#python #gpt #gpt_2 #gpt_3 #language_model #transformers

https://github.com/EleutherAI/gpt-neo

#cuda #training #inference #transformer #bart #beam_search #sampling #bert #multilingual_nmt #gpt_2 #diverse_decoding

https://github.com/bytedance/lightseq

#python #deep_learning #gpt_2 #source_code

https://github.com/VHellendoorn/Code-LMs

#python #bert #chatgpt #chatgpt_api #chatgpt_python #chatgpt3 #gpt_2 #gpt_3 #gpt_3_prompts #gpt_neo #gpt3_library #large_language_models #openai #prompt_engineering #prompt_toolkit #prompt_tuning #prompting #prompts #transformers

https://github.com/promptslab/Promptify

#python #attention_mechanism #deep_learning #gpt #gpt_2 #gpt_3 #language_model #linear_attention #lstm #pytorch #rnn #rwkv #transformer #transformers

https://github.com/BlinkDL/RWKV-LM

#python #auto_regressive_model #autoregressive_models #diffusion_models #generative_ai #generative_model #gpt #gpt_2 #image_generation #large_language_models #neurips #transformers #vision_transformer

VAR (Visual Autoregressive Modeling) is a new way to generate images that improves upon existing methods. It uses a "next-scale prediction" approach, which means it generates images from coarse to fine details, unlike the traditional method of predicting pixel by pixel. This makes VAR models better than diffusion models for the first time. You can try VAR on a demo website and generate images interactively, which is fun and easy. VAR also follows power-law scaling laws, making it efficient and scalable. The benefit to you is that you can create high-quality images quickly and easily, and even explore technical details through provided scripts and models.

https://github.com/FoundationVision/VAR