This is the model card for the first pre-trained model of the EuroLLM series: EuroLLM-1.7B. You can also check the instruction tuned version: EuroLLM-1.7B-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 1.7B 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-1.7B is a 1.7B 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-1.7B-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 256 Nvidia H100 GPUs of the Marenostrum 5 supercomputer, training the model with a constant batch size of 3,072 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
24
Embedding Size
2,048
FFN Hidden Size
5,632
Number of Heads
16
Number of KV Heads (GQA)
8
Activation Function
SwiGLU
Position Encodings
RoPE (\Theta=10,000)
Layer Norm
RMSNorm
Tied Embeddings
No
Embedding Parameters
0.262B
LM Head Parameters
0.262B
Non-embedding Parameters
1.133B
Total Parameters
1.657B
Run the model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "utter-project/EuroLLM-1.7B"
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
Machine Translation
We evaluate EuroLLM-1.7B-Instruct on several machine translation benchmarks: FLORES-200, WMT-23, and WMT-24 comparing it with Gemma-2B and Gemma-7B (also instruction tuned on EuroBlocks).
The results show that EuroLLM-1.7B is substantially better than Gemma-2B in Machine Translation and competitive with Gemma-7B.
Flores-200
Model
AVG
AVG en-xx
AVG xx-en
en-ar
en-bg
en-ca
en-cs
en-da
en-de
en-el
en-es-latam
en-et
en-fi
en-fr
en-ga
en-gl
en-hi
en-hr
en-hu
en-it
en-ja
en-ko
en-lt
en-lv
en-mt
en-nl
en-no
en-pl
en-pt-br
en-ro
en-ru
en-sk
en-sl
en-sv
en-tr
en-uk
en-zh-cn
ar-en
bg-en
ca-en
cs-en
da-en
de-en
el-en
es-latam-en
et-en
fi-en
fr-en
ga-en
gl-en
hi-en
hr-en
hu-en
it-en
ja-en
ko-en
lt-en
lv-en
mt-en
nl-en
no-en
pl-en
pt-br-en
ro-en
ru-en
sk-en
sl-en
sv-en
tr-en
uk-en
zh-cn-en
EuroLLM-1.7B-Instruct
86.89
86.53
87.25
85.17
89.42
84.72
89.13
89.47
86.90
87.60
86.29
88.95
89.40
87.69
74.89
86.41
76.92
84.79
86.78
88.17
89.76
87.70
87.27
87.62
67.84
87.10
90.00
88.18
89.29
89.49
88.32
88.18
86.85
90.00
87.31
87.89
86.60
86.34
87.45
87.57
87.95
89.72
88.80
87.00
86.77
88.34
89.09
88.95
82.69
87.80
88.37
86.71
87.20
87.81
86.79
86.79
85.62
86.48
81.10
86.97
90.25
85.75
89.20
88.88
86.00
87.38
86.76
89.61
87.94
Gemma-2B-EuroBlocks
81.59
78.97
84.21
76.68
82.73
83.14
81.63
84.63
83.15
79.42
84.05
72.58
79.73
84.97
40.50
82.13
67.79
80.53
78.36
84.90
87.43
82.98
72.29
68.68
58.55
83.13
86.15
82.78
86.79
83.14
84.61
78.18
75.37
80.89
78.38
84.38
84.35
83.88
85.77
86.85
86.31
88.24
88.12
84.79
84.90
82.51
86.32
88.29
54.78
86.53
85.83
85.41
85.18
86.77
85.78
84.99
81.65
81.78
67.27
85.92
89.07
84.14
88.07
87.17
85.23
85.09
83.95
87.57
84.77
Gemma-7B-EuroBlocks
85.27
83.90
86.64
86.38
87.87
85.74
84.25
85.69
81.49
85.52
86.93
62.83
84.96
75.34
