diff --git a/README.md b/README.md index 7b95401..7b17780 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,137 @@ ---- -license: apache-2.0 ---- +--- +license: apache-2.0 +language: +- en +- de +- es +- fr +- it +- pt +- pl +- nl +- tr +- sv +- cs +- el +- hu +- ro +- fi +- uk +- sl +- sk +- da +- lt +- lv +- et +- bg +- 'no' +- ca +- hr +- ga +- mt +- gl +- zh +- ru +- ko +- ja +- ar +- hi +library_name: transformers +base_model: +- utter-project/EuroLLM-9B-Instruct +--- + +AWQ quantization: done by stelterlab in INT4 GEMM with AutoAWQ by casper-hansen (https://github.com/casper-hansen/AutoAWQ/) + +Original Weights by the utter-project. Original Model Card follows: + +# Model Card for EuroLLM-9B-Instruct + +This is the model card for EuroLLM-9B-Instruct. You can also check the pre-trained version: [EuroLLM-9B](https://huggingface.co/utter-project/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 + + +![image/png](https://cdn-uploads.huggingface.co/production/uploads/63f33ecc0be81bdc5d903466/ob_1sLM8c7dxuwpv6AAHA.png) +**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](https://arxiv.org/abs/2202.03799))). For Arc-challenge, Hellaswag, and MMLU we are using Okapi datasets ([Lai et al., 2023](https://aclanthology.org/2023.emnlp-demo.28/)) which include 11 languages. For MMLU-Pro and MUSR we translate the English version with Tower ([Alves et al., 2024](https://arxiv.org/abs/2402.17733)) 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 + + +![image/png](https://cdn-uploads.huggingface.co/production/uploads/63f33ecc0be81bdc5d903466/EfilsW_p-JA13mV2ilPkm.png) + +**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). \ No newline at end of file