This FAQ stems from my past interactions with GenAI tools to grasp the fundamentals of GenAI/Foundation models. The questions were posed by me, and the responses are influenced by GenAI tools such as ChatGPT, Claude V2, Bard, and others. I'm sharing this with the hope that it might benefit others. Here are a few of the frequently asked questions, though there are many more..
What are respective foundation models based on different types of content?
Generative AI refers to a class of machine learning models that are capable of generating new data that resembles the data they have been trained on. These models can be used to create a wide variety of content, including text, images, music, and much more.
Text and Language (Large Language Model)
GPT (Generative Pre-trained Transformer): Developed by OpenAI, it's designed for various tasks like text generation, translation, question answering, and summarization.
BERT (Bidirectional Encoder Representations from Transformers): By Google, primarily for understanding the context of words in search queries.
T5 (Text-to-Text Transfer Transformer): Also by Google, it treats every NLP task as a text-to-text task.
Images
DCGAN (Deep Convolutional Generative Adversarial Network): A pioneer in generating high-resolution images.
StyleGAN and StyleGAN2: Developed by NVIDIA, known for generating highly realistic faces and other images.
U-Net: Commonly used for image segmentation tasks, especially in biomedical image segmentation.
Music
MuseNet: By OpenAI, it generates compositions in various styles from classical to pop music.
WaveNet: By DeepMind, a deep generative model for producing realistic speech and sound.
Google's Magenta project
Video
VGAN (Video Generative Adversarial Network): Designed to generate short video clips.
3D-R2N2: A model that reconstructs 3D objects from 2D images.
3D Models
PointNet: Directly processes point clouds to classify or segment 3D objects.
Drug Discovery and Molecular Structures
ChemVAE: Uses variational autoencoders to generate molecular structures.
Games
PCGML (Procedural Content Generation via Machine Learning): Utilizes machine learning for game content generation, including levels, rules, and entities.
Multimodal (Multiple Types of Content)
CLIP (Contrastive Language–Image Pre-training): By OpenAI, it connects vision (images) and language (text) in unique ways.
DALLE: Also by OpenAI, generates images from textual descriptions, showcasing a powerful combination of vision and language understanding.
is large language model like GPT 3.5 a foundation model?
Yes, the term "foundation model" can be used to describe large language models like GPT-3.5. OpenAI has introduced the term "foundation models" to denote large-scale models trained on extensive datasets, serving as a foundational technology on which other models or applications can be built.
Foundation models, including GPT-3.5, can be:
Pre-trained: They are first trained on a vast amount of data in an unsupervised manner.
Fine-tuned: After pre-training, they can be fine-tuned on specific tasks with much smaller datasets.
This two-step process allows foundation models to generalize from the broad knowledge they acquire during pre-training and then specialize in particular tasks during fine-tuning.
The name "foundation" implies that these models serve as a base or a starting point. Instead of training a model from scratch for every new task, developers and researchers can leverage foundation models and adapt them to a wide variety of applications.
Can non language model be foundation model?
Yes, a foundation model does not necessarily need to be a language model. While the term "foundation model" is often used in the context of large language models like GPT-3 or GPT-4, it can also apply to other types of models that are pre-trained on large datasets and can be fine-tuned for specific tasks.
For instance, models used for computer vision tasks, such as image recognition or object detection, can also be foundation models. These models are trained on vast amounts of image data and can then be fine-tuned to perform specific tasks in various domains.
All language model can be foundation model but all foundation model can't be large language model. is this a fair statement?
A foundation model is typically a machine learning model that's pre-trained on a broad dataset and can be fine-tuned for specific tasks. This model often serves as the foundation for many different applications, hence the name.
A large language model, like GPT-3 or GPT-4, can indeed be a foundation model, as they are trained on extensive textual data and can be used or fine-tuned for a variety of specific language tasks.
However, not all language models could be considered foundation models. Some language models are smaller or more task-specific, and don't have the broad applicability that characterizes foundation models.
Conversely, you're correct that not all foundation models are large language models. Foundation models can also be built for other types of data, like images or audio.
So, a more accurate statement might be: "Some language models can be foundation models, and while all foundation models are not large language models, they are all typically large models trained on extensive data and designed for broad applicability."
Are foundation models pre-trained models? what kinds of data they are trained on?
