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GPT J 6B
Where to get GPT-J-6B?
https://huggingface.co/EleutherAI/gpt-j-6B/tree/main
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
| Hyperparameter | Value |
|---|---|
| parameters | 6053381344 |
| layers | 28* |
| model | 4096 |
| ff | 16384 |
| heads | 16 |
| head | 256 |
| ctx | 2048 |
| vocab | 50257/50400† (same tokenizer as GPT-2/3) |
| Positional Encoding | Rotary Position Embedding (RoPE) |
| RoPE Dimensions | 64 |
*Each layer consists of one feedforward block and one self attention block.
† Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3.
28 layers in the GPT-J model:
| Layer | Name |
|---|---|
| 1 | Embedding |
| 2 | LayerNorm |
| 3-4 | TransformerBlock |
| 5-6 | TransformerBlock |
| 7-8 | TransformerBlock |
| 9-10 | TransformerBlock |
| 11-12 | TransformerBlock |
| 13-14 | TransformerBlock |
| 15-16 | TransformerBlock |
| 17-18 | TransformerBlock |
| 19-20 | TransformerBlock |
| 21-22 | TransformerBlock |
| 23-24 | TransformerBlock |
| 25-26 | TransformerBlock |
| 27-28 | TransformerBlock |
The first layer, "Embedding," converts the input text into a vector of numbers that can be processed by the model. The second layer, "LayerNorm," normalizes the inputs to improve the model's stability.
The next 24 layers, "TransformerBlock," are the core components of the model. Each of these layers is responsible for processing the input text, identifying patterns and relationships, and generating the model's output.
Overall, the GPT-J model is a complex and powerful tool that can generate coherent and meaningful text based on a wide range of input prompts. The 28 layers in the model work together seamlessly to enable this capability, making it a valuable resource for a wide range of applications.
a typical transformer block in the GPT-J model:
| Layer | Sub-layer | Name |
|---|---|---|
| 1 | Self-Attention | Multi-Head Attention |
| 2 | LayerNorm | |
| 3 | Feedforward | |
| 4 | LayerNorm |
Each transformer block consists of four sub-layers, with the first sub-layer being a self-attention mechanism that helps the model focus on the most important parts of the input text. The self-attention mechanism is implemented using a multi-head attention architecture, which allows the model to learn from multiple representations of the same input.
The second sub-layer is a normalization layer that helps the model remain stable during training and improves its performance. The third sub-layer is a feedforward neural network that processes the output from the self-attention mechanism and generates a new representation of the input text.
The fourth sub-layer is another normalization layer that further stabilizes the output of the feedforward network. By combining these sub-layers, the transformer block can effectively process the input text and generate meaningful output.
Overall, the transformer block is a key component of the GPT-J model, and its architecture is designed to enable the model to process vast amounts of text efficiently and generate coherent and meaningful output.
What are the files in the repository?
| File | What does it do? |
|---|---|
| added_tokens.json | Extra list of words and their associated index integer |
| config.json | This contains the configuration settings for the model, e.g number of layers, size of the hidden state, size of the vocabulary, and other hyperparameters that affect the behavior of the model. configuration settings for the model, e.g number of layers, size of the hidden state, size of the vocabulary, and other hyperparameters that affect the behavior of the model. |
| flax_model.msgpack | flax_model.msgpack is a file that contains a serialized representation of a GPT-J model in the MessagePack format. The MessagePack format is a lightweight binary serialization format that is designed to be fast and compact. The flax_model.msgpack file is generated when the GPT-J model is trained using the Flax library, which is a high-performance neural network library built on top of JAX. Flax allows for efficient training and inference on accelerators such as GPUs and TPUs. The flax_model.msgpack file contains the learned parameters of the GPT-J model, as well as the model's architecture and hyperparameters. The file can be used to load the GPT-J model into a Flax-based application or to convert the model to another framework or library format.When using the Hugging Face transformers library to interact with GPT-J, you do not need to work with the flax_model.msgpack file directly. Instead, you can use the pre-trained GPT-J models that are available through the Hugging Face Model Hub, which provide pre-converted PyTorch checkpoint files that can be loaded into the transformers library. |
| merges.txt | In the GPT-J repository, merges.txt is a text file that contains the merge rules used to build the byte-level BPE (Byte Pair Encoding) vocabulary for the GPT-J model. BPE is a popular method for subword tokenization, where words are broken down into smaller subword units that can be represented more efficiently and flexibly by the model. In the BPE algorithm, the vocabulary is built by iteratively merging the most frequent pairs of characters or subwords in the input corpus. The merges.txt file contains a list of these merge operations, where each line represents a single merge rule that specifies the two subword units to be merged into a new, longer subword unit.For example, a merge rule might look like this: i n -> in This rule specifies that the subword units "i" and "n" should be merged into a new subword unit "in". The merge rules in merges.txt are ordered by frequency, so the most common pairs of subwords are merged first, followed by less common pairs.The merges.txt file is important because it defines the vocabulary that the GPT-J model uses to represent text inputs. By adjusting the merge rules in the merges.txt file, you can customize the vocabulary of the model to better suit your specific use case or domain. However, modifying the merge rules requires retraining the model's tokenizer and can be a complex and time-consuming process. |
