license, language, base_model, datasets, model-index

license	language	base_model	datasets	model-index
cc-by-nc-4.0	ro	OpenLLM-Ro/RoLlama2-7b-Base	OpenLLM-Ro/ro_sft_alpaca OpenLLM-Ro/ro_sft_alpaca_gpt4 OpenLLM-Ro/ro_sft_dolly OpenLLM-Ro/ro_sft_selfinstruct_gpt4 OpenLLM-Ro/ro_sft_norobots OpenLLM-Ro/ro_sft_orca OpenLLM-Ro/ro_sft_camel OpenLLM-Ro/ro_sft_oasst OpenLLM-Ro/ro_sft_ultrachat OpenLLM-Ro/ro_sft_magpie_mt OpenLLM-Ro/ro_sft_magpie_reasoning	name results OpenLLM-Ro/RoLlama2-7b-Instruct-2025-04-23 task dataset metrics type text-generation name type RoMT-Bench RoMT-Bench name type value Score Score 4.97 task dataset metrics type text-generation name type RoCulturaBench RoCulturaBench name type value Score Score 4.56 task dataset metrics type text-generation name type Romanian_Academic_Benchmarks Romanian_Academic_Benchmarks name type value Average accuracy accuracy 45.51 task dataset metrics type text-generation name type OpenLLM-Ro/ro_arc_challenge OpenLLM-Ro/ro_arc_challenge name type value Average accuracy accuracy 45.7 task dataset metrics type text-generation name type OpenLLM-Ro/ro_mmlu OpenLLM-Ro/ro_mmlu name type value Average accuracy accuracy 40.36 task dataset metrics type text-generation name type OpenLLM-Ro/ro_winogrande OpenLLM-Ro/ro_winogrande name type value Average accuracy accuracy 63.26 task dataset metrics type text-generation name type OpenLLM-Ro/ro_hellaswag OpenLLM-Ro/ro_hellaswag name type value Average accuracy accuracy 60.25 task dataset metrics type text-generation name type OpenLLM-Ro/ro_gsm8k OpenLLM-Ro/ro_gsm8k name type value Average accuracy accuracy 18.02 task dataset metrics type text-generation name type OpenLLM-Ro/ro_truthfulqa OpenLLM-Ro/ro_truthfulqa name type value Average accuracy accuracy 45.48 task dataset metrics type text-generation name type LaRoSeDa_binary LaRoSeDa_binary name type value Average macro-f1 macro-f1 97.6 task dataset metrics type text-generation name type LaRoSeDa_multiclass LaRoSeDa_multiclass name type value Average macro-f1 macro-f1 60.22 task dataset metrics type text-generation name type WMT_EN-RO WMT_EN-RO name type value Average bleu bleu 27.21 task dataset metrics type text-generation name type WMT_RO-EN WMT_RO-EN name type value Average bleu bleu 22.15 task dataset metrics type text-generation name type XQuAD XQuAD name type value Average exact_match exact_match 47.39 task dataset metrics type text-generation name type XQuAD XQuAD name type value Average f1 f1 65.77 task dataset metrics type text-generation name type STS STS name type value Average spearman spearman 59.05 task dataset metrics type text-generation name type STS STS name type value Average pearson pearson 56.45 task dataset metrics type text-generation name type RoMT-Bench RoMT-Bench name type value First turn Score 5.56 name type value Second turn Score 4.39 task dataset metrics type text-generation name type OpenLLM-Ro/ro_arc_challenge