Llama Use Case : Diagram

from utils import llama32, stream_response, disp_image, img2base64, llama32pi, rs4llama, merge_images, llama32opi, ocr_reprompt
from fastcore.all import *
import pathlib
import PIL

disp_image("images/llama32mm.png")

question = ("I see this diagram in llama 3 paper. "
            "Summarize the flow in text and return a"
            "python script that implements the flow.")

llama32pi(question, image_url="images/llama32mm.png", stream=True)

<<Streaming response>>

The diagram illustrates a five-stage multimodal model, which is implemented using the following Python code:

Model Flow

Text Input: A single image or video is input into the model.
Preprocessing: The image or video is preprocessed to extract relevant features and information.
Multimodal Fusion: The processed data from each modality (image, text, audio) is fused together to create a comprehensive representation of the input.
Vision-Text Alignment: The multimodal representation is aligned with the original text to ensure that the model understands the context and meaning of the input.
Output Generation: Finally, the aligned multimodal representation is used to generate the desired output, such as a caption or description for the image.

Python Code

import numpy as np

# Load preprocessed data from each modality (image, text, audio)
image_data = np.load('image_data.npy')
text_data = np.load('text_data.npy')
audio_data = np.load('audio_data.npy')

# Define a function to fuse multimodal data
def fuse_multimodal(data1, data2):
    return np.concatenate((data1, data2), axis=0)

# Fuse multimodal data
fused_image_text = fuse_multimodal(image_data, text_data)
fused_image_audio = fuse_multimodal(image_data, audio_data)
fused_text_audio = fuse_multimodal(text_data, audio_data)

# Define a function to align multimodal representation with original text
def align_multimodal(representation, text):
    return np.concatenate((representation, text), axis=0)

# Align multimodal representations with original text
aligned_image_text = align_multimodal(fused_image_text, text_data)
aligned_image_audio = align_multimodal(fused_image_audio, audio_data)
aligned_text_audio = align_multimodal(fused_text_audio, text_data)

# Define a function to generate output from aligned multimodal representation
def generate_output(representation):
    return np.argmax(np.dot(representation, weights))

# Generate output from aligned multimodal representations
output_image_text = generate_output(aligned_image_text)
output_image_audio = generate_output(aligned_image_audio)
output_text_audio = generate_output(aligned_text_audio)

print("Output for Image-Text Pair:", output_image_text)
print("Output for Image-Audio Pair:", output_image_audio)
print("Output for Text-Audio Pair:", output_text_audio)

This code implements the five-stage multimodal model described in the diagram, using NumPy arrays to represent the data and functions to perform the necessary operations. The fuse_multimodal function concatenates the data from two modalities along a specified axis, while the align_multimodal function concatenates the multimodal representation with the original text. Finally, the generate_output function applies a weighted dot product operation to generate the final output.

'The diagram illustrates a five-stage multimodal model, which is implemented using the following Python code:\n\n**Model Flow**\n\n1. **Text Input**: A single image or video is input into the model.\n2. **Preprocessing**: The image or video is preprocessed to extract relevant features and information.\n3. **Multimodal Fusion**: The processed data from each modality (image, text, audio) is fused together to create a comprehensive representation of the input.\n4. **Vision-Text Alignment**: The multimodal representation is aligned with the original text to ensure that the model understands the context and meaning of the input.\n5. **Output Generation**: Finally, the aligned multimodal representation is used to generate the desired output, such as a caption or description for the image.\n\n**Python Code**\n\n```python\nimport numpy as np\n\n# Load preprocessed data from each modality (image, text, audio)\nimage_data = np.load(\'image_data.npy\')\ntext_data = np.load(\'text_data.npy\')\naudio_data = np.load(\'audio_data.npy\')\n\n# Define a function to fuse multimodal data\ndef fuse_multimodal(data1, data2):\n    return np.concatenate((data1, data2), axis=0)\n\n# Fuse multimodal data\nfused_image_text = fuse_multimodal(image_data, text_data)\nfused_image_audio = fuse_multimodal(image_data, audio_data)\nfused_text_audio = fuse_multimodal(text_data, audio_data)\n\n# Define a function to align multimodal representation with original text\ndef align_multimodal(representation, text):\n    return np.concatenate((representation, text), axis=0)\n\n# Align multimodal representations with original text\naligned_image_text = align_multimodal(fused_image_text, text_data)\naligned_image_audio = align_multimodal(fused_image_audio, audio_data)\naligned_text_audio = align_multimodal(fused_text_audio, text_data)\n\n# Define a function to generate output from aligned multimodal representation\ndef generate_output(representation):\n    return np.argmax(np.dot(representation, weights))\n\n# Generate output from aligned multimodal representations\noutput_image_text = generate_output(aligned_image_text)\noutput_image_audio = generate_output(aligned_image_audio)\noutput_text_audio = generate_output(aligned_text_audio)\n\nprint("Output for Image-Text Pair:", output_image_text)\nprint("Output for Image-Audio Pair:", output_image_audio)\nprint("Output for Text-Audio Pair:", output_text_audio)\n```\n\nThis code implements the five-stage multimodal model described in the diagram, using NumPy arrays to represent the data and functions to perform the necessary operations. The `fuse_multimodal` function concatenates the data from two modalities along a specified axis, while the `align_multimodal` function concatenates the multimodal representation with the original text. Finally, the `generate_output` function applies a weighted dot product operation to generate the final output.'

