--- base_model: utter-project/EuroLLM-9B-Instruct --- This is a quantization of the [EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct). 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. ## Evaluations This model provides an accuracy recovery of 99.61%. | __English__ | __[EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)__ | __[EuroLLM-9B-Instruct-FP8-Dynamic (this)](https://huggingface.co/cortecs/EuroLLM-9B-Instruct-FP8-Dynamic)__ | |:--------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------| | Avg. | 66.35 | 65.35 | | Arc | 63.3 | 61.7 | | Hellaswag | 69.4 | 69.0 | | | | | | __French__ | __[EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)__ | __[EuroLLM-9B-Instruct-FP8-Dynamic (this)](https://huggingface.co/cortecs/EuroLLM-9B-Instruct-FP8-Dynamic)__ | | Avg. | 61.67 | 61.3 | | Arc | 58.1 | 57.3 | | Hellaswag | 70.2 | 70.3 | | MMLU | 56.7 | 56.3 | | | | | | __German__ | __[EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)__ | __[EuroLLM-9B-Instruct-FP8-Dynamic (this)](https://huggingface.co/cortecs/EuroLLM-9B-Instruct-FP8-Dynamic)__ | | Avg. | 60.0 | 60.37 | | Arc | 57.2 | 56.7 | | Hellaswag | 66.3 | 67.1 | | MMLU | 56.5 | 57.3 | | | | | | __Italian__ | __[EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)__ | __[EuroLLM-9B-Instruct-FP8-Dynamic (this)](https://huggingface.co/cortecs/EuroLLM-9B-Instruct-FP8-Dynamic)__ | | Avg. | 61.8 | 61.7 | | Arc | 58.3 | 58.2 | | Hellaswag | 69.9 | 69.4 | | MMLU | 57.2 | 57.5 | | | | | | __Portuguese__ | __[EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)__ | __[EuroLLM-9B-Instruct-FP8-Dynamic (this)](https://huggingface.co/cortecs/EuroLLM-9B-Instruct-FP8-Dynamic)__ | | Avg. | 61.47 | 61.37 | | Arc | 59.1 | 59.3 | | Hellaswag | 70.3 | 70.2 | | MMLU | 55.0 | 54.6 | | | | | | __Spanish__ | __[EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)__ | __[EuroLLM-9B-Instruct-FP8-Dynamic (this)](https://huggingface.co/cortecs/EuroLLM-9B-Instruct-FP8-Dynamic)__ | | Avg. | 62.03 | 61.53 | | Arc | 59.7 | 59.3 | | Hellaswag | 71.4 | 71 | | MMLU | 55 | 54.3 | We did not check for data contamination. Evaluation was done using [Eval. Harness](https://github.com/EleutherAI/lm-evaluation-harness) with `limit=1000`. ## Usage Install **vLLM** and run the [server](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#openai-compatible-server): ``` python -m vllm.entrypoints.openai.api_server --model cortecs/EuroLLM-9B-Instruct-FP8-Dynamic --gpu-memory-util 0.95 ``` Access the model: ``` curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d ' { "model": "cortecs/EuroLLM-9B-Instruct-FP8-Dynamic", "prompt": "San Francisco is a" } ' ``` ⚡ This model is optimized to handle heavy workloads providing a total throughput of ️**1976 tokens per second** using one NVIDIA L4 ⚡