Summary Let's build GPT: from scratch, in code, spelled out. - YouTube (Youtube) www.youtube.com
20,700 words - YouTube video - View YouTube video
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The text discusses the use of transformer architecture in AI Chat and GPT models, including the training process, tokenization, self-attention, and the importance of attention in normalization and improving performance.
Slides
Slide Presentation (9 slides)
Key Points
- Chat is an AI system that completes text-based tasks and has gained popularity in the AI community.
- Chat is based on the transformer architecture, which has had a significant impact on the field of AI.
- A character-level GPT language model can be trained using the "tiny shakespeare" dataset to generate infinite Shakespeare-like text.
- The training process involves tokenizing the text, splitting it into training and validation sets, and training the transformer model on text sequences.
- Self-attention is a key component of the transformer model that allows tokens to communicate and understand context for better predictions.
- Layer normalization and skip connections are added to optimize deep neural networks in the transformer model.
- The transformer model can be scaled up by adding more layers and heads, and dropout can be used to prevent overfitting.
- Pretraining and fine-tuning stages are involved in training GPT for specific tasks, such as question-answering.
Summaries
489 word summary
AI Chat uses transformer architecture, requires extensive training.
Build GPT, generate Shakespeare.
Python, calculus/statistics understanding needed. Google Colab Jupyter notebook utilized. Shakespeare dataset converted to character sequence. Encoding/decoding shown with lookup table.
GPT generates text by encoding characters and tokenizing a Shakespeare dataset.
Chunking improves context
Input into transformer considers time and batch dimensions, with random offsets generating data chunks stacked into a tensor. The input is a 4x8 tensor representing training set chunks, containing 32 independent examples.
Transformer predicts integers with GPT.
Reshape input, evaluate loss, generate function.
Generation process creates tokens. Softmax converts logits to probabilities. Loss is generated if targets provided. Process starts with batch size 1. Resulting indices converted to Python list.
Train GPT with Adam optimization.
Model progress improves with more tokens. Transformer model introduces interaction. Code includes parameters, encoding, decoding, and language model. GPU support for faster processing.
Generating "p's" on device, loss function, model phases, torch context manager, 120 lines of code, output includes loss and sample. Attention block, mathematical trick, sample with batches and time components.
Tokens communicate context.
Efficiently calculate token averages using matrix simplification and multiplication, normalizing rows for accuracy.
Matrix manipulation for incremental averages.
Soft max normalizes data, determines token affinity, uses weighted aggregation in self attention, and introduces intermediate embedding phase.
Build GPT with linear transformations, embedding tables, and self-attention blocks. Initialize token affinity at 0.
Tokens use self-attention for interaction.
Tokens communicate based on affinity and aggregation is done by producing a value vector.
Attention is a directed graph with 8 nodes. Positional encoding assigns positions to the nodes. Batch elements are processed independently. There is no communication between future and past tokens.
GPT uses self-attention and encoder-decoder blocks to predict sentiment. Cross-attention is used from separate sources. Attention is important for normalization.
Soft max converges towards 1 hot vectors, sharpens when multiplied by 8, and is controlled by scaling. Self attention knowledge in head module.
Implement multi-head attention with self-attention, normalization, aggregation, and feeding.
GPT uses parallel communication channels. Tokens compute independently.
Validation loss decreases with integrated communication and computation using multi-head self-attention and feed-forward network. Skip connections improve deep neural network optimization.
Original blocks initialized gradually. Projection layer added. Communication and computation performed. Inner layer multiplied by 4. Training results in validation loss of 2.08 with overfitting. Layer norm improves deep neural networks.
Layer normalization in transformer model improves performance by normalizing rows.
Improved transformer model with scaled-up parameters achieves reduced validation loss.
Transformer generates nonsensical text.
Decoder-only transformer
Encoder-decoder architecture emphasized in original paper.
Transformer without triangular mask, encoder-decoder model, decoder-only transformer, pretrained Up-Pie and Nano GPT models, multiple heads in causal self-attention block, identical communication and computation phases in transformer blocks.
