Shuffle

- Utilize efficient libraries and frameworks that are optimized for CPU usage, such as TensorFlow or PyTorch. ### Example Implementation Here's an example of how you can fine-tune Llama 2 13B on a CPU with these strategies: #### 1. Lo…

return len(self.contexts) # Create dataset and data loader dataset = ContextDataset(contexts, labels) data_loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True) ``` Can someone help me fine-tune this model for …

truncation=True, return_attention_mask=True, return_tensors='pt' ) return { 'query': query_encoding, 'passage': passage_encoding } def __len__(self): …

2. **Check Data Loading Logic**: Ensure that your data loading logic correctly handles batching and does not produce incomplete or inconsistent batches. 3. **Use Fixed Batch Sizes**: If possible, use a fixed batch size to avoid dynamic chan…

- The `allowed_exceptions` parameter allows you to specify which exceptions should trigger a retry. By default, it catches all exceptions, but you can customize it to catch only specific exceptions like `MetricCalcError`. - The `time.sleep`…

2. **Accuracy Score**: This is a metric from `sklearn.metrics` that computes the accuracy of the model's predictions. It is the ratio of the number of correct predictions to the total number of predictions. 3. **Cross-validation Function**…

from torch.utils.data import Dataset, DataLoader import logging import json from cryptography.fernet import Fernet # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', …