GPT-J is an open-source alternative from EleutherAI to OpenAI's GPT-3. Available for anyone to download, GPT-J can be successfully fine-tuned to perform just as well as large models on a range of NLP tasks including question answering, sentiment analysis, and named entity recognition.
Try running GPT-J for yourself on Paperspace with Graphcore's IPU (Intelligence Processing Unit), a completely new kind of massively parallel processor to accelerate machine intelligence. Access advanced, cost-efficient IPU compute on demand in the cloud on Paperspace to build, fine-tune and deploy AI models such as GPT-J.
| Notebook | Framework | Type | Try for Free |
|---|---|---|---|
| Textual Entailment on IPU using GPT-J - Fine-tuning | Hugging Face | Fine-tuning |  |
| Text generation with GPT-J 6B | Hugging Face | Inference | |
| Faster Text Generation with GPT-J using 4-bit Weight Quantization on IPUs | Hugging Face | Inference | |
In the Textual Entailment on IPU using GPT-J - Fine-tuning notebook, we show how to fine-tune a pre-trained GPT-J model running on a 16-IPU system on Paperspace. We will explain how you can fine-tune GPT-J for Text Entailment on the GLUE MNLI dataset to reach SOTA performance, whilst being much more cost-effective than its larger cousins.
In the Text generation with GPT-J 6B notebook, we demonstrate how easy it is to run GPT-J on the Graphcore IPU using this implementation of the model and 🤗 Hub checkpoints of the model weights.
In the Faster Text Generation with GPT-J using 4-bit Weight Quantization on IPUs notebook, we show how to use group quantisation to compress model parameters to 4 bits with no fine-tuning, using 4x less memory and speeding up inference on GPT-J by ~1.5x.
To take your GPT-J usage on IPUs further, or speak to an expert, please feel free to contact us.
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The contents of this repository are made available according to the terms of the MIT license. See the included LICENSE file for details.
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