84.93
83.91
86.92
88.19
86.11
81.73
80.55
66.85
85.31
89.36
85.87
88.62
88.06
86.67
84.79
82.71
86.45
85.19
86.67
85.77
86.36
87.21
88.09
87.17
89.40
88.26
86.74
86.73
87.25
88.87
88.81
72.45
87.62
87.86
87.08
87.01
87.58
86.92
86.70
85.10
85.74
77.81
86.83
90.40
85.41
89.04
88.77
86.13
86.67
86.32
89.27
87.92
WMT-23
Model
AVG
AVG en-xx
AVG xx-en
AVG xx-xx
en-de
en-cs
en-uk
en-ru
en-zh-cn
de-en
uk-en
ru-en
zh-cn-en
cs-uk
EuroLLM-1.7B-Instruct
82.91
83.20
81.77
86.82
81.56
85.23
81.30
82.47
83.61
85.03
84.06
85.25
81.31
78.83
Gemma-2B-EuroBlocks
79.96
79.01
80.86
81.15
76.82
76.05
77.92
78.98
81.58
82.73
82.71
83.99
80.35
78.27
Gemma-7B-EuroBlocks
82.76
82.26
82.70
85.98
81.37
82.42
81.54
82.18
82.90
83.17
84.29
85.70
82.46
79.73
WMT-24
Model
AVG
AVG en-xx
AVG xx-xx
en-de
en-es-latam
en-cs
en-ru
en-uk
en-ja
en-zh-cn
en-hi
cs-uk
ja-zh-cn
EuroLLM-1.7B-Instruct
79.32
79.32
79.34
79.42
80.67
80.55
78.65
80.12
82.96
80.60
71.59
83.48
75.20
Gemma-2B-EuroBlocks
74.72
74.41
75.97
74.93
78.81
70.54
74.90
75.84
79.48
78.06
62.70
79.87
72.07
Gemma-7B-EuroBlocks
78.67
78.34
80.00
78.88
80.47
78.55
78.55
80.12
80.55
78.90
70.71
84.33
75.66
General Benchmarks
We also compare EuroLLM-1.7B with TinyLlama-v1.1 and Gemma-2B on 3 general benchmarks: Arc Challenge and Hellaswag.
For the non-english languages we use the Okapi datasets.
Results show that EuroLLM-1.7B is superior to TinyLlama-v1.1 and similar to Gemma-2B on Hellaswag but worse on Arc Challenge. This can be due to the lower number of parameters of EuroLLM-1.7B (1.133B non-embedding parameters against 1.981B).
Arc Challenge
Model
Average
English
German
Spanish
French
Italian
Portuguese
Chinese
Russian
Dutch
Arabic
Swedish
Hindi
Hungarian
Romanian
Ukrainian
Danish
Catalan
EuroLLM-1.7B
0.3496
0.4061
0.3464
0.3684
0.3627
0.3738
0.3855
0.3521
0.3208
0.3507
0.3045
0.3605
0.2928
0.3271
0.3488
0.3516
0.3513
0.3396
TinyLlama-v1.1
0.2650
0.3712
0.2524
0.2795
0.2883
0.2652
0.2906
0.2410
0.2669
0.2404
0.2310
0.2687
0.2354
0.2449
0.2476
0.2524
0.2494
0.2796
Gemma-2B
0.3617
0.4846
0.3755
0.3940
0.4080
0.3687
0.3872
0.3726
0.3456
0.3328
0.3122
0.3519
0.2851
0.3039
0.3590
0.3601
0.3565
0.3516
Hellaswag
Model
Average
English
German
Spanish
French
Italian
Portuguese
Russian
Dutch
Arabic
Swedish
Hindi
Hungarian
Romanian
Ukrainian
Danish
Catalan
EuroLLM-1.7B
0.4744
0.4760
0.6057
0.4793
0.5337
0.5298
0.5085
0.5224
0.4654
0.4949
0.4104
0.4800
0.3655
0.4097
0.4606
0.436
0.4702
TinyLlama-v1.1
0.3674
0.6248
0.3650
0.4137
0.4010
0.3780
0.3892
0.3494
0.3588
0.2880
0.3561
0.2841
0.3073
0.3267
0.3349
0.3408
0.3613
Gemma-2B
0.4666
0.7165
0.4756
0.5414
0.5180
0.4841
0.5081
0.4664
0.4655
0.3868
0.4383
0.3413
0.3710
0.4316
0.4291
0.4471
0.4448
Bias, Risks, and Limitations
EuroLLM-1.7B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
