Update README.md

README.md

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 This model was converted to GGUF format from [`utter-project/EuroLLM-1.7B-Instruct`](https://huggingface.co/utter-project/EuroLLM-1.7B-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-1.7B-Instruct) for more details on the model.
 
+---
+Model details:
+-
+This is the model card for the first instruction tuned model of the EuroLLM series: EuroLLM-1.7B-Instruct. You can also check the pre-trained version: EuroLLM-1.7B.
+
+    Developed by: Unbabel, Instituto Superior Técnico, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Université.
+    Funded by: European Union.
+    Model type: A 1.7B parameter instruction tuned 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-1.7B is a 1.7B 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-1.7B-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 256 Nvidia H100 GPUs of the Marenostrum 5 supercomputer, training the model with a constant batch size of 3,072 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 	24
+Embedding Size 	2,048
+FFN Hidden Size 	5,632
+Number of Heads 	16
+Number of KV Heads (GQA) 	8
+Activation Function 	SwiGLU
+Position Encodings 	RoPE (\Theta=10,000)
+Layer Norm 	RMSNorm
+Tied Embeddings 	No
+Embedding Parameters 	0.262B
+LM Head Parameters 	0.262B
+Non-embedding Parameters 	1.133B
+Total Parameters 	1.657B
+Run the model
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "utter-project/EuroLLM-1.7B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+text = '<|im_start|>system\n<|im_end|>\n<|im_start|>user\nTranslate the following English source text to Portuguese:\nEnglish: I am a language model for european languages. \nPortuguese: <|im_end|>\n<|im_start|>assistant\n'
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+Results
+Machine Translation
+
+We evaluate EuroLLM-1.7B-Instruct on several machine translation benchmarks: FLORES-200, WMT-23, and WMT-24 comparing it with Gemma-2B and Gemma-7B (also instruction tuned on EuroBlocks). The results show that EuroLLM-1.7B is substantially better than Gemma-2B in Machine Translation and competitive with Gemma-7B.
+Flores-200
+Model 	AVG 	AVG en-xx 	AVG xx-en 	en-ar 	en-bg 	en-ca 	en-cs 	en-da 	en-de 	en-el 	en-es-latam 	en-et 	en-fi 	en-fr 	en-ga 	en-gl 	en-hi 	en-hr 	en-hu 	en-it 	en-ja 	en-ko 	en-lt 	en-lv 	en-mt 	en-nl 	en-no 	en-pl 	en-pt-br 	en-ro 	en-ru 	en-sk 	en-sl 	en-sv 	en-tr 	en-uk 	en-zh-cn 	ar-en 	bg-en 	ca-en 	cs-en 	da-en 	de-en 	el-en 	es-latam-en 	et-en 	fi-en 	fr-en 	ga-en 	gl-en 	hi-en 	hr-en 	hu-en 	it-en 	ja-en 	ko-en 	lt-en 	lv-en 	mt-en 	nl-en 	no-en 	pl-en 	pt-br-en 	ro-en 	ru-en 	sk-en 	sl-en 	sv-en 	tr-en 	uk-en 	zh-cn-en
+EuroLLM-1.7B-Instruct 	86.89 	86.53 	87.25 	85.17 	89.42 	84.72 	89.13 	89.47 	86.90 	87.60 	86.29 	88.95 	89.40 	87.69 	74.89 	86.41 	76.92 	84.79 	86.78 	88.17 	89.76 	87.70 	87.27 	87.62 	67.84 	87.10 	90.00 	88.18 	89.29 	89.49 	88.32 	88.18 	86.85 	90.00 	87.31 	87.89 	86.60 	86.34 	87.45 	87.57 	87.95 	89.72 	88.80 	87.00 	86.77 	88.34 	89.09 	88.95 	82.69 	87.80 	88.37 	86.71 	87.20 	87.81 	86.79 	86.79 	85.62 	86.48 	81.10 	86.97 	90.25 	85.75 	89.20 	88.88 	86.00 	87.38 	86.76 	89.61 	87.94 		
+Gemma-2B-EuroBlocks 	81.59 	78.97 	84.21 	76.68 	82.73 	83.14 	81.63 	84.63 	83.15 	79.42 	84.05 	72.58 	79.73 	84.97 	40.50 	82.13 	67.79 	80.53 	78.36 	84.90 	87.43 	82.98 	72.29 	68.68 	58.55 	83.13 	86.15 	82.78 	86.79 	83.14 	84.61 	78.18 	75.37 	80.89 	78.38 	84.38 	84.35 	83.88 	85.77 	86.85 	86.31 	88.24 	88.12 	84.79 	84.90 	82.51 	86.32 	88.29 	54.78 	86.53 	85.83 	85.41 	85.18 	86.77 	85.78 	84.99 	81.65 	81.78 	67.27 	85.92 	89.07 	84.14 	88.07 	87.17 	85.23 	85.09 	83.95 	87.57 	84.77 		
