| --- |
| license: apache-2.0 |
| 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 |
| base_model: |
| - utter-project/EuroLLM-9B |
| --- |
| |
| # Model Card for EuroLLM-9B-Instruct |
|
|
| This is the model card for EuroLLM-9B-Instruct. You can also check the pre-trained version: [EuroLLM-9B](https://huggingface.co/utter-project/EuroLLM-9B). |
|
|
| - **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. |
| - **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-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-Instruct" |
| 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)) |
| |
| ## Results |
|
|
| ### EU Languages |
|
|
|
|
|  |
| **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](https://arxiv.org/abs/2202.03799))). For Arc-challenge, Hellaswag, and MMLU we are using Okapi datasets ([Lai et al., 2023](https://aclanthology.org/2023.emnlp-demo.28/)) which include 11 languages. For MMLU-Pro and MUSR we translate the English version with Tower ([Alves et al., 2024](https://arxiv.org/abs/2402.17733)) 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 |
|
|
|
|
|  |
|
|
| **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). |
