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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: OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-04-23 |
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datasets: |
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- OpenLLM-Ro/ro_dpo_helpsteer |
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- OpenLLM-Ro/ro_dpo_ultrafeedback |
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- OpenLLM-Ro/ro_dpo_magpie |
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- OpenLLM-Ro/ro_dpo_argilla_magpie |
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- OpenLLM-Ro/ro_dpo_helpsteer2 |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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model-index: |
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- name: OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-04-23 |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: RoMT-Bench |
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type: RoMT-Bench |
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metrics: |
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- type: Score |
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value: 7.26 |
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name: Score |
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- type: Score |
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value: 7.65 |
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name: First turn |
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- type: Score |
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value: 6.86 |
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name: Second turn |
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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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- type: Score |
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value: 5.36 |
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name: Score |
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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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- type: accuracy |
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value: 59.79 |
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name: Average accuracy |
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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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- type: accuracy |
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value: 55.66 |
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name: Average accuracy |
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- type: accuracy |
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value: 52.44 |
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name: 0-shot |
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- type: accuracy |
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value: 55.7 |
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name: 1-shot |
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- type: accuracy |
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value: 56.47 |
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name: 3-shot |
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- type: accuracy |
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value: 55.7 |
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name: 5-shot |
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- type: accuracy |
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value: 57.16 |
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name: 10-shot |
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- type: accuracy |
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value: 56.47 |
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name: 25-shot |
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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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- type: accuracy |
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value: 64.0 |
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name: Average accuracy |
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- type: accuracy |
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value: 65.2 |
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name: 0-shot |
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- type: accuracy |
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value: 63.27 |
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name: 1-shot |
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- type: accuracy |
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value: 63.83 |
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name: 3-shot |
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- type: accuracy |
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value: 63.69 |
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name: 5-shot |
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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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- type: accuracy |
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value: 73.16 |
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name: Average accuracy |
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- type: accuracy |
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value: 74.11 |
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name: 0-shot |
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- type: accuracy |
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value: 72.53 |
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name: 1-shot |
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- type: accuracy |
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value: 72.93 |
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name: 3-shot |
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- type: accuracy |
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value: 73.09 |
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name: 5-shot |
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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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- type: accuracy |
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value: 64.26 |
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name: Average accuracy |
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- type: accuracy |
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value: 65.9 |
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name: 0-shot |
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- type: accuracy |
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value: 66.06 |
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name: 1-shot |
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- type: accuracy |
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value: 62.36 |
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name: 3-shot |
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- type: accuracy |
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value: 61.87 |
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name: 5-shot |
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- type: accuracy |
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value: 65.11 |
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name: 10-shot |
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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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- type: accuracy |
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value: 37.8 |
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name: Average accuracy |
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- type: accuracy |
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value: 16.83 |
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name: 1-shot |
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- type: accuracy |
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value: 43.21 |
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name: 3-shot |
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- type: accuracy |
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value: 53.37 |
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name: 5-shot |
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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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- type: accuracy |
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value: 63.86 |
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name: Average accuracy |
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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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- type: macro-f1 |
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value: 82.84 |
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name: Average macro-f1 |
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- type: macro-f1 |
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value: 39.18 |
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name: 0-shot |
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- type: macro-f1 |
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value: 96.59 |
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name: 1-shot |
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- type: macro-f1 |
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value: 97.63 |
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name: 3-shot |
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- type: macro-f1 |
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value: 97.97 |
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name: 5-shot |
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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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- type: macro-f1 |
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value: 65.95 |
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name: Average macro-f1 |
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- type: macro-f1 |
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value: 58.94 |
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name: 0-shot |
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- type: macro-f1 |
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value: 64.99 |
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name: 1-shot |
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- type: macro-f1 |
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value: 68.86 |
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name: 3-shot |
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- type: macro-f1 |
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value: 71.03 |
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name: 5-shot |
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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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- type: bleu |
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value: 28.16 |
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name: Average bleu |
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- type: bleu |
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value: 26.89 |
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name: 0-shot |
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- type: bleu |
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value: 31.18 |
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name: 1-shot |
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- type: bleu |
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value: 30.65 |
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name: 3-shot |
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- type: bleu |
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value: 23.91 |
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name: 5-shot |
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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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- type: bleu |
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value: 19.34 |
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name: Average bleu |
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- type: bleu |
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value: 2.98 |
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name: 0-shot |
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- type: bleu |
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value: 20.3 |
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name: 1-shot |
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- type: bleu |
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value: 30.08 |
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name: 3-shot |
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- type: bleu |
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value: 24.01 |
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name: 5-shot |
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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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- type: exact_match |
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value: 30.82 |
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name: Average exact_match |
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- type: f1 |
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value: 48.53 |
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name: Average f1 |
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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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- type: spearman |
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value: 73.24 |
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name: Average spearman |
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- type: pearson |
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value: 73.13 |
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name: Average pearson |
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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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- type: exact_match |
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value: 26.39 |
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name: 0-shot |
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- type: exact_match |
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value: 23.87 |
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name: 1-shot |
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- type: exact_match |
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value: 34.03 |
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name: 3-shot |
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- type: exact_match |
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value: 38.99 |
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name: 5-shot |
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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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- type: f1 |
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value: 43.28 |
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name: 0-shot |
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- type: f1 |
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value: 37.38 |
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name: 1-shot |
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- type: f1 |
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value: 54.08 |
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name: 3-shot |
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- type: f1 |
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value: 59.38 |
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name: 5-shot |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_Spearman |
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type: STS_Spearman |
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metrics: |
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- type: spearman |
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value: 73.46 |
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name: 1-shot |
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- type: spearman |
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value: 73.55 |
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name: 3-shot |
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- type: spearman |
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value: 72.7 |
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name: 5-shot |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_Pearson |
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type: STS_Pearson |
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metrics: |
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- type: pearson |
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value: 74.87 |
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name: 1-shot |
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- type: pearson |
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value: 72.96 |
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name: 3-shot |
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- type: pearson |
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value: 71.55 |
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name: 5-shot |
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--- |
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# LuuNgoc2k2/RoGemma2-9b-Instruct-DPO-2025-04-23-Q8_0-GGUF |
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This model was converted to GGUF format from [`OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-04-23`](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-04-23) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-04-23) for more details on the model. |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo LuuNgoc2k2/RoGemma2-9b-Instruct-DPO-2025-04-23-Q8_0-GGUF --hf-file rogemma2-9b-instruct-dpo-2025-04-23-q8_0.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo LuuNgoc2k2/RoGemma2-9b-Instruct-DPO-2025-04-23-Q8_0-GGUF --hf-file rogemma2-9b-instruct-dpo-2025-04-23-q8_0.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo LuuNgoc2k2/RoGemma2-9b-Instruct-DPO-2025-04-23-Q8_0-GGUF --hf-file rogemma2-9b-instruct-dpo-2025-04-23-q8_0.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo LuuNgoc2k2/RoGemma2-9b-Instruct-DPO-2025-04-23-Q8_0-GGUF --hf-file rogemma2-9b-instruct-dpo-2025-04-23-q8_0.gguf -c 2048 |
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``` |
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