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Model: utter-project/EuroLLM-9B
Source: Original Platform

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README.md

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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
+---
+
+# Model Card for EuroLLM-9B
+
+
+This is the model card for EuroLLM-9B. You can also check the instruction tuned version: [EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct).
+
+- **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"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(model_id)
+    
+    text = "English: My name is EuroLLM. Portuguese:"
+    
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=20)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+
+## Results
+
+### EU Languages
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63f33ecc0be81bdc5d903466/ob_1sLM8c7dxuwpv6AAHA.png)
+**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.  
+\* 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
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63f33ecc0be81bdc5d903466/EfilsW_p-JA13mV2ilPkm.png)
+
+**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).

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "bos_token_id": 1,
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+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 42,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "vocab_size": 128000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+  "eos_token_id": 2,
+  "transformers_version": "4.40.1"
+}

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special_tokens_map.json

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tokenizer_config.json

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+}