GPT building process and training stages.
GPT fine-tuning uses question-answer pairs to align the model as an assistant. Reviewers rank the pairs for a reward model, resulting in a question-answer system. There are larger versions of GPT available.
Fine tuning required
520 word summary
AI system Chat generates multiple outcomes, indexes interactions, and is based on the transformer architecture but requires extensive training.
Build GPT from scratch, generate Shakespeare.
Python, calculus/statistics understanding needed. Google Colab Jupyter notebook utilized. Shakespeare dataset converted to character sequence. Encoding/decoding shown with lookup table.
GPT encodes characters, tokenizes Shakespeare dataset, generates text.
Chunked text improves transformer context.
Input into transformer considers time and batch dimensions. Random offsets generate data chunks stacked into a tensor. Input is a 4x8 tensor with rows representing training set chunks. Contains 32 independent examples.
Transformer predicts integers with GPT.
Reshape input, evaluate loss, generate function.
Generation process creates new tokens. Softmax converts logits to probabilities. Loss is generated if targets provided. Process starts with batch size 1. Resulting indices converted to Python list.
Train GPT with Adam optimization.
Model progress improves with more tokens. Transformer model introduces interaction. Code includes parameters, encoding, decoding, and language model. GPU support for faster processing.
Generating "p's" on device, loss function, model phases, torch context manager, 120 lines of code, output includes loss and sample, attention block, mathematical trick, sample with batches and time components.
Tokens communicate with past context.
Efficiently calculate token averages using matrix simplification and multiplication, normalizing rows for accuracy.
Matrix manipulation for incremental averages.
Soft max normalizes data, determines affinity between tokens, and uses weighted aggregation in self attention. Intermediate embedding phase introduced.
Build GPT with linear transformations, embedding tables, and self-attention blocks. Initialize token affinity at 0.
Tokens use self-attention for interaction.
Tokens communicate based on affinity. Aggregation is done by producing a value vector.
Attention is a directed graph. GPT has 8 nodes. Positional encoding gives nodes position. Batch elements processed independently. No communication between future and past tokens.
GPT predicts sentiment using self-attention and encoder-decoder blocks. Cross-attention is used from separate sources. Attention is important for normalization.
Soft max converges towards 1 hot vectors with extreme numbers, sharpens when multiplied by 8, controlled by scaling. Self attention knowledge in head module.
Create self-attention, normalize, aggregate, feed. Implement multi-head attention.
GPT uses parallel communication channels. Tokens compute independently.
Validation loss decreases with integrated communication and computation using multi-head self-attention and feed-forward network. Skip connections improve deep neural network optimization.
Original blocks initialized gradually. Projection layer added. Communication and computation performed. Inner layer multiplied by 4. Training results in validation loss of 2.08 with overfitting. Layer norm improves deep neural networks.
Layer normalization in transformer model improves performance by normalizing rows instead of columns.
Transformer model scaled up with improved parameters, reduced validation loss.
Transformer model generates nonsensical Shakespearean text.
Decoder-only transformer for language modeling.
Original paper focuses on encoder-decoder architecture.
Transformer used without triangular mask, encoder encodes French sentence, decoder incorporates encoder's outputs. Decoder-only transformer imitates text file. Pretrained Up-Pie is training code, Nano GPT is similar pretrained model. Nano GPT's causal self-attention block has multiple heads in batch manner. Transformer blocks have identical communication and computation phases.
GPT building process and training stages.
GPT fine-tuning aligns model as assistant. Uses question-answer pairs. Ranked by reviewers for reward model. Creates question-answer system. Larger GPT versions available.
Fine tuning required for specific tasks.
1014 word summary
Chat, an AI system that completes text-based tasks, is popular in the AI community. It generates multiple outcomes and has websites indexing interactions. Chat is a language model based on the transformer architecture, but extensive training is needed.
Train a character-level GPT language model using "tiny shakespeare" dataset. Generate infinite Shakespeare. Code available in GitHub repository called Nano (GPT). Train on open web text dataset to reproduce GPT-2 performance. Write repository from scratch, define transformer piece by piece, train on tiny shakespeare dataset.
