What is the hidden dimension of LLAMA-2 70b?
What is the hidden dimension of LLAMA-2 70b?
The hidden dimension (also called hidden size or model dimension) for the LLAMA-2 70B model is 8192. This refers to the size of the hidden state vectors used throughout the model's transformer layers. It's a key hyperparameter that significantly influences the model's capacity and computational requirements. For context, here are the hidden dimensions for the different sizes of LLAMA-2 models: - LLAMA-2 7B: 4096 - LLAMA-2 13B: 5120 - LLAMA-2 70B: 8192 The larger hidden dimension of the 70B model contributes to its increased capacity and performance compared to the smaller variants, but also requires significantly more computational resources to train and run.
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 (also called hidden size or embedding dimension) of LLaMA-2 70B is 8192. This refers to the size of the hidden state vectors in the model's transformer layers. It's a key hyperparameter that significantly influences the model's capacity and computational requirements. For context, here are the hidden dimensions for different sizes of LLaMA-2 models: - LLaMA-2 7B: 4096 - LLaMA-2 13B: 5120 - LLaMA-2 70B: 8192 The larger hidden dimension of the 70B model contributes to its increased capacity and performance compared to the smaller variants, but also results in higher computational and memory requirements for training and inference.
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 (also called the hidden size or embedding dimension) of LLaMA-2 70B is 8192. This refers to the size of the hidden state vectors used in the model's transformer layers. It's one of the key hyperparameters that determines the model's capacity and computational requirements. For context, here are the hidden dimensions for the different sizes of LLaMA-2 models: - LLaMA-2 7B: 4096 - LLaMA-2 13B: 5120 - LLaMA-2 70B: 8192 The larger hidden dimension of the 70B model contributes to its increased capacity and performance, but also requires significantly more computational resources to train and run compared to the smaller variants.
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 (also called model dimension or embedding dimension) for LLAMA-2 70B is 8192. This refers to the size of the hidden state vectors used throughout the model's transformer layers. It's a key hyperparameter that significantly influences the model's capacity and computational requirements. For context, here are the hidden dimensions for other LLAMA-2 model sizes: - LLAMA-2 7B: 4096 - LLAMA-2 13B: 5120 - LLAMA-2 70B: 8192 The larger hidden dimension of the 70B model contributes to its increased capacity and performance compared to the smaller variants, but also results in much higher computational and memory requirements.
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 (also called hidden size or embedding dimension) of LLAMA-2 70B is 8192. This refers to the size of the hidden state vectors used in the model's transformer layers. It's a key hyperparameter that significantly influences the model's capacity and computational requirements. For context, here are the hidden dimensions for different sizes of LLAMA-2 models: - LLAMA-2 7B: 4096 - LLAMA-2 13B: 5120 - LLAMA-2 70B: 8192 The larger hidden dimension of the 70B model contributes to its increased capacity and performance compared to the smaller variants, but also requires substantially more computational resources to train and run.
Testing if the previous output contains the string 8,192: False
Testing if the previous output contains the string 8192: True