research-contributions VS gpt-2

Checkout the MONAI library: https://monai.io/. It is tailored towards processing of medical images. It offers a label module which can be used for annotation, as well as a deep learning module with a vast range of models and medical data pipelines.

You can find a pretrained Resnet, but probably not one that's been trained on a kidney object detection dataset. The only kidney CT dataset I know of is for segmentation, not object detection. So you'll have to convert the segmentations to bounding boxes and train your own. Take a look at monai.io for potential resources.

Github: https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR

Medium models: Roughly between 1B to 10B parameters. This is where Mistral 7B, Phi-3, Gemma from Google DeepMind, and wizardlm2 sit. Fun fact: GPT 2 was a medium sized model, much smaller than its latest versions.

"Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. "

They did release GPT-2 under the MIT License.

Did you arrive at this certainty through reading something other than what OpenAI has published? The document [0] that describes the training data for GPT-2 makes this assertion hilarious to me.

[0]: https://github.com/openai/gpt-2/blob/master/model_card.md#da...

I made a clone of https://github.com/openai/gpt-2 on my local laptop