{"id":27007282,"url":"https://github.com/vila-lab/mobile-mmlu","last_synced_at":"2025-06-10T21:04:11.558Z","repository":{"id":269744233,"uuid":"908246109","full_name":"VILA-Lab/Mobile-MMLU","owner":"VILA-Lab","description":"Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmark. 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Our benchmark is redefining mobile intelligence evaluation for a smarter future, with a focus on real-world applicability and performance metrics that matter in mobile environments.\n\nWe also introduce the **Mobile-MMLU-Pro**, which is a more compact and sophisticated version of Mobile-MMLU. You can look at both datasets at the Hugging Face ([Mobile-MMLU](https://huggingface.co/datasets/MBZUAI-LLM/Mobile-MMLU), [Mobile-MMLU-Pro](https://huggingface.co/datasets/MBZUAI-LLM/Mobile-MMLU-Pro)).\n\n## Key Features\n\n- **Comprehensive Coverage**: Spans 80 distinct fields with carefully curated questions\n- **Mobile-Optimized**: Specifically designed for evaluating mobile-compatible LLMs\n- **16,186 Questions**: Extensive dataset including scenario-based questions\n- **Rigorous Evaluation**: Systematic assessment of performance, efficiency, and accuracy\n- **Real-world Applications**: Focus on practical use cases in everyday scenarios\n\n## Leaderboard\n\nVisit our [live leaderboard](https://huggingface.co/spaces/SondosMB/Mobile-MMLU) to see the latest performance rankings of various mobile LLMs across different categories and metrics.\n\n## Getting Started\n\n### Backends\n\nWe currently support the following `backends` for model inference:\n\n* `hf`: [HF Tranformers](https://github.com/huggingface/transformers)\n* `gptqmodel`: [GPTQModel](https://github.com/ModelCloud/GPTQModel) for gptq quantized models\n\n### Response Generation\n\n1. Install required packages:\n```bash\npip install torch transformers datasets pandas tqdm\n```\n\n1. Generate responses using your model:\n```bash\npython generate_answers.py \\\n    --model_name your_model_name \\\n    --batch_size 32 \\\n    --device cuda\n```\n\nThe script supports various arguments:\n- `--model_name`: Name or path of the model (required)\n- `--batch_size`: Batch size for processing (default: 32)\n- `--device`: Device to run the model on (default: `auto` = use cuda if available else cpu)\n- `--backend`: Load Model on (default: `hf`). Use `gptqmodel` for gptq quantized models.\n\n### Response Format\n\nThe script will generate a CSV file with the following format:\n```csv\nquestion_id,predicted_answer\nq1,A\nq2,B\nq3,C\n...\n```\n\nEach row contains:\n- `question_id`: The unique identifier for each question\n- `predicted_answer`: The model's prediction (A, B, C, or D)\n\n### Submission\n\n1. After generating the CSV file with your model's predictions, submit it through our evaluation portal at [link](https://huggingface.co/spaces/SondosMB/Mobile-MMLU)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvila-lab%2Fmobile-mmlu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvila-lab%2Fmobile-mmlu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvila-lab%2Fmobile-mmlu/lists"}