Python proficiency, calculus/statistics knowledge, and previous neural network language model understanding required. Google Colab Jupyter notebook used. Shakespeare dataset downloaded. Text converted to character sequence. Encoding/decoding process demonstrated using lookup table.
GPT uses character-level encoding, tokenizes Shakespeare dataset, splits into training/validation sets. Goal: generate Shakespeare-like text.
Text sequences are chunked for training the transformer model, improving context prediction.
Feeding inputs into transformer considers time and batch dimensions. Random offsets generate chunks of data stacked into a tensor for processing. Input is a 4x8 tensor with rows representing training set chunks. Contains 32 independent examples.
The transformer predicts integers using a neural network and GPT language model.
Reshape input, evaluate loss, generate function extends input for model predictions.
The generation process creates new tokens based on previous predictions and indices. Softmax converts logits to probabilities for sampling. A loss is generated if targets are provided. The process starts with a batch of size 1 and removes the batch dimension. The resulting indices are converted to a Python list.
Improve GPT model by training with Adam optimization for better output results.
The model's progress improves with more tokens, but lacks interaction. Introducing the transformer model solves this. The code is converted and includes parameters, encoding, decoding, and a language model. GPU support is added for faster processing.
Creating context for generating "p's" is important on the device. Estimated loss function reduces noise in measuring current loss during training. Setting model to evaluation phase and resetting to training phase is explained. Torch context manager optimizes memory usage. The script is around 120 lines of code and will be released later. Output includes train loss, val loss, and a sample produced by the model. First attention block for processing tokens is almost ready to be written. Mathematical trick used in self-attention within transformers is introduced. Sample with batches, time components, and formation illustrates the concept.
Tokens in a sequence can communicate with their past context by averaging preceding elements.
Efficiently calculate token averages using matrix simplification and multiplication, normalizing rows for accuracy.
Matrix manipulation calculates incremental averages using vectorization and weighted aggregation with batch matrix multiplication.
Soft max normalizes data and determines affinity between tokens. Weighted aggregation is used in self attention. Intermediate embedding phase introduced.
Build GPT from scratch using linear transformation, embedding tables, and self-attention blocks. Initialize token affinity at 0.
Self-attention allows tokens in a sequence to gather information from each other. Tokens emit queries and keys to calculate interaction. Linear modules generate queries and keys, and matrix multiplication calculates attention weights. Attention weights vary for each batch element.
Tokens in self-attention communicate based on affinity determined by dot product of query and key vectors. Aggregation is done by producing a value vector.
Attention is a communication mechanism in a directed graph. GPT has 8 nodes. Positional encoding gives nodes a sense of position. Batch elements are processed independently. Future tokens don't communicate with past tokens.
GPT uses self attention to predict sentiment, with encode and decoder blocks. Cross attention is used from separate sources. Attention is flexible and important for normalization.
Soft max converges towards 1 hot vectors with extreme numbers, sharpens when multiplied by 8, controlled by scaling. Self attention knowledge in head module.
Create self-attention by applying linear projections, normalize and aggregate values, feed into attention head. Crop context, adjust learning rate and iterations. Implement multi-head attention by running parallel self-attention heads.
GPT uses parallel communication channels for data gathering, incorporating position and token embeddings, attention mechanisms, and feed-forward networks. Tokens compute independently.
Validation loss decreases. Communication and computation integrated with multi-head self-attention and feed-forward network. Skip connections improve optimization in deep neural networks.
Original blocks gradually initialized over time for minimal contribution to residual pathway. Projection layer added. Communication and computation performed. Inner layer of feed forward network multiplied by 4. Training results in validation loss of 2.08 with overfitting. Layer norm improves optimization of deep neural networks, similar to batch normalization.
Layer normalization in transformer model normalized rows instead of columns, improving performance.
Transformer model scaled up with more layers and heads, adjusted hyperparameters, improved validation loss.
