--- 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 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63f33ecc0be81bdc5d903466/ob_1sLM8c7dxuwpv6AAHA.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](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 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63f33ecc0be81bdc5d903466/EfilsW_p-JA13mV2ilPkm.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).