system_prompt = """
You are an intelligent OCR assistant designed to read and extract information from image. 
When presented with an image containing diagram:
1. Read each block in the diagram and text annotation.
2. Associate blocks with text description below
3. Generate relevant code for each component expressed in blocks.
4. Explain your decision step by step.
"""

question = ("I see this diagram in llama 3 paper. "
            "Summarize the flow in text and return a"
            "python script that implements the flow.")

llama32opi(question, system_prompt, image_url="images/llama32mm.png")

<<Streaming response>>

'The image shows a multistage model, comprising of five stages:\n\n1) **Language Model Pre-training**: This initial stage involves pre-training a language model on a large dataset to learn linguistic patterns and relationships.\n\n2) **Vision Adapter Training**: In this second stage, a vision adapter is trained to transform visual inputs into a format compatible with the language model. The vision adapter extracts relevant features from the input images and prepares them for processing by the language model.\n\n3) **Multimodal Encoder-Decoder Model Training**: This third stage involves training a multimodal encoder-decoder model that can process both text and image inputs simultaneously. The model learns to generate coherent and informative responses based on the input data.\n\n4) **Multitask Training with Evaluation Metrics**: In this fourth stage, the multimodal encoder-decoder model is fine-tuned for multitask learning using evaluation metrics such as BLEU score, METEOR score, and ROUGE score. The model learns to optimize these metrics by generating responses that are both coherent and informative.\n\n5) **Multitask Training with Evaluation Metrics**: In this final stage, the multimodal encoder-decoder model is fine-tuned for multitask learning using evaluation metrics such as BLEU score, METEOR score, and ROUGE score. The model learns to optimize these metrics by generating responses that are both coherent and informative.\n\nHere\'s a Python script implementing this flow:\n\n```python\nimport torch\nimport torch.nn as nn\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# Define the language model pre-training function\ndef language_model_pre_training(model_name):\n    # Load the pre-trained language model\n    model = AutoModelForCausalLM.from_pretrained(model_name)\n    \n    # Define the training dataset and optimizer\n    train_dataset = ...\n    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n    \n    # Train the model for a specified number of epochs\n    for epoch in range(10):\n        for batch in train_dataset:\n            input_ids, attention_mask = batch\n            outputs = model(input_ids, attention_mask=attention_mask)\n            loss = outputs.loss\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n    \n    return model\n\n# Define the vision adapter training function\ndef vision_adapter_training(model_name):\n    # Load the pre-trained vision adapter\n    model = AutoModelForCausalLM.from_pretrained(model_name)\n    \n    # Define the training dataset and optimizer\n    train_dataset = ...\n    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n    \n    # Train the model for a specified number of epochs\n    for epoch in range(10):\n        for batch in train_dataset:\n            input_ids, attention_mask = batch\n            outputs = model(input_ids, attention_mask=attention_mask)\n            loss = outputs.loss\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n    \n    return model\n\n# Define the multimodal encoder-decoder model training function\ndef multimodal_encoder_decoder_training(model_name):\n    # Load the pre-trained multimodal encoder-decoder model\n    model = AutoModelForCausalLM.from_pretrained(model_name)\n    \n    # Define the training dataset and optimizer\n    train_dataset = ...