In 15 minutes, transformer model generated recognizable, nonsensical Shakespearean text. The implementation is a decoder-only transformer using a triangular mask for language modeling. The original paper focuses on encoder-decoder architecture for machine translation.
In our video, we show how a transformer is used without a triangular mask, allowing all tokens to communicate freely. The encoder encodes a French sentence and outputs it. The decoder uses cross attention to incorporate the encoder's outputs. We use a decoder-only transformer to imitate a text file. Pretrained Up-Pie is the training code, while Nano GPT is a similar pretrained model. The causal self-attention block in Nano GPT has multiple heads implemented in a batch manner. The transformer blocks have identical communication and computation phases.
Building GPT involves position and token coatings, layer norm, and a final linear layer. Chat training has two stages: pretraining and fine tuning. Pretraining uses a large amount of internet data to train a decoder-only transformer. The OpenAI transformer has 1.75 billion parameters compared to the created transformer's 10 million. The larger model requires significant infrastructure. After pretraining, the model generates documents but lacks helpful responses to questions. It may generate more questions instead.
GPT fine-tuning involves aligning the model to function as an assistant. Training data consists of question-answer pairs. Responses are ranked by human reviewers to create a reward model. This transforms the model into a question-answer system. Larger versions of GPT exist.
Further stages of fine tuning are needed for specific tasks like alignment and sentiment detection.
3190 word summary
Chat, an AI system that completes text-based tasks, has gained popularity in the AI community. It generates different outcomes based on the same prompt and can provide multiple answers. There are websites that index interactions with Chat, including humorous examples like explaining HTML to a dog or writing release notes for a chess game. Chat is a language model that completes sequences of words based on its understanding of English language patterns. The under hood components of Chat are based on the transformer architecture, which has had a significant impact on the field of AI. However, it is not possible to fully reproduce Chat's capabilities without extensive training and fine-tuning.
We will train a character-level GPT language model using a smaller dataset called "tiny shakespeare," which contains all of Shakespeare's works in a single file. The transformer neural network will predict the next character in a sequence based on the highlighted characters and generate character sequences that resemble Shakespeare's language. We can generate infinite Shakespeare once the system is trained. The code to train these transformers is available in a GitHub repository called Nano (GPT), which is a simple implementation consisting of two files. By training on the open web text dataset, we can reproduce the performance of GPT-2. In this lecture, we will write the repository from scratch, defining the transformer piece by piece and training it on the tiny shakespeare dataset to generate infinite Shakespeare.
To understand how chat works, a proficiency in Python, basic understanding of calculus and statistics, and familiarity with previous videos on neural network language models is required. A Google Colab Jupyter notebook is used to share code. The Shakespeare dataset is downloaded and the first 1000 characters are printed. The text is then converted into a sequence of characters, creating a vocabulary of possible elements. A strategy is developed to tokenize the input text by translating characters into integers. A code chunk demonstrates the encoding and decoding process using a lookup table.
There are various ways to encode text into integers, such as using sentence piece or byte pair encoding. OpenAI's GPT uses character-level encoding, resulting in long sequences. The training set of Shakespeare can be tokenized using the encode and decoder functions. The dataset is split into a training and validation set for assessing overfitting. The goal is to create a neural network that generates Shakespeare-like text.
To train the transformer model, chunks of text sequences are used instead of feeding the entire text at once. These chunks have a maximum length called the block size, which is typically set to 8 characters. Each chunk contains multiple examples for the transformer to predict the next character in the sequence. Training on these examples with different context lengths helps the transformer learn to predict characters within various contexts. This is important for later inference when generating text.
When feeding inputs into the transformer, there are two dimensions to consider: time and batch. Multiple chunks of text are stacked in a single tensor for efficiency, but they are processed independently. The code provided generates random offsets to grab chunks of data, which are then stacked into a tensor. The input to the transformer is a 4 by 8 tensor, with each row representing a chunk of the training set, and the targets are used for the loss function. This 4 by 8 array contains 32 independent examples.