| pytorch_model.bin | A binary file that contains the pre-trained parameters of the GPT-J model in the PyTorch format. The PyTorch format is a common format for storing and loading deep learning models in the PyTorch library.The pytorch_model.bin file is created by converting the original checkpoint file in the Flax format into the PyTorch format, using the convert_flax_checkpoint_to_pytorch.py script provided in the GPT-J repository. This allows the GPT-J model to be used with the PyTorch library and the Hugging Face transformers library, which provides a wide range of tools and utilities for working with pre-trained models. The pytorch_model.bin file contains the learned parameters of the GPT-J model, which represent the weights and biases of the various layers and components of the model. These parameters were learned during the training process, where the model was trained on a large corpus of text data to predict the next word or token in a sequence. When using the Hugging Face transformers library to interact with GPT-J, you can load the pre-trained GPT-J model from the pytorch_model.bin file using the from_pretrained() method provided by the library. This method automatically handles the process of downloading and loading the model weights from the Hugging Face Model Hub, and returns a PyTorch model object that can be used for a wide range of natural language processing tasks. |
| special_tokens_map.json | In the GPT-J repository, special_tokens_map.json is a JSON file that maps special tokens used by the GPT-J model to their corresponding token IDs. Special tokens are tokens that have a special meaning or function in the model, such as the beginning-of-sequence token, end-of-sequence token, or padding token. The special_tokens_map.json file is used by the Hugging Face transformers library to manage special tokens in the GPT-J model. The file maps the names of the special tokens to their corresponding token IDs in the GPT-J model's vocabulary, which allows the library to insert the special tokens into input sequences and decode output sequences correctly. For example, the special_tokens_map.json file might contain entries like the following: { "bos_token":< |
| tf_model.h5 | tf_model.h5 file is the pre-trained model file for the GPT-J language model. It contains the weights and architecture of the neural network model trained on a massive amount of text data, allowing it to generate high-quality, coherent and human-like text output. The file is stored in the HDF5 format and can be loaded into the TensorFlow or PyTorch framework to generate text or perform other natural language processing tasks. The tf_model.h5 file in the GPT-J repository is one of the largest and most powerful language models available for generating text, with over 6 billion parameters. |
| tokenizer.json | a file that contains the configuration and vocabulary of the tokenizer used to preprocess the text data for GPT-J model training and text generation. The tokenizer is responsible for breaking down raw text input into a sequence of tokens that can be processed by the model. The tokenizer.json file contains metadata and parameters such as the special tokens used for padding, masking, and separation, the maximum sequence length allowed for input, and the vocabulary size and mapping between tokens and their numerical IDs. The tokenizer.json file can be loaded into the Hugging Face Transformers library or other NLP frameworks to preprocess text input for GPT-J text generation. |
| tokenizer_config.json | A configuration file that contains additional parameters and settings for the tokenizer used in GPT-J model training and text generation. It is related to the tokenizer.json file, which contains the vocabulary and basic configuration information. The tokenizer_config.json file contains additional metadata and settings for the tokenizer, such as the tokenization mode, which determines how the input text is split into tokens, and the handling of special characters, emojis, or non-ASCII characters in the text. The tokenizer_config.json file can be loaded along with the tokenizer.json file to configure the tokenizer for specific text preprocessing needs in GPT-J model training and text generation. It is used by the Hugging Face Transformers library or other NLP frameworks to preprocess text input for GPT-J. |
| vocab.json | contains the vocabulary of the tokenizer used for GPT-J model training and text generation. It maps each token in the vocabulary to its corresponding numerical ID, which is used by the model for processing and generating text. The vocab.json file is similar to the tokenizer.json file, which also contains the vocabulary and configuration information for the tokenizer. However, the vocab.json file only contains the vocabulary mapping and does not include other metadata or parameters that may be required for tokenization or text preprocessing. The vocab.json file can be loaded into the Hugging Face Transformers library or other NLP frameworks to preprocess text input for GPT-J text generation. It is an essential component of the GPT-J language model, as it defines the set of tokens that the model can recognize and generate. |
Model Description GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
Hyperparameter Value
n parameters 6053381344
n layers 28*
d model 4096
d ff 16384
ℎ
n heads 16
ℎ
d head 256
n ctx 2048
n vocab 50257/50400† (same tokenizer as GPT-2/3) Positional Encoding Rotary Position Embedding (RoPE) RoPE Dimensions 64