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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/RoLlama3-8b-Instruct-2024-06-28 |
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datasets: |
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- OpenLLM-Ro/ro_sft_alpaca |
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- OpenLLM-Ro/ro_sft_alpaca_gpt4 |
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- OpenLLM-Ro/ro_sft_dolly |
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- OpenLLM-Ro/ro_sft_selfinstruct_gpt4 |
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- OpenLLM-Ro/ro_sft_norobots |
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- OpenLLM-Ro/ro_sft_orca |
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- OpenLLM-Ro/ro_sft_camel |
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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/RoLlama3-8b-Instruct-2024-06-28 |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: RoMT-Bench |
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type: RoMT-Bench |
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metrics: |
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- type: Score |
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value: 5.15 |
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name: Score |
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- type: Score |
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value: 6.03 |
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name: First turn |
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- type: Score |
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value: 4.28 |
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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: 3.71 |
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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: 50.56 |
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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: 44.7 |
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name: Average accuracy |
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- type: accuracy |
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value: 41.9 |
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name: 0-shot |
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- type: accuracy |
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value: 44.3 |
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name: 1-shot |
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- type: accuracy |
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value: 44.56 |
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name: 3-shot |
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- type: accuracy |
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value: 45.5 |
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name: 5-shot |
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- type: accuracy |
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value: 46.1 |
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name: 10-shot |
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- type: accuracy |
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value: 45.84 |
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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: 52.19 |
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name: Average accuracy |
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- type: accuracy |
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value: 50.85 |
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name: 0-shot |
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- type: accuracy |
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value: 51.24 |
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name: 1-shot |
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- type: accuracy |
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value: 53.3 |
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name: 3-shot |
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- type: accuracy |
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value: 53.39 |
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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: 67.23 |
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name: Average accuracy |
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- type: accuracy |
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value: 65.19 |
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name: 0-shot |
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- type: accuracy |
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value: 66.54 |
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name: 1-shot |
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- type: accuracy |
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value: 67.88 |
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name: 3-shot |
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- type: accuracy |
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value: 69.3 |
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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: 57.69 |
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name: Average accuracy |
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- type: accuracy |
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value: 56.12 |
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name: 0-shot |
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- type: accuracy |
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value: 57.37 |
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name: 1-shot |
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- type: accuracy |
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value: 57.92 |
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name: 3-shot |
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- type: accuracy |
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value: 58.18 |
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name: 5-shot |
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- type: accuracy |
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value: 58.85 |
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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: 30.23 |
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name: Average accuracy |
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- type: accuracy |
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value: 29.42 |
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name: 1-shot |
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- type: accuracy |
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value: 30.02 |
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name: 3-shot |
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- type: accuracy |
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value: 31.24 |
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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: 51.34 |
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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: 97.52 |
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name: Average macro-f1 |
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- type: macro-f1 |
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value: 97.43 |
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name: 0-shot |
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- type: macro-f1 |
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value: 96.6 |
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name: 1-shot |
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- type: macro-f1 |
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value: 97.9 |
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name: 3-shot |
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- type: macro-f1 |
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value: 98.13 |
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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: 67.41 |
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name: Average macro-f1 |
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- type: macro-f1 |
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value: 63.77 |
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name: 0-shot |
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- type: macro-f1 |
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value: 68.91 |
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name: 1-shot |
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- type: macro-f1 |
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value: 66.36 |
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name: 3-shot |
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- type: macro-f1 |
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value: 70.61 |
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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_binary_finetuned |
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type: LaRoSeDa_binary_finetuned |
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metrics: |
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- type: macro-f1 |
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value: 94.15 |
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name: Average macro-f1 |
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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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- type: macro-f1 |
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value: 87.13 |
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name: Average macro-f1 |
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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: 24.01 |
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name: Average bleu |
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- type: bleu |
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value: 6.92 |
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name: 0-shot |
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- type: bleu |
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value: 29.33 |
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name: 1-shot |
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- type: bleu |
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value: 29.79 |
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name: 3-shot |
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- type: bleu |
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value: 30.02 |
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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: 27.36 |
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name: Average bleu |
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- type: bleu |
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value: 4.5 |
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name: 0-shot |
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- type: bleu |
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value: 30.3 |
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name: 1-shot |
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- type: bleu |
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value: 36.96 |
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name: 3-shot |
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- type: bleu |
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value: 37.7 |
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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: WMT_EN-RO_finetuned |
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type: WMT_EN-RO_finetuned |
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metrics: |
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- type: bleu |
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value: 26.53 |
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name: Average bleu |
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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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- type: bleu |
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value: 40.36 |
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name: Average bleu |
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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: 39.43 |
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name: Average exact_match |
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- type: f1 |
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value: 59.5 |
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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: XQuAD_finetuned |
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type: XQuAD_finetuned |
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metrics: |
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- type: exact_match |
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value: 44.45 |
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name: Average exact_match |
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- type: f1 |
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value: 59.76 |
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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: 77.2 |
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name: Average spearman |
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- type: pearson |
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value: 77.87 |
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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: STS_finetuned |
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type: STS_finetuned |
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metrics: |
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- type: spearman |
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value: 85.8 |
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name: Average spearman |
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- type: pearson |
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value: 86.05 |
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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: 4.45 |
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name: 0-shot |
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- type: exact_match |
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value: 48.24 |
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name: 1-shot |
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- type: exact_match |
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value: 52.03 |
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name: 3-shot |
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- type: exact_match |
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value: 53.03 |
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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: 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: 26.08 |
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name: 0-shot |
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- type: f1 |
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value: 68.4 |
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name: 1-shot |
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- type: f1 |
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value: 71.92 |
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name: 3-shot |
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- type: f1 |
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value: 71.6 |
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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: 77.76 |
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name: 1-shot |
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- type: spearman |
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value: 76.72 |
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name: 3-shot |
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- type: spearman |
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value: 77.12 |
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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: 77.83 |
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name: 1-shot |
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- type: pearson |
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value: 77.64 |
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name: 3-shot |
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- type: pearson |
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value: 78.13 |
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name: 5-shot |
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--- |
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|
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# vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF |
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This model was converted to GGUF format from [`OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28`](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28) 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/RoLlama3-8b-Instruct-2024-06-28) for more details on the model. |
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|
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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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|
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### CLI: |
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```bash |
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llama-cli --hf-repo vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-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 vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-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 vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-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 vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-q8_0.gguf -c 2048 |
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``` |
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