The transformer simultaneously processes examples and predicts integers in each position of the tensor. The input is fed into a neural network, specifically the GPT language model. The GPT language model is implemented as a subclass of Module in PyTorch. The input integers are passed into a token embedding table, where each integer corresponds to a row in the table. The rows are arranged into a batch, time, and channel tensor. The model predicts what comes next based on the individual identity of a single token. The predictions are evaluated using the negative log likelihood loss, or cross entropy, to measure their quality.
The target should have a high number, while the other dimensions should be low. However, there is an issue that prevents it from running. We need to reshape the input to match what is expected. We reshape the "lo" and "targets" variables to conform to the desired dimensions. After reshaping, we can evaluate the loss, which is currently 4.87. The initial predictions are not diffuse enough. Now that we can evaluate the model, we want to generate from it. The generate function extends the input to include additional characters.
The generation process involves creating new tokens based on previous predictions. The current indices are used to get prediction strengths and generate new indices. The last step in the time dimension is focused on for predicting what comes next. Softmax is used to convert the logits to probabilities, and sampling is done based on the probability distribution. The sampled integers are added to the current sequence of integers. The process can generate a loss if targets are provided, otherwise, it returns the generated sequence. A batch of size 1 is created to kick off the generation with a new line character. The generate function works on batches, so indexing is done to remove the batch dimension. The resulting array of indices is converted to a Python list.
We generated random text using a GPT model, but it was garbage because the model is currently random. We want to train the model to improve the results. We will use an optimization object called Adam, which is more advanced and works well. We will run the training loop for a certain number of iterations and evaluate the loss. The optimization is happening, but we will increase the number of iterations for better results. After training for longer, the loss is improving. Although it won't be Shakespeare, we expect more reasonable output.
The model's progress is improving as the number of tokens increases. However, the current model is simple and the tokens are not interacting with each other to make predictions. To address this, the tokens need to communicate and understand the context in order to make better predictions. This is where the transformer model comes in. The code is converted from a Jupyter notebook to a script to simplify the process. The script includes various parameters and familiar elements such as data encoding, decoding, and creating a language model. Additionally, the ability to run on a GPU is added for faster processing.
In the text excerpt, the author discusses creating context for generating "p's" and the importance of creating it on the device. They also mention the need for an estimated loss function to reduce noise in the measurement of current loss during training. The author explains the practice of setting the model to evaluation phase and resetting it to training phase, although it doesn't currently affect the model. They mention the use of a Torch context manager to optimize memory usage. The author notes that the script is around 120 lines of code and will be released later. They show an example of the output from running the script, including train loss, val loss, and a sample produced by the model. The author mentions being almost ready to write the first attention block for processing tokens and introduces a mathematical trick used in self-attention within transformers. They create a sample with batches, time components, and formation at each point in the sequence to illustrate the concept.
To ensure that tokens in a sequence only communicate with their past context, an easy way is to average the preceding elements. While this method may result in a loss of spatial arrangement information, it allows for the calculation of the average vectors for each token in the sequence. By initializing a bag of words at 0 and iterating over the batch dimensions and previous tokens, the average is calculated and stored in a one-dimensional vector. This process allows for communication between tokens while preserving the context of each token's history.
We can efficiently calculate the outcome of averaging tokens using matrix simplification. By using matrix multiplication, we can achieve this. The trick is to use the function "tril" to obtain the lower triangular portion of the matrix, which allows us to ignore certain elements and focus on the desired result. By normalizing the rows of matrix b to sum to 1, we can calculate an average instead of a sum.
We can manipulate elements of a matrix to calculate averages in an incremental fashion. By multiplying a given matrix with a multiple line matrix, we can achieve these averages conveniently. To make the process more efficient, we can use vectorization. We create an array called "way" that represents the weights for averaging each row. By performing batch matrix multiplication, we can apply the weighted aggregation to all batch elements individually. The weights are specified in a t by t array, resulting in a weighted sum. The aggregation is performed in a triangular form, where each token only receives information from the tokens preceding it. Another way to achieve the same result is by using the "max" function and applying a mask fill to set certain elements to negative infinity. Finally, we can use softmax along each row to complete the process.
