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Richard Ramos
Richard Ramos

How to Use GPT-J 6B in Transformers: A Tutorial

GPT-J-6B: What is it and how to download it?


GPT-J-6B is a large language model that can generate natural language text from a given prompt. It is based on the GPT architecture, which stands for Generative Pre-trained Transformer, and it has 6 billion parameters, which makes it one of the largest models available to the public. It was developed by EleutherAI, an open-source research collective that aims to democratize artificial intelligence.

gpt-j-6b download

In this article, we will explain what GPT-J-6B is, what are its features, what are its limitations and biases, and how to download it and use it for text generation. We will also provide some examples of the outputs that GPT-J-6B can produce.

What is GPT-J-6B?

What is GPT-J-6B?

GPT-J-6B is a transformer model trained using Ben Wang's Mesh Transformer JAX framework. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. 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.

What are the features of GPT-J-6B?

GPT-J-6B learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. Some of the features of GPT-J-6B are:

How to download and use gpt-j-6b in python

gpt-j-6b hugging face model card and files

gpt-j-6b vs gpt-3 comparison and benchmarks

gpt-j-6b finetuning tutorial and examples

gpt-j-6b text generation demo and api

gpt-j-6b mesh transformer jax code and documentation

gpt-j-6b pretrained weights and checkpoints

gpt-j-6b rotary position embedding implementation

gpt-j-6b training data and the pile dataset

gpt-j-6b limitations and biases analysis

gpt-j-6b colab notebook and google cloud tpu

gpt-j-6b spanish version bertin-gpt-j-6b

gpt-j-6b 8bit version for desktop gpu

gpt-j-6b transformer architecture and hyperparameters

gpt-j-6b autoregressive language model description

gpt-j-6b downstream tasks and applications

gpt-j-6b open source license and citation

gpt-j-6b eleutherai project and team

gpt-j-6b reddit discussion and feedback

gpt-j-6b arxiv paper and blog post

  • It can generate coherent and fluent text on various topics and domains.

  • It can perform zero-shot learning on various natural language processing tasks, such as text summarization, question answering, sentiment analysis, etc.

  • It can generate code from natural language descriptions or vice versa.

  • It can generate creative content such as stories, poems, lyrics, jokes, etc.

What are the limitations and biases of GPT-J-6B?

GPT-J-6B is not intended for deployment without fine-tuning, supervision, and/or moderation. It is not a product in itself and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case.

GPT-J-6B was trained on an English-language only dataset called The Pile, which is an open-source 886 gigabyte language modelling data set that is split into 22 smaller datasets. The Pile contains various types of texts from different sources and domains, such as books, Wikipedia, news articles, GitHub repositories, etc. However, this also means that GPT-J-6B may inherit some of the biases and inaccuracies present in the data.

Some of the limitations and biases of GPT-J-6B are:

  • It may generate factual errors or inconsistencies, especially on topics that require domain knowledge or expertise.

  • It may generate text that is irrelevant, repetitive, or nonsensical, especially on long or complex prompts.

  • It may generate text that is biased, stereotypical, or discriminatory, especially on sensitive or controversial topics.

  • It may generate text that is plagiarized, copyrighted, or harmful to someone physically, emotionally, or financially.

Therefore, it is important to use GPT-J-6B with caution and critical thinking. Do not blindly trust the outputs and always verify the sources and facts. Do not use the outputs for malicious or illegal purposes. Do not expose the outputs to vulnerable or impressionable audiences. Do not rely on the outputs for decision making or problem solving.

How to download GPT-J-6B?

Using Hugging Face Transformers library

Hugging Face Transformers is a popular open-source library that provides state-of-the-art natural language processing models and tools. It supports various frameworks such as PyTorch, TensorFlow, JAX, and Flax. It also provides easy access to hundreds of pre-trained models and datasets through its hub.

One of the models available on the hub is GPT-J-6B, which can be downloaded and used with the Hugging Face Transformers library. Here are the steps to do so:


To install the Hugging Face Transformers library, you can use pip or conda. For example, using pip, you can run the following command in your terminal:

pip install transformers

This will install the latest version of the library and its dependencies. You can also specify a particular version if you want. For more details, please refer to the official documentation.

Loading the model and tokenizer

To load the GPT-J-6B model and tokenizer, you can use the AutoModelForCausalLM and AutoTokenizer classes from the transformers module. These classes will automatically detect and load the appropriate model and tokenizer from the hub based on the name you provide. For example, you can run the following code in Python:

from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B") tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")

This will download and cache the model and tokenizer files in your local directory. You can also specify a different cache location if you want. For more details, please refer to the official documentation.

Generating text from a prompt

To generate text from a prompt using GPT-J-6B, you can use the generate method of the model class. This method takes various arguments that control the generation process, such as the maximum length, the number of samples, the temperature, the top-k, the top-p, etc. For example, you can run the following code in Python:

prompt = "Write a short story about a dragon and a knight." input_ids = tokenizer.encode(prompt, return_tensors="pt") output_ids = model.generate(input_ids, max_length=100, num_return_sequences=1, temperature=0.9, top_k=50, top_p=0.95) output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) print(output_text)

This will generate one sample of text with a maximum length of 100 tokens based on the prompt. The temperature, top-k, and top-p parameters control the randomness and diversity of the generation. You can experiment with different values to see how they affect the output. For more details, please refer to the official documentation.

Using Google Colab notebook

Google Colab is a free online service that allows you to create and run Python notebooks in your browser. It provides access to various computing resources such as GPUs and TPUs. It also integrates with Google Drive and other Google services.

One of the advantages of using Google Colab is that you do not need to install anything on your local machine. You can simply open a notebook from a URL and run it in your browser. You can also save and share your notebooks with others.

One of the notebooks available on Google Colab is GPT-J-6B Playground, which was created by Stella Biderman from EleutherAI. This notebook allows you to interact with GPT-J-6B and generate text from various prompts. Here are the steps to do so:

Accessing the notebook

To access the GPT-J-6B Playground notebook, you can use this URL:

This will open the notebook in your browser. You can also save a copy of the notebook to your Google Drive if you want.

Running the code cells

To run the code cells in the notebook, you need to connect to a runtime environment. You can choose either a CPU, a GPU, or a TPU as your hardware accelerator. To do so, you can click on the "Runtime" menu and select "Change runtime type". Then, you can select your preferred option from the dropdown menu and click "Save".

After connecting to a runtime, you can run the code cells by clicking on the "Play" button on the left side of each cell. You can also use the keyboard shortcut "Ctrl+Enter" to run the current cell. You need to run the cells in order, from top to bottom, to avoid errors.

The first cell will install the dependencies and download the model files. This may take a few minutes depending on your internet speed and hardware. The second cell will import the modules and define some helper functions. The third cell will load the model and tokenizer. The fourth cell will set some parameters for text generation.

Generating text from a prompt

To generate text from a prompt using GPT-J-6B, you can use the fifth cell in the no


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