This is the model card for EuroLLM-9B. You can also check the instruction tuned version: EuroLLM-9B-Instruct.
Developed by: Unbabel, Instituto Superior Técnico, Instituto de Telecomunicações, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Université.
Funded by: European Union.
Model type: A 9B parameter multilingual transfomer LLM.
The EuroLLM project has the goal of creating a suite of LLMs capable of understanding and generating text in all European Union languages as well as some additional relevant languages.
EuroLLM-9B is a 9B parameter model trained on 4 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets.
EuroLLM-9B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
Model Description
EuroLLM uses a standard, dense Transformer architecture:
We use grouped query attention (GQA) with 8 key-value heads, since it has been shown to increase speed at inference time while maintaining downstream performance.
We perform pre-layer normalization, since it improves the training stability, and use the RMSNorm, which is faster.
We use the SwiGLU activation function, since it has been shown to lead to good results on downstream tasks.
We use rotary positional embeddings (RoPE) in every layer, since these have been shown to lead to good performances while allowing the extension of the context length.
For pre-training, we use 400 Nvidia H100 GPUs of the Marenostrum 5 supercomputer, training the model with a constant batch size of 2,800 sequences, which corresponds to approximately 12 million tokens, using the Adam optimizer, and BF16 precision.
Here is a summary of the model hyper-parameters:
Sequence Length
4,096
Number of Layers
42
Embedding Size
4,096
FFN Hidden Size
12,288
Number of Heads
32
Number of KV Heads (GQA)
8
Activation Function
SwiGLU
Position Encodings
RoPE (\Theta=10,000)
Layer Norm
RMSNorm
Tied Embeddings
No
Embedding Parameters
0.524B
LM Head Parameters
0.524B
Non-embedding Parameters
8.105B
Total Parameters
9.154B
Run the model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "utter-project/EuroLLM-9B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "English: My name is EuroLLM. Portuguese:"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Results
EU Languages
image/png
Table 1: Comparison of open-weight LLMs on multilingual benchmarks. The borda count corresponds to the average ranking of the models (see (Colombo et al., 2022)). For Arc-challenge, Hellaswag, and MMLU we are using Okapi datasets (Lai et al., 2023) which include 11 languages. For MMLU-Pro and MUSR we translate the English version with Tower (Alves et al., 2024) to 6 EU languages.
* As there are no public versions of the pre-trained models, we evaluated them using the post-trained versions.
The results in Table 1 highlight EuroLLM-9B's superior performance on multilingual tasks compared to other European-developed models (as shown by the Borda count of 1.0), as well as its strong competitiveness with non-European models, achieving results comparable to Gemma-2-9B and outperforming the rest on most benchmarks.
English
image/png
Table 2: Comparison of open-weight LLMs on English general benchmarks.
* As there are no public versions of the pre-trained models, we evaluated them using the post-trained versions.
The results in Table 2 demonstrate EuroLLM's strong performance on English tasks, surpassing most European-developed models and matching the performance of Mistral-7B (obtaining the same Borda count).
Bias, Risks, and Limitations
EuroLLM-9B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
