |
|
value: 58.82 |
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- name: 5-shot |
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type: f1 |
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value: 61.29 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: 0-shot |
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type: spearman |
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value: -1.74 |
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- name: 1-shot |
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type: spearman |
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value: 15.47 |
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- name: 3-shot |
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type: spearman |
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value: 9.93 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: 0-shot |
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type: pearson |
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value: -1.40 |
|
- name: 1-shot |
|
type: pearson |
|
value: 15.00 |
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- name: 3-shot |
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type: pearson |
|
value: 10.33 |
|
|
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--- |
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|
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **foundational 7B model**. Links to other models can be found at the bottom of this page. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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OpenLLM represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants. |
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- **Developed by:** OpenLLM-Ro |
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<!-- - **Funded by [optional]:** [More Information Needed] --> |
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<!-- - **Shared by [optional]:** [More Information Needed] --> |
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<!-- - **Model type:** [More Information Needed] --> |
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- **Language(s):** Romanian |
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- **License:** Llama2 Community License Agreement |
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- **Continual pretrained from model:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) |
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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-recipes |
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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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RoLlama2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base") |
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input_text = "Mihai Eminescu a fost " |
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input_ids = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**input_ids, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Academic Benchmarks |
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| Model | Average | ARC | MMLU |Winogrande|HellaSwag | GSM8k |TruthfulQA| |
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|--------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
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| Llama-2-7b | 37.04 | 36.05 | **33.66** | 57.56 | 48.00 | **4.75** | 42.22 | |
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| *RoLlama2-7b-Base* | ***38.03*** | ***37.95*** | *27.22* | ***59.29*** | ***57.22*** | *2.53* | ***44.00*** | |
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<!-- ## Downstream Tasks |
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| Model | Sentiment Analysis | ARC | MMLU |Winogrande|HellaSwag | GSM8k |TruthfulQA| |
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|--------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
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| Llama-2-7b | 37.04 | 36.05 | **33.66** | 57.56 | 48.00 | **4.75** | 42.22 | |
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| *RoLlama2-7b-Base* | ***38.03*** | ***37.95*** | *27.22* | ***59.29*** | ***57.22*** | *2.53* | ***44.00*** | |
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--> |
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## RoLlama2 Model Family |
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| Model | Link | |
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|--------------------|:--------:| |
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|*RoLlama2-7b-Base* | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base) | |
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|RoLlama2-7b-Instruct| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct) | |
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|RoLlama2-7b-Chat | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Chat) | |
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## Citation |
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``` |
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@misc{masala2024vorbecstiromanecsterecipetrain, |
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title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, |
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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}, |
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year={2024}, |
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eprint={2406.18266}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2406.18266}, |
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} |
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``` |
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<!-- **APA:** |
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[More Information Needed] --> |
