[V][README]

README.md

@@ -18,55 +18,35 @@ This capability is particularly beneficial for tasks that involve substantial da
 
 ```python
 # Import the necessary libraries
-import torch  # Import the torch library
-
-# Import the MultiModalMamba model from the mm_mamba module
-from mm_mamba import MultiModalMamba
-
-# Generate a random tensor 'x' of size (1, 224) with random elements between 0 and 10000
-x = torch.randint(0, 10000, (1, 196))
-
-# Generate a random image tensor 'img' of size (1, 3, 224, 224)
-img = torch.randn(1, 3, 224, 224)
-
-# Audio tensor 'aud' of size 2d
-aud = torch.randn(1, 224)
-
-# Video tensor 'vid' of size 5d - (batch_size, channels, frames, height, width)
-vid = torch.randn(1, 3, 16, 224, 224)
-
-# Create a MultiModalMamba model object with the following parameters:
-model = MultiModalMamba(
-    vocab_size=10000,
-    dim=512,
-    depth=6,
-    dropout=0.1,
-    heads=8,
-    d_state=512,
-    image_size=224,
-    patch_size=16,
-    encoder_dim=512,
-    encoder_depth=6,
-    encoder_heads=8,
+import torch 
+from torch import nn
+from mm_mamba import MultiModalMambaBlock
+
+# Create some random input tensors
+x = torch.randn(1, 16, 64)  # Tensor with shape (batch_size, sequence_length, feature_dim)
+y = torch.randn(1, 3, 64, 64)  # Tensor with shape (batch_size, num_channels, image_height, image_width)
+
+# Create an instance of the MultiModalMambaBlock model
+model = MultiModalMambaBlock(
+    dim = 64,  # Dimension of the token embeddings
+    depth = 5,  # Number of Mamba layers
+    dropout = 0.1,  # Dropout probability
+    heads = 4,  # Number of attention heads
+    d_state = 16,  # Dimension of the state embeddings
+    image_size = 64,  # Size of the input image
+    patch_size = 16,  # Size of each image patch
+    encoder_dim = 64,  # Dimension of the encoder token embeddings
+    encoder_depth = 5,  # Number of encoder transformer layers
+    encoder_heads = 4,  # Number of encoder attention heads
     fusion_method="mlp",
-    return_embeddings=False,
-    post_fuse_norm=True,
 )
 
-# Pass the tensor 'x' and 'img' through the model and store the output in 'out'
-out = model(x, img, aud, vid)
+# Pass the input tensors through the model
+out = model(x, y)
 
-# Print the shape of the output tensor 'out'
+# Print the shape of the output tensor
 print(out.shape)
 
-
-# After much training
-
-model.eval()
-
-# Generate text
-model.generate(text)
-
 ```
 
 
@@ -89,6 +69,12 @@ x = torch.randint(0, 10000, (1, 196))
 # Generate a random image tensor 'img' of size (1, 3, 224, 224)
 img = torch.randn(1, 3, 224, 224)
 
+# Audio tensor 'aud' of size 2d
+aud = torch.randn(1, 224)
+
+# Video tensor 'vid' of size 5d - (batch_size, channels, frames, height, width)
+vid = torch.randn(1, 3, 16, 224, 224)
+
 # Create a MultiModalMamba model object with the following parameters:
 model = MultiModalMamba(
     vocab_size=10000,
@@ -108,22 +94,11 @@ model = MultiModalMamba(
 )
 
 # Pass the tensor 'x' and 'img' through the model and store the output in 'out'
-out = model(x, img)
+out = model(x, img, aud, vid)
 
 # Print the shape of the output tensor 'out'
 print(out.shape)
 
-
-# After much training
-model.eval()
-
-# Tokenize texts
-text_tokens = tokenize(text)
-
-# Send text tokens to the model
-logits = model(text_tokens)
-
-text = detokenize(logits)
 ```
 
 # Real-World Deployment

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "mmm-zeta"
-version = "0.1.0"
+version = "0.1.1"
 description = "MultiModalMamba - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]