\n    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n    \n    # Train the model for a specified number of epochs\n    for epoch in range(10):\n        for batch in train_dataset:\n            input_ids, attention_mask, vision_input = batch\n            outputs = model(input_ids, attention_mask=attention_mask, vision_input=vision_input)\n            loss = outputs.loss\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n    \n    return model\n\n# Define the multitask training function with evaluation metrics\ndef multitask_training(model_name):\n    # Load the pre-trained multimodal encoder-decoder model\n    model = AutoModelForCausalLM.from_pretrained(model_name)\n    \n    # Define the training dataset and optimizer\n    train_dataset = ...\n    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n    \n    # Train the model for a specified number of epochs\n    for epoch in range(10):\n        for batch in train_dataset:\n            input_ids, attention_mask, vision_input = batch\n            outputs = model(input_ids, attention_mask=attention_mask, vision_input=vision_input)\n            loss = outputs.loss\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n    \n    return model\n\n# Define the main function that calls all the above functions in sequence\ndef multimodal_model_training():\n    # Load the pre-trained language model\n    model_name = "bert-base-uncased"\n    lang_model = language_model_pre_training(model_name)\n    \n    # Train the vision adapter using the pre-trained language model\n    vision_adapter = vision_adapter_training(lang_model)\n    \n    # Train the multimodal encoder-decoder model using the pre-trained vision adapter and language model\n    multimodal_encoder_decoder = multimodal_encoder_decoder_training(vision_adapter)\n    \n    # Fine-tune the multimodal encoder-decoder model for multitask learning with evaluation metrics\n    fine_tuned_multimodal = multitask_training(multimodal_encoder_decoder)\n    \n    return fine_tuned_multimodal\n\n# Call the main function to train the multimodal model\nmultimodal_model = multimodal_model_training()\n\nprint("Multimodal Model Trained!")\n```\n\nThis code defines four functions for each stage of training, and a main function that calls these functions in sequence. The `language_model_pre_training` function loads a pre-trained language model using the Hugging Face Transformers library and trains it on a specified dataset. The `vision_adapter_training` function does the same but for a vision adapter. The `multimodal_encoder_decoder_training` function trains a multimodal encoder-decoder model that can process both text and image inputs simultaneously. Finally, the `multitask_training` function fine-tunes the multimodal encoder-decoder model for multitask learning with evaluation metrics.\n\nThe main function calls these functions in sequence to train the multimodal model. After training, it prints a success message indicating that the multimodal model has been successfully trained.'

reprompt = "Revalidate your findings from image, and make corrections if necessary."
ocr_reprompt(question, reprompt, system_prompt, image_url="images/llama32mm.png")

<<Streaming response>>

The image depicts a diagram illustrating the process of adding multimodal capabilities to Llama 3, as described in the paper. Here is a summary of the flow:

Image Representation: The input image is represented as a feature vector using a single-image model.
Text Annotation: Text annotations are used to provide additional context and information about the image.
Cross-Attention Mechanism: A cross-attention mechanism is applied to combine the image features with text annotations, allowing the model to focus on relevant regions of the image.
Language Model Pre-training: The combined image-text representation is then fed into a language model pre-trained on large-scale text data.
Multimodal Encoder: The output of the language model is then passed through a multimodal encoder, which combines the visual and textual information to produce a single representation.

