Create README.md

README.md

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+---
+license: apache-2.0
+---
+
+# Graphcore/convnext-base-ipu
+
+Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
+
+Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.
+
+
+## Model description
+GPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and additional layer normalisation.  
+ 
+Paper link : [A ConvNet for the 2020s](https://arxiv.org/pdf/2201.03545.pdf) 
+
+
+
+## Intended uses & limitations
+
+This model contains just the `IPUConfig` files for running the [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) model on Graphcore IPUs.
+
+**This model contains no model weights, only an IPUConfig.** 
+
+## Usage
+
+```
+from optimum.graphcore import IPUConfig
+ipu_config = IPUConfig.from_pretrained("Graphcore/convnext-base-ipu")
+```