OpenLLM-Ro/ro_arc_challenge name type value 0-shot accuracy 43.02 name type value 1-shot accuracy 45.84 name type value 3-shot accuracy 45.24 name type value 5-shot accuracy 46.19 name type value 10-shot accuracy 46.7 name type value 25-shot accuracy 47.22 task dataset metrics type text-generation name type OpenLLM-Ro/ro_mmlu OpenLLM-Ro/ro_mmlu name type value 0-shot accuracy 38.64 name type value 1-shot accuracy 40.77 name type value 3-shot accuracy 41.19 name type value 5-shot accuracy 40.86 task dataset metrics type text-generation name type OpenLLM-Ro/ro_winogrande OpenLLM-Ro/ro_winogrande name type value 0-shot accuracy 63.61 name type value 1-shot accuracy 62.75 name type value 3-shot accuracy 63.46 name type value 5-shot accuracy 63.22 task dataset metrics type text-generation name type OpenLLM-Ro/ro_hellaswag OpenLLM-Ro/ro_hellaswag name type value 0-shot accuracy 59.79 name type value 1-shot accuracy 59.62 name type value 3-shot accuracy 60.12 name type value 5-shot accuracy 60.71 name type value 10-shot accuracy 61.01 task dataset metrics type text-generation name type OpenLLM-Ro/ro_gsm8k OpenLLM-Ro/ro_gsm8k name type value 1-shot accuracy 6.14 name type value 3-shot accuracy 22.52 name type value 5-shot accuracy 25.4 task dataset metrics type text-generation name type LaRoSeDa_binary LaRoSeDa_binary name type value 0-shot macro-f1 98.17 name type value 1-shot macro-f1 96.3 name type value 3-shot macro-f1 97.8 name type value 5-shot macro-f1 98.13 task dataset metrics type text-generation name type LaRoSeDa_multiclass LaRoSeDa_multiclass name type value 0-shot macro-f1 49.8 name type value 1-shot macro-f1 56.03 name type value 3-shot macro-f1 65.33 name type value 5-shot macro-f1 69.7 task dataset metrics type text-generation name type WMT_EN-RO WMT_EN-RO name type value 0-shot bleu 19.34 name type value 1-shot bleu 29.89 name type value 3-shot bleu 29.99 name type value 5-shot bleu 29.62 task dataset metrics type text-generation name type WMT_RO-EN WMT_RO-EN name type value 0-shot bleu 2.29 name type value 1-shot bleu 14.74 name type value 3-shot bleu 34.82 name type value 5-shot bleu 36.75 task dataset metrics type text-generation name type XQuAD_EM XQuAD_EM name type value 0-shot exact_match 42.86 name type value 1-shot exact_match 47.82 name type value 3-shot exact_match 48.32 name type value 5-shot exact_match 50.59 task dataset metrics type text-generation name type XQuAD_F1 XQuAD_F1 name type value 0-shot f1 63.66 name type value 1-shot f1 65.27 name type value 3-shot f1 66.04 name type value 5-shot f1 68.12 task dataset metrics type text-generation name type STS_Spearman STS_Spearman name type value 1-shot spearman 54.51 name type value 3-shot spearman 60.98 name type value 5-shot spearman 61.65 task dataset metrics type text-generation name type STS_Pearson STS_Pearson name type value 1-shot pearson 54.35 name type value 3-shot pearson 57.88 name type value 5-shot pearson 57.13

