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pytorch_model.bin
pytorch_model.bin is a binary file that contains the serialized state_dict of a PyTorch model. The state_dict is a dictionary object that maps the names of model parameters to their corresponding tensors, and it is the primary way that PyTorch models are stored and loaded.
The state_dict is a nested dictionary structure where the keys are the names of the model parameters and the values are PyTorch tensors that contain the values of those parameters. The state_dict dictionary is hierarchical, with the top-level keys being the names of the model layers or modules, and the values being nested dictionaries that contain the parameter names and tensor values for each layer/module.
For example, suppose we have a simple PyTorch model with two layers, named fc1 and fc2, each with a weight tensor and a bias tensor. The state_dict dictionary for this model might look like this:
{
'fc1.weight': tensor([...]),
'fc1.bias': tensor([...]),
'fc2.weight': tensor([...]),
'fc2.bias': tensor([...])
}
In this example, the top-level keys are the names of the layers, and the nested dictionaries contain the parameter names and tensor values for each layer.
When the PyTorch model is saved to a pytorch_model.bin file, the state_dict is serialized and written to the file as binary data. When the model is loaded from the file, the binary data is deserialized and the state_dict is reconstructed as a dictionary object in memory.
How many layers are in gpt-j-6b pytorch_model.bin?
The pytorch_model.bin file for the GPT-J-6B model contains the serialized state_dict for the model, which includes the values of all the parameters for each layer in the model.
Specifically, the GPT-J-6B model has 28 transformer layers, each with 6 attention heads and a feedforward neural network layer. Additionally, there are embedding layers for the input tokens, positional encodings, and token type embeddings.
Therefore, the pytorch_model.bin file for the GPT-J-6B model contains the weights and biases for all these layers, resulting in a very large file size of several gigabytes.
Each layer consists of one feedforward block and one self attention block.
Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3.
What is it mean by 'model dimension' of 4096?
"model dimension" typically refers to the size of the hidden state vector used by the model's transformer layers.
The hidden state vector used by the transformer layers has a fixed dimensionality, which is typically denoted by n. For example, in GPT-J-6B, the model dimension is 4096, meaning that the hidden state vector has 4096 dimensions.
The model dimension determines the number of parameters in the model and also affects the expressive power of the model. A larger model dimension allows the model to capture more complex patterns in the input data, but it also requires more computation and memory to train and use the model.
Here hidden state vector has a dimension of 4096. This means that the vector has 4096 elements, each of which represents a different aspect of the input sequence. The vector is updated as the model processes each token in the input sequence, with each update capturing the model's evolving understanding of the sequence.