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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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library_name: transformers |
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base_model: utter-project/EuroLLM-9B-Instruct |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/EuroLLM-9B-Instruct-Q4_K_M-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. |
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Refer to the [original model card](https://huggingface.co/utter-project/EuroLLM-9B-Instruct) for more details on the model. |
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--- |
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Model details: |
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- |
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This is the model card for EuroLLM-9B-Instruct. You can also check the pre-trained version: EuroLLM-9B. |
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Developed by: Unbabel, Instituto Superior Técnico, |
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Instituto de Telecomunicações, University of Edinburgh, Aveni, |
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University of Paris-Saclay, University of Amsterdam, Naver Labs, |
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Sorbonne Université. |
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Funded by: European Union. |
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Model type: A 9B parameter multilingual transfomer LLM. |
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Language(s) (NLP): Bulgarian, Croatian, Czech, |
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Danish, Dutch, English, Estonian, Finnish, French, German, Greek, |
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Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, |
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Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, |
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Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, |
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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 |
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of understanding and generating text in all European Union languages as |
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well as some additional relevant languages. |
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EuroLLM-9B is a 9B parameter model trained on 4 trillion tokens divided |
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across the considered languages and several data sources: Web data, |
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parallel data (en-xx and xx-en), and high-quality datasets. |
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EuroLLM-9B-Instruct was further instruction tuned on EuroBlocks, an |
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instruction tuning dataset with focus on general instruction-following |
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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 |
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it has been shown to increase speed at inference time while maintaining |
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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 |
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these have been shown to lead to good performances while allowing the |
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extension of the context length. |
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For pre-training, we use 400 Nvidia H100 GPUs of the Marenostrum 5 |
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supercomputer, training the model with a constant batch size of 2,800 |
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sequences, which corresponds to approximately 12 million tokens, using |
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the Adam optimizer, and BF16 precision. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/EuroLLM-9B-Instruct-Q4_K_M-GGUF --hf-file eurollm-9b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/EuroLLM-9B-Instruct-Q4_K_M-GGUF --hf-file eurollm-9b-instruct-q4_k_m.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/EuroLLM-9B-Instruct-Q4_K_M-GGUF --hf-file eurollm-9b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/EuroLLM-9B-Instruct-Q4_K_M-GGUF --hf-file eurollm-9b-instruct-q4_k_m.gguf -c 2048 |
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``` |
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