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  2. config.json +4 -3
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  6. tokenizer_config.json +2049 -1
README.md CHANGED
@@ -1,161 +1,59 @@
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  ---
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- license: apache-2.0
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- language:
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- - en
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- - de
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- - es
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- - fr
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- - it
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- - pt
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- - pl
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- - nl
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- - tr
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- - sv
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- - cs
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- - el
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- - hu
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- - ro
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- - fi
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- - uk
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- - sl
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- - sk
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- - da
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- - lt
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- - lv
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- - et
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- - bg
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- - no
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- - ca
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- - hr
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- - ga
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- - mt
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- - gl
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- - zh
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- - ru
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- - ko
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- - ja
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- - ar
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- - hi
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  ---
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- ## *Model updated on September 24*
 
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- # Model Card for EuroLLM-1.7B-Instruct
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- 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](https://huggingface.co/utter-project/EuroLLM-1.7B).
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- - **Developed by:** Unbabel, Instituto Superior Técnico, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Université.
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- - **Funded by:** European Union.
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- - **Model type:** A 1.7B parameter instruction tuned multilingual transfomer LLM.
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- - **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.
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- - **License:** Apache License 2.0.
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- ## Model Details
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- 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.
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- 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.
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- EuroLLM-1.7B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
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- ### Model Description
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- EuroLLM uses a standard, dense Transformer architecture:
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- - 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.
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- - We perform pre-layer normalization, since it improves the training stability, and use the RMSNorm, which is faster.
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- - We use the SwiGLU activation function, since it has been shown to lead to good results on downstream tasks.
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- - 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.
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- 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.
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- Here is a summary of the model hyper-parameters:
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- | | |
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- |--------------------------------------|----------------------|
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- | Sequence Length | 4,096 |
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- | Number of Layers | 24 |
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- | Embedding Size | 2,048 |
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- | FFN Hidden Size | 5,632 |
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- | Number of Heads | 16 |
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- | Number of KV Heads (GQA) | 8 |
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- | Activation Function | SwiGLU |
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- | Position Encodings | RoPE (\Theta=10,000) |
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- | Layer Norm | RMSNorm |
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- | Tied Embeddings | No |
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- | Embedding Parameters | 0.262B |
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- | LM Head Parameters | 0.262B |
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- | Non-embedding Parameters | 1.133B |
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- | Total Parameters | 1.657B |
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- ## Run the model
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-
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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-
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- model_id = "utter-project/EuroLLM-1.7B-Instruct"
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- tokenizer = AutoTokenizer.from_pretrained(model_id)
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- model = AutoModelForCausalLM.from_pretrained(model_id)
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-
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- 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'
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-
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- inputs = tokenizer(text, return_tensors="pt")
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- outputs = model.generate(**inputs, max_new_tokens=20)
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- print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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- ## Results
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- ### Machine Translation
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-
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- We evaluate EuroLLM-1.7B-Instruct on several machine translation benchmarks: FLORES-200, WMT-23, and WMT-24 comparing it with [Gemma-2B](https://huggingface.co/google/gemma-2b) and [Gemma-7B](https://huggingface.co/google/gemma-7b) (also instruction tuned on EuroBlocks).
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- The results show that EuroLLM-1.7B is substantially better than Gemma-2B in Machine Translation and competitive with Gemma-7B.
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-
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-
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- #### Flores-200
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- | 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 |
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- |--------------------------------|------|-----------|-----------|-------|-------|-------|-------|-------|-------|-------|--------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|----------|-------|-------|-------|-------|-------|-------|-------|----------|-------|-------|-------|-------|-------|-------|-------|--------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|----------|-------|-------|-------|-------|-------|-------|-------|----------|
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- | 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 |
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- | 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 |
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- | 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 |
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-
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-
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- #### WMT-23
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- | 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 |
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- |--------------------------------|------|-----------|-----------|-----------|-------|-------|-------|-------|----------|-------|-------|-------|----------|-------|
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- | 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 | 79.42 | 86.82 |
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- | 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 | 78.99 | 81.15 |
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- | 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 | 81.33 | 85.98 |
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-
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-
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- #### WMT-24
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- | 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 |
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- |---------|------|------|-------|----|---|-------|-------|--------|--------|-------|-------|-------|-----|
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- | 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 |
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- |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 |
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- |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 |
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-
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-
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- ### General Benchmarks
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- We also compare EuroLLM-1.7B with [TinyLlama-v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) and [Gemma-2B](https://huggingface.co/google/gemma-2b) on 3 general benchmarks: Arc Challenge and Hellaswag.
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- For the non-english languages we use the [Okapi](https://aclanthology.org/2023.emnlp-demo.28.pdf) datasets.
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- 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).
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-
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- #### Arc Challenge
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- | Model | Average | English | German | Spanish | French | Italian | Portuguese | Chinese | Russian | Dutch | Arabic | Swedish | Hindi | Hungarian | Romanian | Ukrainian | Danish | Catalan |
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- |--------------------|---------|---------|--------|---------|--------|---------|------------|---------|---------|-------|--------|---------|--------|-----------|----------|-----------|--------|---------|
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- | 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 |
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- | 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 |
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- | 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 |
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-
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- #### Hellaswag
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- | Model | Average | English | German | Spanish | French | Italian | Portuguese | Russian | Dutch | Arabic | Swedish | Hindi | Hungarian | Romanian | Ukrainian | Danish | Catalan |
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- |--------------------|---------|---------|--------|---------|--------|---------|------------|---------|--------|--------|---------|--------|-----------|----------|-----------|--------|---------|
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- | 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 | 0.4445 |
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- | 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 |
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- | 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 |
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-
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-
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- ## Bias, Risks, and Limitations
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-
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- 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).
 
