What is the hidden dimension of LLAMA-2 70b?
What is the hidden dimension of LLAMA-2 70b?
The hidden dimension of LLAMA-2 70B (LLaMA stands for Large Language Model Meta AI) is 8192. This refers to the dimensionality of the hidden layers in the transformer architecture used in this model.
Testing if the previous output contains the string 8,192
: False
Testing if the previous output contains the string 8192
: True
What is the hidden dimension of LLAMA-2 70b?
What is the hidden dimension of LLAMA-2 70b?
The hidden dimension of the LLAMA-2 70B model is 8192. This parameter is part of the architecture that defines the size of the hidden layers within the model, contributing to its overall capacity and performance.
Testing if the previous output contains the string 8,192
: False
Testing if the previous output contains the string 8192
: True
What is the hidden dimension of LLAMA-2 70b?
What is the hidden dimension of LLAMA-2 70b?
The hidden dimension of LLAMA-2 70B is 8192. This refers to the size of the hidden layers in the transformer architecture used in the model.
Testing if the previous output contains the string 8,192
: False
Testing if the previous output contains the string 8192
: True
What is the hidden dimension of LLAMA-2 70b?
What is the hidden dimension of LLAMA-2 70b?
The hidden dimension of LLAMA-2 70B, which is a large language model developed by Meta AI, is 8192. This refers to the size of the hidden layers within the transformer architecture of the model. This large hidden dimension is one of the key factors contributing to the model's ability to process and generate complex language patterns.
Testing if the previous output contains the string 8,192
: False
Testing if the previous output contains the string 8192
: True
What is the hidden dimension of LLAMA-2 70b?
What is the hidden dimension of LLAMA-2 70b?
The hidden dimension of LLAMA-2 70b, which refers to the size of the internal representation in the model, is 8192. This is an important parameter as it determines the size of the vectors used in the internal layers of the model, affecting both the model's capacity to learn and its computational requirements.
Testing if the previous output contains the string 8,192
: False
Testing if the previous output contains the string 8192
: True