初始化项目，由ModelHub XC社区提供模型
Model: OpenLLM-Ro/RoGemma-7b-Instruct Source: Original Platform
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README.md
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---
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license: cc-by-nc-4.0
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language:
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- ro
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base_model:
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- google/gemma-7b
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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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- OpenLLM-Ro/ro_sft_oasst
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- OpenLLM-Ro/ro_sft_ultrachat
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- OpenLLM-Ro/ro_sft_magpie_mt
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- OpenLLM-Ro/ro_sft_magpie_reasoning
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model-index:
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- name: OpenLLM-Ro/RoGemma-7b-Instruct-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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- name: Score
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type: Score
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value: 6.28
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- task:
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type: text-generation
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dataset:
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name: RoCulturaBench
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type: RoCulturaBench
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metrics:
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- name: Score
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type: Score
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value: 3.65
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- task:
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type: text-generation
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dataset:
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name: Romanian_Academic_Benchmarks
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type: Romanian_Academic_Benchmarks
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 50.52
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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: 47.70
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- task:
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type: text-generation
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dataset:
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name: OpenLLM-Ro/ro_mmlu
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type: OpenLLM-Ro/ro_mmlu
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 51.66
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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: 66.32
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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: 53.59
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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: 36.04
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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: 47.81
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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: 95.44
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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: 59.24
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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: 25.17
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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: 21.17
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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: 15.88
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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: 29.16
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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: 75.90
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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: 75.16
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- task:
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type: text-generation
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dataset:
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name: RoMT-Bench
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type: RoMT-Bench
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metrics:
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- name: First turn
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type: Score
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value: 6.97
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- name: Second turn
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type: Score
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value: 5.58
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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: 46.19
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- name: 1-shot
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type: accuracy
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value: 46.53
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- name: 3-shot
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type: accuracy
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value: 46.02
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- name: 5-shot
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type: accuracy
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value: 48.33
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- name: 10-shot
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type: accuracy
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value: 49.27
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- name: 25-shot
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type: accuracy
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value: 49.87
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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: 51.13
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- name: 1-shot
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type: accuracy
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value: 50.94
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- name: 3-shot
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type: accuracy
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value: 52.67
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- name: 5-shot
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type: accuracy
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value: 51.90
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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: 67.40
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- name: 1-shot
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type: accuracy
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value: 65.04
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- name: 3-shot
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type: accuracy
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value: 65.67
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- name: 5-shot
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type: accuracy
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value: 67.17
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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: 58.03
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- name: 1-shot
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type: accuracy
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value: 56.63
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- name: 3-shot
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type: accuracy
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value: 52.47
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- name: 5-shot
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type: accuracy
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value: 48.63
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- name: 10-shot
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type: accuracy
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value: 52.18
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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: 1-shot
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type: accuracy
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value: 24.11
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- name: 3-shot
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type: accuracy
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value: 37.76
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- name: 5-shot
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type: accuracy
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value: 46.25
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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: 96.33
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- name: 1-shot
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type: macro-f1
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value: 94.62
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- name: 3-shot
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type: macro-f1
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value: 95.06
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- name: 5-shot
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type: macro-f1
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value: 95.76
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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: 43.65
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- name: 1-shot
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type: macro-f1
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value: 64.30
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- name: 3-shot
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type: macro-f1
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value: 64.22
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- name: 5-shot
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type: macro-f1
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value: 64.81
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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: 13.30
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- name: 1-shot
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type: bleu
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value: 28.59
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- name: 3-shot
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type: bleu
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value: 29.48
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- name: 5-shot
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type: bleu
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value: 29.31
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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: 1.11
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- name: 1-shot
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type: bleu
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value: 18.97
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- name: 3-shot
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type: bleu
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value: 31.99
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- name: 5-shot
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type: bleu
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value: 32.60
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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: 17.31
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- name: 1-shot
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type: exact_match
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value: 12.44
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- name: 3-shot
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type: exact_match
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value: 13.11
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- name: 5-shot
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type: exact_match
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value: 20.67
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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: 29.90
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- name: 1-shot
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type: f1
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value: 24.24
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- name: 3-shot
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type: f1
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value: 25.64
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- name: 5-shot
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type: f1
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value: 36.86
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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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- name: 1-shot
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type: spearman
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value: 76.50
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- name: 3-shot
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type: spearman
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value: 73.63
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- name: 5-shot
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type: spearman
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value: 77.58
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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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- name: 1-shot
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type: pearson
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value: 75.15
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- name: 3-shot
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type: pearson
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value: 72.69
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- name: 5-shot
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type: pearson
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value: 77.63
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---
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# Model Card for Model ID
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This model points/is identical to [RoGemma-7b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2025-04-23).
