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  Photo by MART PRODUCTION at pexel If you’re interested in customizing an agent for a specific task, one way to do this is to fine-tune the models on your dataset.  For preparing dataset you can see this article . 1. Curate the Dataset - Using NeMo Curator:   - Install NVIDIA NeMo: `pip install nemo_toolkit`   - Use NeMo Curator to prepare your dataset according to your specific requirements. 2. Fine-Tune the Model - Using NeMo Framework:   1. Setup NeMo:      ```python      import nemo      import nemo.collections.nlp as nemo_nlp      ```   2. Prepare the Data:      ```python      # Example to prepare dataset      from nemo.collections.nlp.data.text_to_text import TextToTextDataset      dataset = TextToTextDataset(file_path="path_to_your_dataset")      ```   3. Fine-Tune the Model:      ```python ...

  Explore To install NVIDIA CUDA with your GeForce 940MX GPU and Intel Core i7 processor, follow these steps: Verify GPU Compatibility : First, ensure that your GPU (GeForce 940MX) is supported by CUDA. According to the NVIDIA forums, the 940MX is indeed supported 1 . You can also check the official NVIDIA specifications for the GeForce 940MX, which confirms its CUDA support 2 . System Requirements : To use CUDA on your system, you’ll need the following installed: A CUDA-capable GPU (which you have) A supported version of Windows (e.g., Windows 10, Windows 11) NVIDIA CUDA Toolkit (available for download from the NVIDIA website 3 ) Download and Install CUDA Toolkit : Visit the NVIDIA CUDA Toolkit download page and select the appropriate version for your system. Follow the installation instructions provided on the page. Make sure to choose the correct version for your operating system. Test the Installation : After installation, verify that CUDA is working correctly: Open a command ...

  Photo by  Izabel 🏳️‍🌈  on  Unsplash Nvidia reached $1 trillion as first chip maker. We all know that this company famous for its GPU and AI processing cheaps. NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics. Let quickly know about the main technologies behind it. Both NAND flash memory and DRAM (Dynamic Random Access Memory) play important roles in supporting GPU (Graphics Processing Unit) and AI (Artificial Intelligence) applications. NAND Flash Memory: NAND flash memory is used in GPU and AI systems for long-term data storage and retrieval. It provides high-capacity, non-volatile storage that retains data even when the power is turned off. GPUs and AI systems often require large amounts of data storage for training datasets, model parameters, and intermediate results. NAND flash memory, such as SSDs (Solid-State Drives), is commonly used to store this data. The fast read speeds of NAND flash memo...