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EuroLLM-9B-Instruct-Q6_K-GGUF

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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)

 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.
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)