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  ---
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+ library_name: transformers
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+ base_model: Unbabel/EuroLLM-1B-annealed-fw
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: experiments/EuroLLM/EuroLLM-1B-annealed-fw-euroblocks-v0.1
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+ results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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+ # experiments/EuroLLM/EuroLLM-1B-annealed-fw-euroblocks-v0.1
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+ This model is a fine-tuned version of [Unbabel/EuroLLM-1B-annealed-fw](https://huggingface.co/Unbabel/EuroLLM-1B-annealed-fw) on the None dataset.
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+ ## Model description
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+ More information needed
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+ ## Intended uses & limitations
 
 
 
 
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+ More information needed
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+ ## Training and evaluation data
 
 
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+ More information needed
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+ ## Training procedure
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+ ### Training hyperparameters
 
 
 
 
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+ The following hyperparameters were used during training:
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+ - learning_rate: 7e-06
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+ - train_batch_size: 2
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 2
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+ - gradient_accumulation_steps: 24
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+ - total_train_batch_size: 96
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+ - total_eval_batch_size: 2
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 300
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+ - num_epochs: 4
 
 
 
 
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+ ### Training results
 
 
 
 
 
 
 
 
 
 
 
 
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+ ### Framework versions
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+ - Transformers 4.44.2
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+ - Pytorch 2.2.2+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "vocab_size": 128000
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  }
 