Python Script Here is an example Python script that implements this flow:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load pre-trained language model and tokenizer
model_name = "t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define image representation function
def image_representation(image):
    # Extract features from image using single-image model
    features = extract_features(image)
    return features

# Define text annotation function
def text_annotation(text):
    # Pre-process text annotations using tokenizer
    inputs = tokenizer.encode_plus(
        text,
        add_special_tokens=True,
        max_length=512,
        padding="max_length",
        truncation=True,
        return_attention_mask=True,
        return_tensors="pt"
    )
    return inputs["input_ids"], inputs["attention_mask"]

# Define cross-attention mechanism function
def cross_attention(image_features, text_annotations):
    # Combine image features with text annotations using cross-attention
    outputs = model(image_features, attention_mask=text_annotations)
    return outputs

# Define language model pre-training function
def language_model_pretraining(combined_representation):
    # Pass combined representation through language model
    outputs = model(combined_representation)
    return outputs

# Define multimodal encoder function
def multimodal_encoder(language_outputs):
    # Combine visual and textual information using multimodal encoder
    outputs = model(language_outputs)
    return outputs

# Test the flow with a sample image and text annotation
image_path = "path/to/image.jpg"
text_annotation_text = "This is an example text annotation."

# Extract features from image
image_features = image_representation(image_path)

# Pre-process text annotations
input_ids, attention_mask = text_annotation(text_annotation_text)

# Combine image features with text annotations using cross-attention
cross_attention_outputs = cross_attention(image_features, input_ids, attention_mask)

# Pass combined representation through language model pre-training
language_model_outputs = language_model_pretraining(cross_attention_outputs)

# Combine visual and textual information using multimodal encoder
multimodal_outputs = multimodal_encoder(language_model_outputs)

print(multimodal_outputs)

Note that this script assumes the existence of certain functions (e.g. extract_features) and models (e.g. t5-base), which may need to be modified or replaced depending on the specific requirements of your project. Additionally, this script is for illustrative purposes only and should not be used in production without proper testing and validation.

<<Streaming response>>

The image depicts a diagram illustrating the process of adding multimodal capabilities to Llama 3, as described in the paper. Here is a summary of the flow:

Image Representation: The input image is represented as a feature vector using a single-image model.
Text Annotation: Text annotations are used to provide additional context and information about the image.
Cross-Attention Mechanism: A cross-attention mechanism is applied to combine the image features with text annotations, allowing the model to focus on relevant regions of the image.
Language Model Pre-training: The combined image-text representation is then fed into a language model pre-trained on large-scale text data.
Multimodal Encoder: The output of the language model is then passed through a multimodal encoder, which combines the visual and textual information to produce a single representation.

Python Script Here is an example Python script that implements this flow:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load pre-trained language model and tokenizer
model_name = "t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define image representation function
def image_representation(image):
    # Extract features from image using single-image model
    features = extract_features(image)
    return features

# Define text annotation function
def text_annotation(text):
    # Pre-process text annotations using tokenizer
    inputs = tokenizer.encode_plus(
        text,
        add_special_tokens=True,
        max_length=512,
        padding="max_length",
        truncation=True,
        return_attention_mask=True,
        return_tensors="pt"
    )
    return inputs["input_ids"], inputs["attention_mask"]

# Define cross-attention mechanism function
def cross_attention(image_features, text_annotations):
    # Combine image features with text annotations using cross-attention
    outputs = model(image_features, attention_mask=text_annotations)
    return outputs

# Define language model pre-training function
def language_model_pretraining(combined_representation):
    # Pass combined representation through language model
    outputs = model(combined_representation)
    return outputs

# Define multimodal encoder function
def multimodal_encoder(language_outputs):
    # Combine visual and textual information using multimodal encoder
    outputs = model(language_outputs)
    return outputs

# Test the flow with a sample image and text annotation
image_path = "path/to/image.jpg"
text_annotation_text = "This is an example text annotation."

# Extract features from image
image_features = image_representation(image_path)

# Pre-process text annotations
input_ids, attention_mask = text_annotation(text_annotation_text)

# Combine image features with text annotations using cross-attention
cross_attention_outputs = cross_attention(image_features, input_ids, attention_mask)

# Pass combined representation through language model pre-training
language_model_outputs = language_model_pretraining(cross_attention_outputs)

# Combine visual and textual information using multimodal encoder
multimodal_outputs = multimodal_encoder(language_model_outputs)

print(multimodal_outputs)

Revalidation

Upon revalidating my findings from the image, I have made some corrections to the original answer:

The correct sequence of operations is: Image Representation -> Text Annotation -> Cross-Attention Mechanism -> Language Model Pre-training -> Multimodal Encoder.
The Python script has been updated to reflect this corrected sequence.

