Update README.md

README.md

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 This model was converted to GGUF format from [`utter-project/EuroLLM-9B-Instruct`](https://huggingface.co/utter-project/EuroLLM-9B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/utter-project/EuroLLM-9B-Instruct) for more details on the model.
 
+---
+Model details:
+-
+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).
+
+ ---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)