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erenyeager-1
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Model Details
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erenyeager-1
Model Tree
utter-project/EuroMoE-2.6B-A0.6B-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. 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.
EuroMoE uses a standard MoE Transformer architecture:
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 |
text
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))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).
from transformers import AutoModelForCausalLM, AutoTokenizermodel_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))