Soft max is a normalizing operation that produces the same matrix when applied twice. In soft max, each element is differentiated and divided by the sum. This process creates a mask that determines the interaction strength or affinity between tokens. By setting certain tokens to negative infinity, they are excluded from aggregation. The weighted aggregation of past elements depends on their affinity with each other. This weighted aggregation is used in self attention. A lower triangular matrix indicates how much each element contributes to a specific position. This concept is used to develop the self attention block. Some preliminary changes are made, such as removing unnecessary variable passing and introducing an intermediate embedding phase.
To build GPT from scratch, we start with a language modeling head that uses linear transformation. We encode the tokens' identities and positions using embedding tables. The addition of token and position embeddings creates a matrix that holds both identity and position information. Self-attention is the crux of the model, where a small self-attention block is implemented for a single head. The code achieves a simple average of previous and current tokens, masked and normalized using a lower triangular structure. The affinity between tokens is initialized to 0.
Self-attention is a technique that allows tokens in a sequence to gather information from other tokens. Each token emits a query and a key vector, which are used to calculate the interaction between tokens. By multiplying the queries and keys, we obtain a matrix that represents the attention weights between tokens. This allows for data-dependent information flow and enables tokens to learn more about specific tokens in the sequence. The implementation involves linear modules to generate the queries and keys, followed by a matrix multiplication to calculate the attention weights. This process results in attention weights that vary for each batch element, as each batch contains different tokens at different positions.
In self-attention, tokens communicate based on their affinity. Affinity is determined by the dot product of query and key vectors. The resulting distribution determines how much information to aggregate from each token. The aggregation is done by producing a value vector. The output of a single self-attention head is a 16-dimensional vector. Each token has private information (x) and communicates interesting information (v) to other tokens.
Attention is a communication mechanism in which nodes in a directed graph aggregate information via weighted sums of nodes that point to them. The graph structure of GPT consists of 8 nodes, with each node being pointed to by the previous nodes and itself. Attention can be applied to any arbitrary directed graph. There is no notion of space in attention, so positional encoding is necessary to give nodes a sense of position. Elements across the batch dimension are processed independently and do not communicate with each other. In language modeling, future tokens do not communicate with past tokens, but this constraint is not necessary in general cases.
In building GPT, self attention is used to predict the sentiment of a sentence. The encode block allows nodes to communicate, while the decoder block maintains a triangular structure to prevent nodes from giving away the answer. Cross attention is used when information is pulled from a separate source. Attention is generalized and more flexible than self attention. Scaled attention is important for normalization and preserving variance.
Soft max converges towards 1 hot vectors when there are very positive and very negative numbers. Soft max sharpens towards the maximum value when the tensor values are multiplied by 8. Extreme values in the initial visualization of Soft max are not desired. Scaling is used to control variance at the start. The self attention knowledge is implemented in a head module for further use.
You can create a self-attention component by applying linear projections to all nodes. Use the register buffer to create a lower triangular matrix. Normalize and aggregate the values. Feed the encoded information into the self-attention head. Crop the context to ensure it doesn't exceed the block size. Decrease the learning rate and increase the number of iterations for better results. Implement multi-head attention by running multiple heads of self-attention in parallel and concatenating their outputs.
GPT utilizes multiple communication channels in parallel, each with smaller dimensions. This is similar to group convolution. Multiple independent channels of communication help tokens gather different types of data. The paper introduces position and token embeddings, masked multi-headed attention, and cross-attention to an encoder. Computation is added on a per-token level through a feed-forward network. The feed-forward network consists of a linear layer followed by a reLU activation. It is applied sequentially after the self-attention step. Tokens perform the feed-forward computation independently.
The validation loss decreases from 2.28 to 2.24. Communication and computation are integrated in the block using multi-head self-attention and a feed-forward network. The number of heads is determined by the number of embeddings and the embedding dimension. Skip connections, or residual connections, are added to improve optimization in deep neural networks. These connections allow gradients to flow through every addition node, creating a gradient superhighway.