Model Card for Model ID

This model points/is identical to RoLlama2-7b-Instruct-2025-04-23.

RoLlama2 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.

Model Details

Model Description

OpenLLM represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.

• Developed by: OpenLLM-Ro

• Language(s): Romanian
• License: cc-by-nc-4.0
• Finetuned from model: RoLlama2-7b-Base
• Trained using: RoAlpaca, RoAlpacaGPT4, RoDolly, RoSelfInstruct, RoNoRobots, RoOrca, RoCamel, RoOpenAssistant, RoUltraChat, RoMagpiePro, RoMagpieReasoning

Model Sources

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

Intended Use

Intended Use Cases

RoLlama2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.

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.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Instruct")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Instruct")

instruction = "Care este cel mai înalt vârf muntos din România?"
chat = [
        {"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."},
        {"role": "user", "content": instruction},
        ]
prompt = tokenizer.apply_chat_template(chat, tokenize=False)

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
Llama-2-7b-chat	36.84	37.03	33.80	55.87	45.36	4.90	44.09
RoLlama2-7b-Instruct-2024-05-14	45.71	43.66	39.70	70.34	57.36	18.78	44.44
RoLlama2-7b-Instruct-2024-10-09	44.50	44.73	40.39	63.67	59.12	13.29	45.78
RoLlama2-7b-Instruct-2025-04-23	45.51	45.70	40.36	63.26	60.25	18.02	45.48
RoLlama2-7b-Instruct-DPO-2024-10-09	43.20	44.24	38.39	62.57	59.20	15.72	39.07
RoLlama2-7b-Instruct-DPO-2025-04-23	46.77	48.16	41.38	64.15	61.37	18.35	47.20

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)
Llama-2-7b-chat	87.78	52.81	97.27	82.02	15.55	28.53	19.99	31.48
RoLlama2-7b-Instruct-2024-05-14	97.48	65.26	98.83	87.28	27.38	10.32	27.59	40.13
RoLlama2-7b-Instruct-2024-10-09	97.66	62.41	97.97	60.89	27.13	19.39	27.63	39.75
RoLlama2-7b-Instruct-2025-04-23	97.60	60.22	-	-	27.21	22.15	-	-
RoLlama2-7b-Instruct-DPO-2024-10-09	97.31	60.56	-	-	26.56	21.68	-	-
RoLlama2-7b-Instruct-DPO-2025-04-23	97.77	65.21	-	-	25.48	22.75	-	-

	XQuAD				STS
	Few-shot		Finetuned		Few-shot		Finetuned
Model	(EM)	(F1)	(EM)	(F1)	(Spearman)	(Pearson)	(Spearman)	(Pearson)
Llama-2-7b-chat	32.35	54.00	60.34	75.98	32.56	31.99	74.08	72.64
RoLlama2-7b-Instruct-2024-05-14	44.52	64.75	54.96	70.20	65.50	67.79	84.44	84.76
RoLlama2-7b-Instruct-2024-10-09	45.71	65.08	59.24	74.25	59.69	57.16	84.66	85.07
RoLlama2-7b-Instruct-2025-04-23	47.39	65.77	-	-	59.05	56.45	-	-
RoLlama2-7b-Instruct-DPO-2024-10-09	35.78	59.31	-	-	61.22	58.41	-	-
RoLlama2-7b-Instruct-DPO-2025-04-23	38.28	60.88	-	-	66.76	64.72	-	-

Romanian MT-Bench

Model	Average	1st turn	2nd turn	Answers in Ro
Llama-2-7b-chat	1.08	1.44	0.73	45/160
RoLlama2-7b-Instruct-2024-05-14	3.86	4.67	3.04	160/160
RoLlama2-7b-Instruct-2024-10-09	4.43	4.92	3.94	160/160
RoLlama2-7b-Instruct-2025-04-23	4.97	5.56	4.39	160/160
RoLlama2-7b-Instruct-DPO-2024-10-09	4.61	5.15	4.06	160/160
RoLlama2-7b-Instruct-DPO-2025-04-23	5.55	5.84	5.26	160/160

RoCulturaBench

Model	Average	Answers in Ro
Llama-2-7b-chat	1.21	33/100
RoLlama2-7b-Instruct-2024-05-14	3.77	100/100
RoLlama2-7b-Instruct-2024-10-09	4.08	100/100
RoLlama2-7b-Instruct-2025-04-23	4.56	100/100
RoLlama2-7b-Instruct-DPO-2024-10-09	4.80	100/100
RoLlama2-7b-Instruct-DPO-2025-04-23	5.24	100/100

RoLlama2 Model Family

Model	Link
RoLlama2-7b-Base-2024-05-14	link
RoLlama2-7b-Instruct-2024-05-14	link
RoLlama2-7b-Instruct-2024-10-09	link
RoLlama2-7b-Instruct-2025-04-23	link
RoLlama2-7b-Instruct-DPO-2024-10-09	link
RoLlama2-7b-Instruct-DPO-2025-04-23	link

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"
}