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V100 with NVLink is a good choice for fine-tuning of diffusion and transformer models, e.g. Llama2 and Stable Diffusion, as well as non-transofrmer models like Resnet-50 and Wav2Vec2.
Cost effective for fine-tuning of conventional models.
Great for domains where CNN, RNN models are popular, e. g. computer vision or medical diagnostics.
Best choice if speed is your top priority.
Perfect for bigNLP, LLM, and all models with Transformer architecture.
The world’s most powerful GPU for supercharging AI and HPC workloads coming soon!
The process of developing machine learning models can be categorized into two main approaches: model training and fine-tuning. While model training involves building a model from scratch, fine-tuning adjusts an existing, pre-trained model to meet specific requirements. Here’s a practical comparison of model fine-tuning versus model training:
Fine-tuning involves adjusting the parameters of a model to enhance its performance. For instance, in a neural network, modifying the learning rate or batch size can significantly improve accuracy. A common approach is to use grid or random search methods to identify the optimal hyperparameters for a given classification task.
Fine-tuning pre-trained models is a common practice for adapting them to new tasks. For example, you can utilize a model trained on a large image dataset to enhance performance on a smaller, specialized image classification task.
Enhance your training dataset by generating modified versions of data points. In image processing, this could involve rotating, flipping, or adding noise to images. By doing so, you can create a more robust model and reduce the risk of overfitting.
Techniques such as L1 or L2 regularization effectively prevent overfitting by penalizing complex models. By incorporating a regularization term into the loss function, these techniques help maintain model simplicity, resulting in improved generalization and better performance on unseen data.
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