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<!-- Provide a quick summary of what the model is/does. -->
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RoGemma is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 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-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
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- **Developed by:** OpenLLM-Ro
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<!-- - **Funded by [optional]:** [More Information Needed] -->
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<!-- - **Shared by [optional]:** [More Information Needed] -->
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<!-- - **Model type:** [More Information Needed] -->
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- **Language(s):** Romanian
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- **License:** cc-by-nc-4.0
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- **Finetuned from model:** [gemma-7b](https://huggingface.co/google/gemma-7b)
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- **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_ultrachat), [RoMagpiePro](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_mt), [RoMagpieReasoning](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_reasoning)
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
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- **Paper:** https://arxiv.org/abs/2406.18266
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## Intended Use
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### Intended Use Cases
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RoGemma 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/RoGemma-7b-Instruct")
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma-7b-Instruct")
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instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
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chat = [
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{"role": "user", "content": instruction},
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]
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
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inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
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outputs = model.generate(input_ids=inputs, max_new_tokens=128)
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print(tokenizer.decode(outputs[0]))
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```
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## Academic Benchmarks
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||||
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<table>
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<tbody>
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<tr>
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<td><strong>Model</strong></td>
|
||||
<td><strong><center>Average</center></strong></td>
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<td><strong><center>ARC</center></strong></td>
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<td><strong><center>MMLU</center></strong></td>
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||||
<td><strong><center>Winogrande</center></strong></td>
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<td><strong><center>Hellaswag</center></strong></td>
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<td><strong><center>GSM8k</center></strong></td>
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<td><strong><center>TruthfulQA</center></strong></td>
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</tr>
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<tr>
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<td>gemma-1.1-7b-it</td><td><center>41.44</center></td><td><center>40.32</center></td><td><center>47.22</center></td><td><center>55.01</center></td><td><center>47.03</center></td><td><center>9.50</center></td><td><center>49.58</center></td>
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</tr>
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<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>53.41</strong></center></td><td><center><strong>52.44</strong></center></td><td><center>54.44</center></td><td><center><strong>69.36</strong></center></td><td><center><strong>61.96</strong></center></td><td><center>31.06</center></td><td><center><strong>51.23</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>50.48</center></td><td><center>52.01</center></td><td><center>52.37</center></td><td><center>66.97</center></td><td><center>56.34</center></td><td><center>25.98</center></td><td><center>49.18</center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>50.52</em></center></td><td><center><em>47.70</em></center></td><td><center><em>51.66</em></center></td><td><center><em>66.32</em></center></td><td><center><em>53.59</em></center></td><td><center><em><strong>36.04</strong></em></center></td><td><center><em>47.81</em></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>48.27</center></td><td><center>46.66</center></td><td><center><strong>54.45</strong></center></td><td><center>63.73</center></td><td><center>49.33</center></td><td><center>34.98</center></td><td><center>40.45</center></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
## Downstream tasks
|
||||
|
||||
<table>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
|
||||
<td colspan="4"><center><strong>WMT</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
||||
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
||||
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
||||
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Model</strong></td>
|
||||
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
|
||||
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
|
||||
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
|
||||
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
|
||||
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
|
||||
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
|
||||
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
|
||||
