RoGemma2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the human aligned instruct 9B model. Links to other models can be found at the bottom of this page.

Model Details

Model Description

This model points/is identical to RoGemma2-9b-Instruct-DPO-2025-04-23.

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.

Developed by: OpenLLM-Ro

Language(s): Romanian
License: cc-by-nc-4.0
Finetuned from model: RoGemma2-9b-Instruct-2025-04-23
Trained using: RoHelpSteer, RoUltraFeedback, RoMagpieDPO, RoArgillaMagpie, RoHelpSteer2

Model Sources

Repository: https://github.com/OpenLLM-Ro/LLaMA-Factory
Paper: https://arxiv.org/abs/2406.18266

Intended Use

Intended Use Cases

RoGemma2 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.

Out-of-Scope Use

Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.

How to Get Started with the Model

Use the code below to get started with the model.

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-DPO")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-DPO")

instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
chat = [
        {"role": "user", "content": instruction},
        ]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")

inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))

Academic Benchmarks

Model	Average	ARC	MMLU	Winogrande	Hellaswag	GSM8k	TruthfulQA
gemma-2-9b-it	56.22	50.33	64.01	64.88	63.11	41.95	53.03
RoGemma2-9b-Instruct-2024-10-09	57.06	56.20	62.98	71.00	60.52	37.86	53.77
RoGemma2-9b-Instruct-2025-04-23	54.39	50.24	62.00	70.38	52.25	40.51	50.97
RoGemma2-9b-Instruct-DPO-2024-10-09	59.08	54.10	63.41	70.02	59.35	57.24	50.39
RoGemma2-9b-Instruct-DPO-2025-04-23	59.79	55.66	64.00	73.16	64.26	37.80	63.86

Downstream tasks

	LaRoSeDa	WMT
	Few-shot	Finetuned	Few-shot	Finetuned
Model	Binary (Macro F1)	Multiclass (Macro F1)	Binary (Macro F1)	Multiclass (Macro F1)	EN-RO (Bleu)	RO-EN (Bleu)	EN-RO (Bleu)	RO-EN (Bleu)
gemma-2-9b-it	90.82	52.51	98.97	86.02	19.97	28.94	27.94	41.61
RoGemma2-9b-Instruct-2024-10-09	96.19	62.49	98.93	88.33	25.74	23.16	28.43	40.94
RoGemma2-9b-Instruct-2025-04-23	84.23	60.14	-	-	17.78	18.24	-	-
RoGemma2-9b-Instruct-DPO-2024-10-09	97.74	67.40	-	-	27.32	15.96	-	-
RoGemma2-9b-Instruct-DPO-2025-04-23	82.84	65.95	-	-	28.16	19.34	-	-

	XQuAD	STS
	Few-shot	Finetuned	Few-shot	Finetuned
Model	(EM)	(F1)	(EM)	(F1)	(Spearman)	(Pearson)	(Spearman)	(Pearson)
gemma-2-9b-it	37.56	57.48	71.09	84.78	71.39	71.73	89.07	89.29
RoGemma2-9b-Instruct-2024-10-09	51.37	70.74	50.00	64.10	77.15	77.10	89.45	89.89
RoGemma2-9b-Instruct-2025-04-23	49.22	66.33	-	-	70.17	70.80	-	-
RoGemma2-9b-Instruct-DPO-2024-10-09	32.42	58.68	-	-	80.82	81.50	-	-
RoGemma2-9b-Instruct-DPO-2025-04-23	30.82	48.53	-	-	73.24	73.13	-	-

MT-Bench

Model	Average	1st turn	2nd turn	Answers in Ro
gemma-2-9b-it	7.50	7.91	7.09	159/160
RoGemma2-9b-Instruct-2024-10-09	6.08	6.78	5.39	160/160
RoGemma2-9b-Instruct-2025-04-23	6.78	7.00	6.55	160/160
RoGemma2-9b-Instruct-DPO-2024-10-09	6.77	7.24	6.30	160/160
RoGemma2-9b-Instruct-DPO-2025-04-23	7.26	7.65	6.86	160/160

RoCulturaBench

Model	Average	Answers in Ro
gemma-2-9b-it	5.68	100/100
RoGemma2-9b-Instruct-2024-10-09	4.20	100/100
RoGemma2-9b-Instruct-2025-04-23	4.89	100/100
RoGemma2-9b-Instruct-DPO-2024-10-09	4.83	100/100
RoGemma2-9b-Instruct-DPO-2025-04-23	5.36	100/100

RoGemma2 Model Family

Model	Link
RoGemma2-9b-Instruct-2024-10-09	link
RoGemma2-9b-Instruct-2025-04-23	link
RoGemma2-9b-Instruct-DPO-2024-10-09	link
RoGemma2-9b-Instruct-DPO-2025-04-23	link

Citation

@misc{masala2024vorbecstiromanecsterecipetrain,
      title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, 
      author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
      year={2024},
      eprint={2406.18266},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.18266}, 
}