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Model: stelterlab/EuroLLM-9B-Instruct-AWQ Source: Original Platform
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README.md
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license: apache-2.0
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language:
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- en
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- de
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- es
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- fr
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- it
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- pt
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- pl
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- nl
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- tr
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- sv
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- cs
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- el
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- hu
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- ro
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- fi
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- uk
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- sl
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- sk
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- da
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- lt
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- lv
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- et
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- bg
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- 'no'
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- ca
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- hr
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- ga
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- mt
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- gl
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- zh
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- ru
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- ko
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- ja
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- ar
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- hi
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library_name: transformers
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base_model:
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- utter-project/EuroLLM-9B-Instruct
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---
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AWQ quantization: done by stelterlab in INT4 GEMM with AutoAWQ by casper-hansen (https://github.com/casper-hansen/AutoAWQ/)
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Original Weights by the utter-project. Original Model Card follows:
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# Model Card for EuroLLM-9B-Instruct
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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).
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- **Developed by:** Unbabel, Instituto Superior Técnico, Instituto de Telecomunicações, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Université.
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- **Funded by:** European Union.
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- **Model type:** A 9B parameter multilingual transfomer LLM.
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- **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.
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- **License:** Apache License 2.0.
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## Model Details
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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.
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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.
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EuroLLM-9B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
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### Model Description
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EuroLLM uses a standard, dense Transformer architecture:
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- 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.
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- We perform pre-layer normalization, since it improves the training stability, and use the RMSNorm, which is faster.
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- We use the SwiGLU activation function, since it has been shown to lead to good results on downstream tasks.
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- 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.
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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.
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Here is a summary of the model hyper-parameters:
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| | |
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|--------------------------------------|----------------------|
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| Sequence Length | 4,096 |
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| Number of Layers | 42 |
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| Embedding Size | 4,096 |
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| FFN Hidden Size | 12,288 |
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| Number of Heads | 32 |
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| Number of KV Heads (GQA) | 8 |
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| Activation Function | SwiGLU |
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| Position Encodings | RoPE (\Theta=10,000) |
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| Layer Norm | RMSNorm |
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| Tied Embeddings | No |
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| Embedding Parameters | 0.524B |
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| LM Head Parameters | 0.524B |
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| Non-embedding Parameters | 8.105B |
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| Total Parameters | 9.154B |
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## Run the model
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "utter-project/EuroLLM-9B-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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messages = [
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{
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"role": "system",
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"content": "You are EuroLLM --- an AI assistant specialized in European languages that provides safe, educational and helpful answers.",
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},
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{
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"role": "user", "content": "What is the capital of Portugal? How would you describe it?"
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},
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(inputs, max_new_tokens=1024)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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## Results
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### EU Languages
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**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.
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\* As there are no public versions of the pre-trained models, we evaluated them using the post-trained versions.
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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.
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### English
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**Table 2:** Comparison of open-weight LLMs on English general benchmarks.
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\* As there are no public versions of the pre-trained models, we evaluated them using the post-trained versions.
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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).
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## Bias, Risks, and Limitations
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EuroLLM-9B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
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