transformers gguf en de es fr it pt pl nl tr sv cs el hu ro fi uk sl sk da lt lv et bg no ca hr ga mt gl zh ru ko ja ar hi arxiv:2409.16235 license:apache-2.0 endpoints_compatible region:us
---
language:
- en
- de
- es
- fr
- it
- pt
- pl
- nl
- tr
- sv
- cs
- el
- hu
- ro
- fi
- uk
- sl
- sk
- da
- lt
- lv
- et
- bg
- 'no'
- ca
- hr
- ga
- mt
- gl
- zh
- ru
- ko
- ja
- ar
- hi
library_name: transformers
license: apache-2.0
---

## *Model updated on September 24*


# Model Card for EuroLLM-1.7B


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](https://huggingface.co/utter-project/EuroLLM-1.7B-Instruct).

- **Developed by:** Unbabel, Instituto Superior Técnico, 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.
- **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.
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](https://huggingface.co/google/gemma-2b) and [Gemma-7B](https://huggingface.co/google/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      | 79.42      | 86.82      |
| 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      | 78.99      | 81.15      |
| 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      | 81.33      | 85.98      |



#### 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](https://huggingface.co/TinyLlama/TinyLlama_v1.1) and [Gemma-2B](https://huggingface.co/google/gemma-2b) on 3 general benchmarks: Arc Challenge and Hellaswag.
For the non-english languages we use the [Okapi](https://aclanthology.org/2023.emnlp-demo.28.pdf) 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     | 0.4445     |
| 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).

## Paper

Paper: [EuroLLM: Multilingual Language Models for Europe](https://huggingface.co/papers/2409.16235)