Jamba

PyTorch-Implementierung von Jamba: „Jamba: Ein hybrides Transformer-Mamba-Sprachmodell“

installieren

$ pip install jamba

Verwendung

 # Import the torch library, which provides tools for machine learning
import torch

# Import the Jamba model from the jamba.model module
from jamba . model import Jamba

# Create a tensor of random integers between 0 and 100, with shape (1, 100)
# This simulates a batch of tokens that we will pass through the model
x = torch . randint ( 0 , 100 , ( 1 , 100 ))

# Initialize the Jamba model with the specified parameters
# dim: dimensionality of the input data
# depth: number of layers in the model
# num_tokens: number of unique tokens in the input data
# d_state: dimensionality of the hidden state in the model
# d_conv: dimensionality of the convolutional layers in the model
# heads: number of attention heads in the model
# num_experts: number of expert networks in the model
# num_experts_per_token: number of experts used for each token in the input data
model = Jamba (
    dim = 512 ,
    depth = 6 ,
    num_tokens = 100 ,
    d_state = 256 ,
    d_conv = 128 ,
    heads = 8 ,
    num_experts = 8 ,
    num_experts_per_token = 2 ,
)

# Perform a forward pass through the model with the input data
# This will return the model's predictions for each token in the input data
output = model ( x )

# Print the model's predictions
print ( output )