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utter-project/
EuroMoE-2.6B-A0.6B-Instruct-2512

Name: EuroMoE-2.6B-A0.6B-Instruct-2512
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Model Card for EuroMoE-2.6B-A0.6B-Instruct-2512

This is the model card for EuroMoE-2.6B-A0.6B-Instruct-2512. You can also check the pre-trained version: EuroMoE-2.6B-A0.6B-2512.

Developed by: Instituto Superior Técnico - University of Lisbon, Instituto de Telecomunicações, University of Edinburgh, Aveni, Unbabel, University of Paris-Saclay, Artefact Research Center, University of Amsterdam, Naver Labs, Sorbonne Université.
Funded by: European Union.
Model type: A 2.6B parameter multilingual transformer MoE with 0.6B active parameters.
Language(s) (NLP): Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, Turkish, and Ukrainian.
License: Apache License 2.0.

Model Details

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. EuroMoE-2.6B-A0.6B is a 2.6B parameter model trained on 8 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets. EuroMoE-2.6B-A0.6B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.

Model Description

EuroMoE uses a standard MoE Transformer architecture:

We use grouped query attention (GQA) with 2 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 512 Nvidia A100 GPUs of the Leonardo supercomputer, training the model with a constant batch size of 4096 sequences, which corresponds to approximately 17 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	24
Embedding Size	1,024
Total/Active experts	64/8
Expert Hidden Size	512
Number of Heads	8
Number of KV Heads (GQA)	2
Activation Function	SwiGLU
Position Encodings	RoPE (\Theta=500,000)
Layer Norm	RMSNorm
Tied Embeddings	Yes
Embedding Parameters	0.13B
LM Head Parameters	0.13B
Active Non-embedding Parameters	0.34B
Total Non-embedding Parameters	2.35B
Active Parameters	0.6B
Total Parameters	2.61B

Run the model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "utter-project/EuroMoE-2.6B-A0.6B-Instruct-2512"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {
        "role": "system",
        "content": "You are EuroLLM --- an AI assistant specialized in European languages that provides safe, educational and helpful answers.",
    },
    {
        "role": "user", "content": "What is the capital of Portugal? How would you describe it?"
    },
    ]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Bias, Risks, and Limitations

EuroMoE-2.6B-A0.6B-Instruct-2512 has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).