Yes, foundation models are pre-trained models. The term "foundation" signifies that these models serve as a foundational or base layer that provides a broad understanding of the data they're trained on. This base can then be fine-tuned or adapted for various specific tasks, capitalizing on the knowledge captured during pre-training.
The kinds of data foundation models are trained on typically depend on the domain of application, but in general, the data should be extensive and diverse to ensure that the model learns a broad range of patterns and structures.
How non-textual model like image model or video model etc. understand prompts?
Traditional image models or video models, like classic Convolutional Neural Networks (CNNs) used for image classification, object detection, or video analysis, do not inherently "understand" textual prompts. These models are designed to take images or videos as input and produce a particular output, such as a class label or bounding box coordinates. They do not have a mechanism for processing textual information in the same way language models do.
However, recent advances have given rise to models that can combine understanding from different modalities, including text and images/videos like multimodal models.
Why to use foundation model for enterprise business use case? why not large language model?
while large language models (a type of foundation model) are excellent for tasks involving language processing, a foundation model could be a more suitable choice if your business case requires a versatile, high-performing, and scalable AI solution that goes beyond language tasks. It's important to match the choice of model to the specific needs and constraints of your enterprise use-case.
What if I take large language model as foundation model and trained and fine-tuned for business use case? can we do it?
Using a large language model as a foundation and then fine-tuning it for business-specific tasks is a powerful strategy. It allows you to leverage the generalized knowledge of the foundation model while tailoring its capabilities to suit specific business requirements.
When considering a large language model (LLM) to use as a foundation model for business, a few standout options are primarily developed by leading organizations like OpenAI and Google. Here are the prominent ones:
GPT Series (by OpenAI):
GPT-3: As of my last update in September 2021, GPT-3 is one of the most advanced and largest models with 175 billion parameters. It offers impressive capabilities in natural language processing, generation, and understanding.
GPT-2: A predecessor to GPT-3, GPT-2 has versions with varying sizes, from 125 million to 1.5 billion parameters. It's still powerful and can be used for a variety of tasks.
BERT and its Variants (by Google):
BERT (Bidirectional Encoder Representations from Transformers): BERT revolutionized the NLP landscape with its bidirectional understanding of text. It's designed for tasks that require understanding the context from both left and right of a word in a sentence.
RoBERTa: An optimized version of BERT, with changes in model architecture that improve its performance.
DistilBERT: A lighter version of BERT, retaining much of BERT's performance but with fewer parameters, making it faster and more efficient.
T5 (Text-to-Text Transfer Transformer by Google):
T5 considers every NLP problem as a text-to-text problem, making it versatile. For instance, translation would be converting English text to French text, and classification would be converting a question into an answer like "true" or "false."
XLNet:
XLNet is an extension of the Transformer-XL model, addressing some of BERT's limitations by using a permutation-based training strategy.
ELECTRA (by Google):
Rather than predicting missing words like BERT, ELECTRA is designed to distinguish between "real" and "fake" words in a sentence, making its training more efficient.
Considerations when choosing a model:
Task Specificity: Some models might be better suited for specific tasks. For instance, GPT-3 is excellent at text generation, while BERT excels at tasks requiring deep contextual understanding.
Infrastructure and Cost: Larger models require significant computational resources, leading to higher costs. Ensure you have the necessary infrastructure and budget.
Fine-tuning Capabilities: If you're planning to fine-tune the model for your business domain, ensure that the model's architecture and licensing allow for that.
Performance: Review benchmarks and performance metrics relevant to your use case to select the most effective model.
Community and Support: Models with a large user community often have more resources, tools, and support available, which can be beneficial for deployment and troubleshooting.
What do you mean by 175B parameters in the foundation model or large language model?
When we say that a model has 175 billion parameters, it means that the model has 175 billion of these weights and biases that it can adjust to learn patterns in the data.
Having a large number of parameters allows a model to learn more complex patterns and relationships in the data. In the case of GPT-3, it means that the model can learn an incredibly diverse array of language patterns, making it extremely powerful for natural language processing tasks.
However, the downside of having so many parameters is that the model becomes computationally expensive to train and requires a significant amount of data to avoid overfitting, which occurs when a model learns the training data too well and performs poorly on unseen data.
Where do I get foundation model for our own business use cases?