+Gemma-7B-EuroBlocks 	85.27 	83.90 	86.64 	86.38 	87.87 	85.74 	84.25 	85.69 	81.49 	85.52 	86.93 	62.83 	84.96 	75.34 	84.93 	83.91 	86.92 	88.19 	86.11 	81.73 	80.55 	66.85 	85.31 	89.36 	85.87 	88.62 	88.06 	86.67 	84.79 	82.71 	86.45 	85.19 	86.67 	85.77 	86.36 	87.21 	88.09 	87.17 	89.40 	88.26 	86.74 	86.73 	87.25 	88.87 	88.81 	72.45 	87.62 	87.86 	87.08 	87.01 	87.58 	86.92 	86.70 	85.10 	85.74 	77.81 	86.83 	90.40 	85.41 	89.04 	88.77 	86.13 	86.67 	86.32 	89.27 	87.92 					
+WMT-23
+Model 	AVG 	AVG en-xx 	AVG xx-en 	AVG xx-xx 	en-de 	en-cs 	en-uk 	en-ru 	en-zh-cn 	de-en 	uk-en 	ru-en 	zh-cn-en 	cs-uk
+EuroLLM-1.7B-Instruct 	82.91 	83.20 	81.77 	86.82 	81.56 	85.23 	81.30 	82.47 	83.61 	85.03 	84.06 	85.25 	81.31 	78.83
+Gemma-2B-EuroBlocks 	79.96 	79.01 	80.86 	81.15 	76.82 	76.05 	77.92 	78.98 	81.58 	82.73 	82.71 	83.99 	80.35 	78.27
+Gemma-7B-EuroBlocks 	82.76 	82.26 	82.70 	85.98 	81.37 	82.42 	81.54 	82.18 	82.90 	83.17 	84.29 	85.70 	82.46 	79.73
+WMT-24
+Model 	AVG 	AVG en-xx 	AVG xx-xx 	en-de 	en-es-latam 	en-cs 	en-ru 	en-uk 	en-ja 	en-zh-cn 	en-hi 	cs-uk 	ja-zh-cn
+EuroLLM-1.7B-Instruct 	79.32 	79.32 	79.34 	79.42 	80.67 	80.55 	78.65 	80.12 	82.96 	80.60 	71.59 	83.48 	75.20
+Gemma-2B-EuroBlocks 	74.72 	74.41 	75.97 	74.93 	78.81 	70.54 	74.90 	75.84 	79.48 	78.06 	62.70 	79.87 	72.07
+Gemma-7B-EuroBlocks 	78.67 	78.34 	80.00 	78.88 	80.47 	78.55 	78.55 	80.12 	80.55 	78.90 	70.71 	84.33 	75.66
+General Benchmarks
+
+We also compare EuroLLM-1.7B with TinyLlama-v1.1 and Gemma-2B on 3 general benchmarks: Arc Challenge and Hellaswag. For the non-english languages we use the Okapi datasets. Results show that EuroLLM-1.7B is superior to TinyLlama-v1.1 and similar to Gemma-2B on Hellaswag but worse on Arc Challenge. This can be due to the lower number of parameters of EuroLLM-1.7B (1.133B non-embedding parameters against 1.981B).
+Arc Challenge
+Model 	Average 	English 	German 	Spanish 	French 	Italian 	Portuguese 	Chinese 	Russian 	Dutch 	Arabic 	Swedish 	Hindi 	Hungarian 	Romanian 	Ukrainian 	Danish 	Catalan
+EuroLLM-1.7B 	0.3496 	0.4061 	0.3464 	0.3684 	0.3627 	0.3738 	0.3855 	0.3521 	0.3208 	0.3507 	0.3045 	0.3605 	0.2928 	0.3271 	0.3488 	0.3516 	0.3513 	0.3396
+TinyLlama-v1.1 	0.2650 	0.3712 	0.2524 	0.2795 	0.2883 	0.2652 	0.2906 	0.2410 	0.2669 	0.2404 	0.2310 	0.2687 	0.2354 	0.2449 	0.2476 	0.2524 	0.2494 	0.2796
+Gemma-2B 	0.3617 	0.4846 	0.3755 	0.3940 	0.4080 	0.3687 	0.3872 	0.3726 	0.3456 	0.3328 	0.3122 	0.3519 	0.2851 	0.3039 	0.3590 	0.3601 	0.3565 	0.3516
+Hellaswag
+Model 	Average 	English 	German 	Spanish 	French 	Italian 	Portuguese 	Russian 	Dutch 	Arabic 	Swedish 	Hindi 	Hungarian 	Romanian 	Ukrainian 	Danish 	Catalan
+EuroLLM-1.7B 	0.4744 	0.4760 	0.6057 	0.4793 	0.5337 	0.5298 	0.5085 	0.5224 	0.4654 	0.4949 	0.4104 	0.4800 	0.3655 	0.4097 	0.4606 	0.436 	0.4702
+TinyLlama-v1.1 	0.3674 	0.6248 	0.3650 	0.4137 	0.4010 	0.3780 	0.3892 	0.3494 	0.3588 	0.2880 	0.3561 	0.2841 	0.3073 	0.3267 	0.3349 	0.3408 	0.3613
+Gemma-2B 	0.4666 	0.7165 	0.4756 	0.5414 	0.5180 	0.4841 	0.5081 	0.4664 	0.4655 	0.3868 	0.4383 	0.3413 	0.3710 	0.4316 	0.4291 	0.4471 	0.4448
+Bias, Risks, and Limitations
+
+EuroLLM-1.7B-Instruct has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
+Paper
+
+Paper: EuroLLM: Multilingual Language Models for Europe
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)