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Romanian
llama

LLMic Model Card

LLMic: Romanian Foundation Language Model

Model Summary

LLMic is a bilingual Romanian-English foundation model. LLmic is a 3B parameters dense decoder-only Transformer model based on Llama2.

Architecture

Parameter Value
Sequence Length 2048
Number of Layers 24
Embedding Size 2,560
FFN Hidden Size 10,240
Number of Heads 20
Number of KV Heads 5
Activation Function SiLU
Position Encodings RoPE (Θ=500,000)
Layer Norm RMSNorm (ε=10⁻⁵)
Tied Embeddings No

Intended Use

Our model is designed to accelerate research on Romanian language models, serving as a building block for generative AI applications.

Use with transformers

from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

device = "cuda"
model_id = "faur-ai/LLMic"
prompt = "Capitala României este"

model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer)

inputs = tokenizer.encode(
    prompt,
    add_special_tokens=False,
    return_tensors='pt',
).to(device)

outputs = model.generate(
    streamer=streamer,
    input_ids=inputs,
    temperature=0.8,
    do_sample=True
)

Data Overview

Training Datasets

Source Size
Romanian (300B)
Web Sources 621 GB
Discussions, Curated & Parallel 10 GB
English (700B)
FineWebEdu --
Dolma Subset 109 GB

Benchmark datasets

We evaluated LLMic on the WMT16 language translation benchmark for English-to-Romanian.

Model Score
LLMIC 41.01
mBART 38.50
Llama-3.1-8B-Instruct 29.02
RoMistral-7b-Instruct 27.70
RoLlama3-8b-Instruct 27.31
Mistral-7B-Instruct-v0.2 26.19
RoGemma-7b-Instruct 25.96
Gemma-1.1-7b-it 25.48

Citation

BibTeX:

@misc{bădoiu2025llmicromanianfoundationlanguage,
      title={LLMic: Romanian Foundation Language Model}, 
      author={Vlad-Andrei Bădoiu and Mihai-Valentin Dumitru and Alexandru M. Gherghescu and Alexandru Agache and Costin Raiciu},
      year={2025},
      eprint={2501.07721},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.07721}, 
}
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Dataset used to train faur-ai/LLMic