If you're looking to obtain foundation models for your business use cases, below are some reputable sources with direct URLs:
OpenAI:
API for GPT-3: OpenAI's API platform
Main Website: OpenAI
Hugging Face's Model Hub:
Repository: Hugging Face Model Hub
Transformers Library: Hugging Face Transformers on Github
Google Cloud AI & TensorFlow Hub:
Google Cloud AI services: Google Cloud AI & Machine Learning Products
TensorFlow Hub: TensorFlow Hub Repository
Microsoft Azure:
Azure AI services: Microsoft Azure AI Services
AWS:
AWS AI services: Amazon Web Services AI & Machine Learning
Custom Training & Datasets:
Google's Dataset Search: Google Dataset Search Tool
UCI Machine Learning Repository: UCI Repository
Can we have empty foundation model?
A "foundation model" by definition is a large-scale model that has been pretrained on a massive amount of data. This pretraining phase is what allows the model to learn a wide range of features, patterns, and representations from the data, and what makes it useful for a variety of tasks.
An "empty" foundation model, or a model that hasn't been pretrained, would essentially be an uninitialized neural network. It would not have any learned knowledge or representations, and its performance on any given task would likely be very poor until it has been trained on some data.
In this sense, it's not really accurate or useful to speak of an "empty" foundation model. The value of a foundation model comes from the knowledge it has gained during its pretraining phase. Without this pretraining, it's just a regular machine learning model that needs to be trained from scratch.
It's also worth noting that training a foundation model from scratch requires a huge amount of computational resources and data, which is why most people choose to use existing pretrained models and fine-tune them for their specific tasks.
Can we say empty without data or untrained foundation model is like a brain of a child?
The comparison of an untrained machine learning model to a newborn child's brain is an analogy often used to explain how neural networks learn. Like a child's brain, an untrained model doesn't have any prior knowledge about the world, nor does it understand any patterns or rules. It's essentially a blank slate.
When a child interacts with the world and learns from experiences (which is analogous to a model being trained on data), they begin to understand and recognize patterns, learn rules, and make predictions. In a similar way, a machine learning model learns patterns and structures from the data it's trained on and can then make predictions on new, unseen data.
However, it's important to note that this analogy has its limits. A child's brain is far more complex and capable of more types of learning and adaptation than any current AI model. While machine learning models learn from patterns in data, human learning involves a wide array of cognitive processes, emotional responses, and physical interactions with the world.
In the context of foundation models, the comparison holds to the extent that these models, like a child's brain, start from a state of not knowing anything. The massive pretraining phase, where the model learns from large amounts of data, is what transforms them into powerful tools that can understand and generate human-like text, translate languages, answer questions, and more. But remember, without the pretraining phase, they would be like "the brain of a newborn" - unable to do these tasks.
Can we say foundation model is like building a brain for a particular skill? like if we want to learn chess we need training, same way in foundation model. can we relate this to Matrix movie?
Your comparison is quite apt. Like a human brain learning a particular skill such as chess, a foundation model is first trained on a massive amount of general data to learn the basics - this is analogous to a human learning to understand language, recognizing objects, etc. Then, the model is fine-tuned on a specific task, much like how a human would focus their learning on chess once they've mastered the basics.
The Matrix movie provides an interesting (if somewhat exaggerated) analogy. In the film, characters download entire skill sets directly into their brains in a matter of seconds, like learning Kung Fu or how to fly a helicopter. In real life, human learning doesn't work that way, but this analogy illustrates how foundation models can be fine-tuned to specialize in a specific task.
However, there are important differences. While foundation models can learn to perform specific tasks quite well, they don't "understand" these tasks in the same way humans do. The model doesn't have a consciousness or a subjective experience, and it doesn't truly comprehend what it's doing. It is simply processing input data and producing output based on patterns it learned during training.
Moreover, a foundation model trained on a specific task wouldn't be able to perform an entirely different task without further fine-tuning. A model fine-tuned for chess wouldn't know how to translate languages or diagnose diseases, for example, unless it was further fine-tuned on those specific tasks.
But overall, your comparison provides a useful way to conceptualize how foundation models work: they learn a broad base of knowledge and then specialize in a specific task, much like a human learning a new skill.
"foundation model without data or without training is like it has neural network without any connections or relations. when we train a foundation model, neural network get created with required connections and relations based on data pattern." is this statement fair?