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>gemma-1.1-7b-it</td><td><center>87.54</center></td><td><center>51.48</center></td><td><center>83.87</center></td><td><center>85.61</center></td><td><center>17.96</center></td><td><center><strong>27.74</strong></center></td><td><center>25.48</center></td><td><center>36.11</center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>97.86</strong></center></td><td><center><strong>65.70</strong></center></td><td><center>98.43</center></td><td><center><strong>87.17</strong></center></td><td><center><strong>27.91</strong></center></td><td><center>23.08</center></td><td><center><strong>27.99</strong></center></td><td><center><strong>39.51</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>86.96</center></td><td><center>56.72</center></td><td><center><strong>98.80</strong></center></td><td><center>85.81</center></td><td><center>24.45</center></td><td><center>14.20</center></td><td><center>25.96</center></td><td><center>39.07</center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>95.44</em></center></td><td><center><em>59.24</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>25.17</em></center></td><td><center><em>21.17</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>96.45</center></td><td><center>63.23</center></td><td><center>-</center></td><td><center>-</center></td><td><center>20.73</center></td><td><center>7.87</center></td><td><center>-</center></td><td><center>-</center></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
|
||||
<table>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td colspan="4"><center><strong>XQuAD</strong></center></td>
|
||||
<td colspan="4"><center><strong>STS</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
||||
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
||||
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
||||
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Model</strong></td>
|
||||
<td><center><strong>(EM)</strong></center></td>
|
||||
<td><center><strong>(F1)</strong></center></td>
|
||||
<td><center><strong>(EM)</strong></center></td>
|
||||
<td><center><strong>(F1)</strong></center></td>
|
||||
<td><center><strong>(Spearman)</strong></center></td>
|
||||
<td><center><strong>(Pearson)</strong></center></td>
|
||||
<td><center><strong>(Spearman)</strong></center></td>
|
||||
<td><center><strong>(Pearson)</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>gemma-1.1-7b-it</td><td><center><strong>42.10</strong></center></td><td><center><strong>62.30</strong></center></td><td><center><strong>60.34</strong></center></td><td><center><strong>77.40</strong></center></td><td><center>49.10</center></td><td><center>50.23</center></td><td><center>83.43</center></td><td><center>83.64</center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>17.75</center></td><td><center>28.11</center></td><td><center>52.02</center></td><td><center>68.43</center></td><td><center>73.96</center></td><td><center><strong>75.16</strong></center></td><td><center>86.45</center></td><td><center>86.31</center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>26.03</center></td><td><center>41.58</center></td><td><center>46.72</center></td><td><center>60.79</center></td><td><center>73.23</center></td><td><center>71.58</center></td><td><center><strong>88.42</strong></center></td><td><center><strong>88.45</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>15.88</em></center></td><td><center><em>29.16</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>75.90</strong></em></center></td><td><center><em><strong>75.16</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>19.14</center></td><td><center>38.10</center></td><td><center>-</center></td><td><center>-</center></td><td><center>69.38</center></td><td><center>69.34</center></td><td><center>-</center></td><td><center>-</center></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
|
||||
## MT-Bench
|
||||
|
||||
<table>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td><strong>Model</strong></td>
|
||||
<td><strong><center>Average</center></strong></td>
|
||||
<td><strong><center>1st turn</center></strong></td>
|
||||
<td><strong><center>2nd turn</center></strong></td>
|
||||
<td><strong><center>Answers in Ro</center></strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>gemma-1.1-7b-it</td><td><center>4.83</center></td><td><center>5.11</center></td><td><center>4.55</center></td><td><center><strong>160/160</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>5.26</center></td><td><center>5.92</center></td><td><center>4.60</center></td><td><center><strong>160/160</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>5.24</center></td><td><center>5.55</center></td><td><center>4.94</center></td><td><center><strong>160/160</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em><strong>6.28</strong></em></center></td><td><center><em><strong>6.97</strong></em></center></td><td><center><em><strong>5.58</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>5.47</center></td><td><center>5.92</center></td><td><center>5.03</center></td><td><center><strong>160/160</strong></center></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
## RoCulturaBench
|
||||
|
||||
<table>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td><strong>Model</strong></td>
|
||||
<td><strong><center>Average</center></strong></td>
|
||||
<td><strong><center>Answers in Ro</center></strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>gemma-1.1-7b-it</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>3.26</center></td><td><center><strong>100/100</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>3.51</center></td><td><center><strong>100/100</strong></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>3.65</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center><strong>3.94</strong></center></td><td><center><strong>100/100</strong></center></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
## RoGemma Model Family
|
||||
|
||||
| Model | Link |
|
||||
|--------------------|:--------:|
|
||||
|RoGemma-7b-Instruct-2024-06-28| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-06-28) |
|
||||
|RoGemma-7b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09) |
|
||||
|*RoGemma-7b-Instruct-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2025-04-23) |
|
||||
|RoGemma-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09) |
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
```
|
||||
@inproceedings{masala-etal-2024-vorbesti,
|
||||
title = "``Vorbe\c{s}ti Rom{\^a}ne\c{s}te?'' A Recipe to Train Powerful {R}omanian {LLM}s with {E}nglish Instructions",
|
||||
author = "Masala, Mihai and Ilie-Ablachim, Denis and Dima, Alexandru and Corlatescu, Dragos Georgian and Zavelca, Miruna-Andreea and Olaru, Ovio and Terian, Simina-Maria and Terian, Andrei and Leordeanu, Marius and Velicu, Horia and Popescu, Marius and Dascalu, Mihai and Rebedea, Traian",
|
||||
editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung",
|
||||
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
|
||||
month = nov,
|
||||
year = "2024",
|
||||
address = "Miami, Florida, USA",
|
||||
publisher = "Association for Computational Linguistics",
|
||||
url = "https://aclanthology.org/2024.findings-emnlp.681/",
|
||||
doi = "10.18653/v1/2024.findings-emnlp.681",
|
||||
pages = "11632--11647"
|
||||
}
|
||||
```
|
||||
<!-- **APA:**
|
||||
|
||||
[More Information Needed] -->
|
||||
Reference in New Issue
Block a user