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---
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/EuroMoE-2.6B-A0.6B-Preview
---
# Model Card for EuroMoE-2.6B-A0.6B-Instruct-Preview
⚠️ PREVIEW RELEASE: This is a preview version of EuroMoE-2.6B-A0.6B-Instruct-Preview. The model is still under development and may have limitations in performance and stability. Use with caution in production environments.
This is the model card for EuroMoE-2.6B-A0.6B-Instruct-Preview. You can also check the pre-trained version: [EuroMoE-2.6B-A0.6B-Preview](https://huggingface.co/utter-project/EuroMoE-2.6B-A0.6B-Preview).
- **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 2.6B parameter multilingual transformer MoE with 0.6B active parameters.
- **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.
EuroMoE-2.6B-A0.6B is a 22B parameter model trained on 8 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets.
EuroMoE-2.6B-A0.6B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
### Model Description
EuroMoE uses a standard MoE Transformer architecture:
- We use grouped query attention (GQA) with 2 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 512 Nvidia A100 GPUs of the Leonardo supercomputer, training the model with a constant batch size of 4096 sequences, which corresponds to approximately 17 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 | 1,024 |
| Total/Active experts | 64/8 |
| Expert Hidden Size | 512 |
| Number of Heads | 8 |
| Number of KV Heads (GQA) | 2 |
| Activation Function | SwiGLU |
| Position Encodings | RoPE (\Theta=500,000) |
| Layer Norm | RMSNorm |
| Tied Embeddings | Yes |
| Embedding Parameters | 0.13B |
| LM Head Parameters | 0.13B |
| Active Non-embedding Parameters | 0.34B |
| Total Non-embedding Parameters | 2.35B |
| Active Parameters | 0.6B |
| Total Parameters | 2.61B |
## Run the model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview"
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))
## Bias, Risks, and Limitations
EuroMoE-2.6B-A0.6B-Instruct-Preview has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements). |