stelterlab/EuroLLM-9B-Instruct-MLX-4bit

The Model stelterlab/EuroLLM-9B-Instruct-MLX-4bit was converted to MLX format from utter-project/EuroLLM-9B-Instruct using mlx-lm version 0.19.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("stelterlab/EuroLLM-9B-Instruct-MLX-4bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

Original Model Card follows:

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.

• 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)). For Arc-challenge, Hellaswag, and MMLU we are using Okapi datasets (Lai et al., 2023) which include 11 languages. For MMLU-Pro and MUSR we translate the English version with Tower (Alves et al., 2024) 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).