In the beginning, the original blocks contribute very little to the residual pathway and are gradually initialized over time. The implementation involves adding a projection layer and performing communication and computation. The feed forward network's inner layer should be multiplied by 4. Training results in a validation loss of 2.08 and some overfitting. The addition of layer norm is helpful for optimizing deep neural networks. Layer norm is similar to batch normalization.
We implemented layer normalization in our transformer model, normalizing the rows instead of the columns. We no longer need running buffers or a distinction between training and test time. We keep gamma and beta, but not momentum. This is called the pre-norm formulation, which is a deviation from the original paper. We also added layer norms before the transformation, resulting in a slight improvement in performance. Adding layer norms would likely be more beneficial in larger and deeper networks. Additionally, there should be a layer norm at the end of the transformer and before the final linear activation.
The transformer model is complete and has been scaled up by adding more layers and heads. Dropout has been introduced to prevent overfitting. Hyperparameters have been adjusted, resulting in a validation loss improvement from 2.07 to 1.48. The model was pretrained for approximately 15 minutes on a powerful GPU and may not be reproducible on a CPU or less powerful devices.
In about 15 minutes, the transformer model was able to generate recognizable text in the style of Shakespeare. However, the output is nonsensical when read closely. This demonstrates the capabilities of the transformer model trained on a character level using a dataset from Shakespeare. The architecture used in this implementation is a decoder-only transformer, lacking the attention and encode components. The decoder uses a triangular mask to generate text, making it suitable for language modeling. The original paper on which this implementation is based focuses on machine translation, where the encoder-decoder architecture is used to condition the generation on additional information, such as a sentence to be translated.
In our video, we demonstrate how a transformer is used without a triangular mask, allowing all tokens to communicate freely. The encoder encodes the content of a French sentence and outputs it. In the decoder, cross attention is used to incorporate the outputs of the encoder. The keys and values come from the top, generated by the encoder. This conditioning allows the decoder to consider the full encoded French prompt. We use a decoder-only transformer because we are imitating a text file. Pretrained Up-Pie is the boilerplate code for training the network, while Nano GPT is a pretrained model of interest. The model in Nano GPT is almost identical to what we have done in our demonstration. The causal self-attention block is similar, but with multiple heads implemented in a batch manner. The blocks of the transformer are identical in communication and computation phases.
The code for building GPT includes position and token coatings, layer norm, and a final linear layer. Training chat involves two stages: pretraining and fine tuning. In pretraining, a large chunk of internet data is used to train a decoder-only transformer. The OpenAI transformer has 1.75 billion parameters compared to the 10 million in the created transformer. The architecture and hyperparameters are similar, but training the larger model requires a massive infrastructure. After pretraining, the model generates documents and articles, but doesn't provide helpful responses to questions. It may generate more questions instead.
The fine-tuning process for GPT involves aligning the model to function as an assistant by collecting training data that resembles assistant behavior. This data consists of question-answer pairs in a specific format. The model is then trained to focus on this type of data, gradually aligning it to expect questions and provide answers. The fine-tuning process also includes steps where different responses are ranked by human reviewers to create a reward model. This reward model is used to train the model further and ensure that the generated answers are expected to receive high rewards. This fine-tuning stage transforms the model from a document computer to a question-answer system. Some of the data used in this stage is not publicly available. The training process involves a decoder-only transformer and follows the paper "Attention is All You Need." The code for training the model will be released, along with a notebook and Google Colab app. Larger versions of GPT, such as G3, exist and are architecturally similar but significantly larger. The summary does not cover fine-tuning stages beyond language modeling.
To achieve specific tasks, alignment, sentiment detection, or document completion, further stages of fine tuning are necessary. This can involve supervised fine tuning or more advanced techniques like training a reward model and using Ppo for alignment. Much more can be done on top of the model, but the lecture concludes here.
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Source: https://www.youtube.com/watch?v=kCc8FmEb1nY
Page title: Let's build GPT: from scratch, in code, spelled out. - YouTube
Meta description: We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections t...