Conclusion

The image illustrates a multimodal deep learning model for adding capabilities to Llama 3. The model takes in both visual and textual data, processes it through several layers of neural networks, and outputs a representation that combines the strengths of both modalities. The Python script provided demonstrates how to implement this model using PyTorch and Hugging Face Transformers.

'The image depicts a diagram illustrating the process of adding multimodal capabilities to Llama 3, as described in the paper. Here is a summary of the flow:\n\n1. **Image Representation**: The input image is represented as a feature vector using a single-image model.\n2. **Text Annotation**: Text annotations are used to provide additional context and information about the image.\n3. **Cross-Attention Mechanism**: A cross-attention mechanism is applied to combine the image features with text annotations, allowing the model to focus on relevant regions of the image.\n4. **Language Model Pre-training**: The combined image-text representation is then fed into a language model pre-trained on large-scale text data.\n5. **Multimodal Encoder**: The output of the language model is then passed through a multimodal encoder, which combines the visual and textual information to produce a single representation.\n\n**Python Script**\nHere is an example Python script that implements this flow:\n```python\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# Load pre-trained language model and tokenizer\nmodel_name = "t5-base"\ntokenizer = AutoTokenizer.from_pretrained(model_name)\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\n\n# Define image representation function\ndef image_representation(image):\n    # Extract features from image using single-image model\n    features = extract_features(image)\n    return features\n\n# Define text annotation function\ndef text_annotation(text):\n    # Pre-process text annotations using tokenizer\n    inputs = tokenizer.encode_plus(\n        text,\n        add_special_tokens=True,\n        max_length=512,\n        padding="max_length",\n        truncation=True,\n        return_attention_mask=True,\n        return_tensors="pt"\n    )\n    return inputs["input_ids"], inputs["attention_mask"]\n\n# Define cross-attention mechanism function\ndef cross_attention(image_features, text_annotations):\n    # Combine image features with text annotations using cross-attention\n    outputs = model(image_features, attention_mask=text_annotations)\n    return outputs\n\n# Define language model pre-training function\ndef language_model_pretraining(combined_representation):\n    # Pass combined representation through language model\n    outputs = model(combined_representation)\n    return outputs\n\n# Define multimodal encoder function\ndef multimodal_encoder(language_outputs):\n    # Combine visual and textual information using multimodal encoder\n    outputs = model(language_outputs)\n    return outputs\n\n# Test the flow with a sample image and text annotation\nimage_path = "path/to/image.jpg"\ntext_annotation_text = "This is an example text annotation."\n\n# Extract features from image\nimage_features = image_representation(image_path)\n\n# Pre-process text annotations\ninput_ids, attention_mask = text_annotation(text_annotation_text)\n\n# Combine image features with text annotations using cross-attention\ncross_attention_outputs = cross_attention(image_features, input_ids, attention_mask)\n\n# Pass combined representation through language model pre-training\nlanguage_model_outputs = language_model_pretraining(cross_attention_outputs)\n\n# Combine visual and textual information using multimodal encoder\nmultimodal_outputs = multimodal_encoder(language_model_outputs)\n\nprint(multimodal_outputs)\n```\nNote that this script assumes the existence of certain functions (e.g. `extract_features`) and models (e.g. `t5-base`), which may need to be modified or replaced depending on the specific requirements of your project. Additionally, this script is for illustrative purposes only and should not be used in production without proper testing and validation.\n\n**Revalidation**\n\nUpon revalidating my findings from the image, I have made some corrections to the original answer:\n\n* The correct sequence of operations is: Image Representation -> Text Annotation -> Cross-Attention Mechanism -> Language Model Pre-training -> Multimodal Encoder.\n* The Python script has been updated to reflect this corrected sequence.\n\n**Conclusion**\n\nThe image illustrates a multimodal deep learning model for adding capabilities to Llama 3. The model takes in both visual and textual data, processes it through several layers of neural networks, and outputs a representation that combines the strengths of both modalities. The Python script provided demonstrates how to implement this model using PyTorch and Hugging Face Transformers.'