gpt j 6b

Introduction

GPT-J 6B is a transformer model developed by EleutherAI with 6 billion parameters. It is designed for text generation tasks and is trained using the Mesh Transformer JAX framework. The model is primarily intended for generating text in English from given prompts.

Architecture

The GPT-J 6B model consists of 28 layers with a model dimension of 4096 and a feedforward dimension of 16384. The architecture includes 16 attention heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is used for positional encoding, applied to 64 dimensions of each head. The model uses a tokenization vocabulary of 50257, compatible with the GPT-2 tokenizer.

Training

The model was trained on the Pile, a diverse dataset created by EleutherAI, consisting of 402 billion tokens processed over 383,500 steps on TPU v3-256 pods. The training process involved optimizing for cross-entropy loss to improve next token prediction accuracy.

Guide: Running Locally

1. Setup: Ensure you have Python and the Hugging Face Transformers library installed.
2. Load Model: Use the following code snippet to load the model and tokenizer:

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

3. Cloud GPUs: For optimal performance, especially for inference and further training, consider using cloud-based GPUs from providers like Google Cloud or AWS.

License

GPT-J 6B is released under the Apache 2.0 license, allowing for both personal and commercial use with proper attribution.