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  {
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+ "_name_or_path": "Unbabel/EuroLLM-1B-annealed-fw",
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1894
+ "236": {
1895
+ "content": "<extra_id_231>",
1896
+ "lstrip": false,
1897
+ "normalized": false,
1898
+ "rstrip": false,
1899
+ "single_word": false,
1900
+ "special": false
1901
+ },
1902
+ "237": {
1903
+ "content": "<extra_id_232>",
1904
+ "lstrip": false,
1905
+ "normalized": false,
1906
+ "rstrip": false,
1907
+ "single_word": false,
1908
+ "special": false
1909
+ },
1910
+ "238": {
1911
+ "content": "<extra_id_233>",
1912
+ "lstrip": false,
1913
+ "normalized": false,
1914
+ "rstrip": false,
1915
+ "single_word": false,
1916
+ "special": false
1917
+ },
1918
+ "239": {
1919
+ "content": "<extra_id_234>",
1920
+ "lstrip": false,
1921
+ "normalized": false,
1922
+ "rstrip": false,
1923
+ "single_word": false,
1924
+ "special": false
1925
+ },
1926
+ "240": {
1927
+ "content": "<extra_id_235>",
1928
+ "lstrip": false,
1929
+ "normalized": false,
1930
+ "rstrip": false,
1931
+ "single_word": false,
1932
+ "special": false
1933
+ },
1934
+ "241": {
1935
+ "content": "<extra_id_236>",
1936
+ "lstrip": false,
1937
+ "normalized": false,
1938
+ "rstrip": false,
1939
+ "single_word": false,
1940
+ "special": false
1941
+ },
1942
+ "242": {
1943
+ "content": "<extra_id_237>",
1944
+ "lstrip": false,
1945
+ "normalized": false,
1946
+ "rstrip": false,
1947
+ "single_word": false,
1948
+ "special": false
1949
+ },
1950
+ "243": {
1951
+ "content": "<extra_id_238>",
1952
+ "lstrip": false,
1953
+ "normalized": false,
1954
+ "rstrip": false,
1955
+ "single_word": false,
1956
+ "special": false
1957
+ },
1958
+ "244": {
1959
+ "content": "<extra_id_239>",
1960
+ "lstrip": false,
1961
+ "normalized": false,
1962
+ "rstrip": false,
1963
+ "single_word": false,
1964
+ "special": false
1965
+ },
1966
+ "245": {
1967
+ "content": "<extra_id_240>",
1968
+ "lstrip": false,
1969
+ "normalized": false,
1970
+ "rstrip": false,
1971
+ "single_word": false,
1972
+ "special": false
1973
+ },
1974
+ "246": {
1975
+ "content": "<extra_id_241>",
1976
+ "lstrip": false,
1977
+ "normalized": false,
1978
+ "rstrip": false,
1979
+ "single_word": false,
1980
+ "special": false
1981
+ },
1982
+ "247": {
1983
+ "content": "<extra_id_242>",
1984
+ "lstrip": false,
1985
+ "normalized": false,
1986
+ "rstrip": false,
1987
+ "single_word": false,
1988
+ "special": false
1989
+ },
1990
+ "248": {
1991
+ "content": "<extra_id_243>",
1992
+ "lstrip": false,
1993
+ "normalized": false,
1994
+ "rstrip": false,
1995
+ "single_word": false,
1996
+ "special": false
1997
+ },
1998
+ "249": {
1999
+ "content": "<extra_id_244>",
2000
+ "lstrip": false,
2001
+ "normalized": false,
2002
+ "rstrip": false,
2003
+ "single_word": false,
2004
+ "special": false
2005
+ },
2006
+ "250": {
2007
+ "content": "<extra_id_245>",
2008
+ "lstrip": false,
2009
+ "normalized": false,
2010
+ "rstrip": false,
2011
+ "single_word": false,
2012
+ "special": false
2013
+ },
2014
+ "251": {
2015
+ "content": "<extra_id_246>",
2016
+ "lstrip": false,
2017
+ "normalized": false,
2018
+ "rstrip": false,
2019
+ "single_word": false,
2020
+ "special": false
2021
+ },
2022
+ "252": {
2023
+ "content": "<extra_id_247>",
2024
+ "lstrip": false,
2025
+ "normalized": false,
2026
+ "rstrip": false,
2027
+ "single_word": false,
2028
+ "special": false
2029
+ },
2030
+ "253": {
2031
+ "content": "<extra_id_248>",
2032
+ "lstrip": false,
2033
+ "normalized": false,
2034
+ "rstrip": false,
2035
+ "single_word": false,
2036
+ "special": false
2037
+ },
2038
+ "254": {
2039
+ "content": "<extra_id_249>",
2040
+ "lstrip": false,
2041
+ "normalized": false,
2042
+ "rstrip": false,
2043
+ "single_word": false,
2044
+ "special": false
2045
+ },
2046
+ "255": {
2047
+ "content": "<extra_id_250>",
2048
+ "lstrip": false,
2049
+ "normalized": false,
2050
+ "rstrip": false,
2051
+ "single_word": false,
2052
+ "special": false
2053
+ },
2054
+ "256": {
2055
+ "content": "<extra_id_251>",
2056
+ "lstrip": false,
2057
+ "normalized": false,
2058
+ "rstrip": false,
2059
+ "single_word": false,
2060
+ "special": false
2061
+ },
2062
+ "257": {
2063
+ "content": "<extra_id_252>",
2064
+ "lstrip": false,
2065
+ "normalized": false,
2066
+ "rstrip": false,
2067
+ "single_word": false,
2068
+ "special": false
2069
+ },
2070
+ "258": {
2071
+ "content": "<extra_id_253>",
2072
+ "lstrip": false,
2073
+ "normalized": false,
2074
+ "rstrip": false,
2075
+ "single_word": false,
2076
+ "special": false
2077
+ },
2078
+ "259": {
2079
+ "content": "<extra_id_254>",
2080
+ "lstrip": false,
2081
+ "normalized": false,
2082
+ "rstrip": false,
2083
+ "single_word": false,
2084
+ "special": false
2085
+ },
2086
+ "260": {
2087
+ "content": "<extra_id_255>",
2088
+ "lstrip": false,
2089
+ "normalized": false,
2090
+ "rstrip": false,
2091
+ "single_word": false,
2092
+ "special": false
2093
  }
2094
  },
2095
  "bos_token": "<s>",
2096
+ "chat_template": "{%- set system_message = namespace(content='') -%}{%- if messages[0]['role'] == 'system' -%}{%- set system_message.content = messages[0]['content'] -%}{%- endif -%}<|im_start|>system\n{{ system_message.content }}<|im_end|>\n{% for message in messages -%}{%- if message['role'] != 'system' %}<|im_start|>{{ message['role'] }}\n{{ message['content'] }}<|im_end|>\n{%- endif -%}{%- endfor -%}",
2097
  "clean_up_tokenization_spaces": false,
2098
  "eos_token": "<|im_end|>",
2099
  "legacy": true,