Return Value and Parameters of np.arange()NumPy arange() is one of the array creation routines based on numerical ranges. It creates an instance of ndarray with evenly spaced values and returns the reference to it.You can define the interval of the values contained in an array, space ...
2.In the datasets.py file, update the LoadImagesAndLabels class. Replace the image loading logic with code to load .npy files using np.load(). 3.Modify the load_image() function in the utils\dataloaders..py file to handle .npy files appropriately. You can use np.load() to load the ...
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NumPy arange(): How to Use np.arange() If you want to learn pandas, then the official Getting Started page is an excellent place to begin. The introduction to data structures can help you learn about the fundamental data types, Series and DataFrame. Likewise, the excellent official introducto...
This parameter is an integer that specifies the dimension of the embedding vector. You can refer to the MockEmbedding class in the llama_index/token_counter/mock_embed_model.py file for more details. The default embedding dimension used in the LlamaIndex framework is 1536. This is indicated ...
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import numpy as np This will enable you to refer to NumPy asnpwhen when you call the concatenate function. Concatenate two numpy arrays First, let’s just concatenate together two simple NumPy arrays. Create numpy arrays To do this, we’ll first create two NumPy arrayswith the np.array fun...
import numpy as np import torch import argparse from transformers import LlamaForCausalLM, LlamaTokenizer from torch.utils.data import DataLoader, TensorDataset from neural_compressor.adaptor.torch_utils.waq import TorchSmoothQuant def main(): args = parser.parse_args() model_path='weights/llama-2-...