Your statement captures the essence of how neural networks, including foundation models, learn from data. However, it could benefit from a few clarifications to more accurately represent how these models work.
When a neural network (or a foundation model) is initialized, it already has a structure composed of interconnected nodes (also called "neurons") -- so it's not quite accurate to say that it has no connections or relations. What it doesn't have is meaningful connections or relations that can effectively represent patterns in data. These connections are represented by parameters (also known as weights) in the network.
At the start, the weights are typically initialized with random values. As the model is trained on data, it uses a process called gradient descent to iteratively adjust these weights based on the difference between its predictions and the actual values (the "error"). Through this process, the model learns to form useful connections and relations that capture patterns in the data.
So, a more accurate version of your statement might be:
"A foundation model before training is like a neural network with random or arbitrary connections and relations. When we train the foundation model, the network adjusts these connections based on patterns in the data, forming meaningful relations that allow it to make accurate predictions."
It's also important to note that even after training, the relations formed by a foundation model (or any machine learning model) are purely statistical and don't necessarily represent causal relationships or human-like understanding.
What is the size of untrained foundation model?
The "size" of a foundation model can refer to two things: the number of parameters it has (i.e., the model's complexity) and the amount of memory it takes up in storage or in memory during training or inference.
Number of Parameters: This refers to the number of learnable weights and biases in the model. The number of parameters is determined by the architecture of the model (e.g., the number and size of the layers in the model) and doesn't change with training. For example, GPT-3, a large foundation model, has 175 billion parameters whether it's trained or untrained.
Memory Size: This refers to the amount of storage space the model takes up, which is mainly determined by the number of parameters and the precision of the values. A model with more parameters or higher-precision values will take up more memory. The memory size of a model can change slightly during training, as certain deep learning libraries may store additional information (like optimizer states) that increases the size of the trained model.
So, in summary, an untrained foundation model has the same number of parameters as the trained version, and its memory size is roughly the same, though it may increase slightly after training due to the storage of additional information.
What could be the size(GB) in memory with 175 billion parameters?
In a neural network, each parameter is typically stored as a 32-bit floating point number. This means that each parameter takes up 32 bits, or 4 bytes, of memory.
So, a model with 175 billion parameters would take up around 700 GB of memory just to store the parameters.
The actual memory usage can be higher due to various reasons, such as the need to store intermediate values during computation, the use of certain optimization algorithms that require additional memory per parameter, and the overhead of the software libraries used. The exact memory requirements can also vary depending on the specifics of the machine learning framework or library that you're using.
For example, in practical applications, models with this many parameters are often trained with techniques such as model parallelism, where the model is split across multiple devices or machines, or with mixed-precision training, which can reduce the memory requirements.
In which format trained or untrained foundation model saved in disk?
The format in which a trained or untrained foundation model is saved to disk can vary depending on the specific machine learning library or framework that's being used.
Here are a few examples:
TensorFlow: TensorFlow saves models using the SavedModel format, which includes both the model architecture and the trained weights. This format is designed to be portable, so the model can be loaded and used in different environments. TensorFlow also supports the older checkpoint format, which saves just the weights of the model.
PyTorch: PyTorch saves models using the .pth or .pt file extension. You can either save the entire model using torch.save(model, filepath), which includes both the model architecture and the trained weights, or you can save only the model's state_dict (i.e., the trained weights) using torch.save(model.state_dict(), filepath).
Keras: Keras, which is now integrated into TensorFlow, saves models in the .h5 format when you use the model.save() function. This includes both the model architecture and the weights.
When you save a model, it's usually in a trained state, since the main reason to save a model is to be able to use it later without having to retrain it. However, you could also save an untrained model if you wanted to, for example to reuse the same architecture with different initial weights.
When we train untrained foundation model, does it create a separate copy and save?
Typically, when you train a model (including foundation models), you don't create a separate copy of the model. Instead, you update the weights (parameters) of the existing model in-place as you iterate over the training data. These weight updates are what allow the model to learn patterns in the data and improve its performance on the task it's being trained for.
Once the training is finished, you can then choose to save the trained model. This will create a file (or sometimes a set of files) on your disk that contains the model's architecture and its trained weights.
What are the differences among training , prompting and fine-tuning?
Very interesting and informative indeed...i book marked :-)