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Which Python library is specifically designed for working with large language models (LLMs)?
Correct Answer: C
The HuggingFace Transformers library is specifically designed for working with large languagemodels (LLMs), providing tools for model training, fine-tuning, and inference with transformer-based architectures (e. g., BERT, GPT, T5). NVIDIA's NeMo documentation often references HuggingFace Transformers for NLP tasks, as it supports integration with NVIDIA GPUs and frameworks like PyTorch for optimized performance. Option A (NumPy) is for numerical computations, not LLMs. Option B (Pandas) is for data manipulation, not model-specific tasks. Option D (Scikit-learn) is for traditional machine learning, not transformer-based LLMs. References: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index
