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EuroLLM-1.7B-Instruct-GGUF

Name: EuroLLM-1.7B-Instruct-GGUF
Rating: 2.5 (774 reviews)

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utter-project/EuroLLM-1.7B

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QuantFactory/EuroLLM-1.7B-Instruct-GGUF

This is quantized version of utter-project/EuroLLM-1.7B-Instruct created using llama.cpp

Original Model Card

Model updated on September 24

Model Card for EuroLLM-1.7B-Instruct

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
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).