GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
utter-project
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
utter-project
Model Tree
utter-project/EuroLLM-22B-2512
Input Modalities
Output Modalities
Supported Functionality
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-22B is a 22B 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-22B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
EuroLLM uses a standard, dense Transformer architecture withgrouped query attention (GQA), pre-layer normalization with RMSNorm, SwiGLU activations and rotary positional embeddings (RoPE) in every layer. Here is a summary of the model hyper-parameters:
| Sequence Length | 32,768 |
| Number of Layers | 56 |
| Embedding Size | 6,144 |
| FFN Hidden Size | 16,384 |
| Number of Heads | 48 |
| Number of KV Heads (GQA) | 8 |
| Activation Function | SwiGLU |
| Position Encodings | RoPE (\Theta=1,000,000) |
| Layer Norm | RMSNorm |
| Tied Embeddings | No |
| Embedding Parameters | 0.786B |
| LM Head Parameters | 0.786B |
| Non-embedding Parameters | 21.067B |
| Total Parameters | 22.639B |
EuroLLM-22B was trained on approximately 4 trillion tokens, using 400 Nvidia H100 GPUs on the MareNostrum5 supercomputer, thanks to an EuroHPC extreme-scale access grant. The training process was carefully structured into three key phases:
During post-training, we adapt EuroLLM to be an instruction-following model capable of handling multi-turn conversations. We start by regenerating the final responses from publicly available datasets using several open models, and keep the best candidate using a reward model. To this data, we add records from other datasets (Nemotron, Hermes-3 and Tulu 3), removing duplicates based on the first prompt. This pipeline shows how EuroLLM can be easily adapted for your use-cases.
The model excels at translation tasks being capable of translating across all official EU languages, matching or outperforming strong models like Gemma-3-27B, Qwen-3-32B and Apertus-70B. Furthermore, when it comes to general benchmarks, it is the best EU-made fully open model.
markdown
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "utter-project/EuroLLM-22B-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))
Table 1: Comparison of fully open and open-weight LLMs on a suite of multilingual benchmarks, averaging over all languages supported by EuroLLM-22B that are present in each benchmark. The table reports scores on HellaSwag, MMLU, MMLU-Pro, ARC-Challenge, MGSM, FLORES, and WMT24++. The Borda Count (Colombo et al., 2022) reflects the average ranking of each model across all benchmarks. Bold values indicate the best overall system for each benchmark, while underscored values denote the best fully open system.
Table 2: Comparison of fully open and open-weight LLMs on a suite of English benchmarks. The table reports scores on IFEval, HellaSwag, MMLU, MMLU-Pro, BBH, ARC-Challenge, GPQA, GSM8K, MATH-500, and HumanEval. The Borda Count reflects the average ranking of each model across all benchmarks. Bold values indicate the best overall system for each benchmark, while underscored values denote the best fully open system.
EuroLLM-22B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
If you use our work, please cite:
markdown
@misc{ramos2026eurollm22btechnicalreport,title={EuroLLM-22B: Technical Report},author={Miguel Moura Ramos and Duarte M. Alves and Hippolyte Gisserot-Boukhlef and João Alves and Pedro Henrique Martins and Patrick Fernandes and José Pombal and Nuno M. Guerreiro and Ricardo Rei and Nicolas Boizard and Amin Farajian and Mateusz Klimaszewski and José G. C. de Souza and Barry Haddow and François Yvon and Pierre Colombo and Alexandra Birch and André F. T. Martins},year={2026},eprint={2602.05879},archivePrefix={arXiv},primaryClass={cs.CL},url={https://arxiv.org/abs/2602.05879},}