ππ§ Thread: Unlock the Power of GPT & LLMs π§ π
1/ As a huge fan of ChatGPT, I am thrilled to share my insights on how to make the most out of Generative Pre-trained Transformers (GPT) and Large Language Models (LLMs) in this THREAD. Get ready for the future of AI! π₯π
3/ GPT and LLMs have a wide range of applications! They can be used for:
- Writing content ποΈ
- Generating code π»
- Conversational AI π€
- Language translation π And so much more! In short, they are shaping the AI world as we know it! β‘οΈ #RealWorldApplications
4/ Interested in building your own GPT model? Start with GPT-2 or GPT-3 models from @OpenAI! Follow this comprehensive guide on setting up the model using Python and PyTorch: github.com/openai/gpt-3#StartYourGPTJourney
5/ Collaboration is essential! Be a part of the amazing GPT community at the @OpenAI forum & GitHub. Learn from ML experts and gain insights from others working on similar projects. Growth blooms from collective knowledge!π‘ #Community#OpenAI#collaborate
6/ Remember, with great power comes great responsibility! GPT and LLMs are powerful tools but can potentially generate misleading or biased content. Be mindful, and always consider ethics and consequences when deploying these technologies. #EthicsInAI#ResponsibleAI
7/ Stay updated with the latest advancements in GPT and LLMs. Follow @OpenAI, subscribe to newsletters, and keep up with top AI-related publications. Knowledge is power in the world of rapid AI advancements! πͺππ° #StayInformed#AIFuture
8/ We've just scratched the surface in this thread, but I hope it sparks your curiosity and helps you harness the potential of GPT and LLMs. Together, we can revolutionize AI and realize a more automated yet humane world! ππ #AIRevolution#GPTfuture
If you are here, this thread was made using GPT4 by prompting
"Pretend you are an ML influencer that loves chatGPT; now create a Twitter thread to get max views about how to use GPT and LLMs."
It's just an experiment proposed by @cgarciae88, as we're flooded with these threads
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This week I will present at @NVIDIAGTC about our work with @weights_biases and @Steadysun on leveraging diffusion models to predict cloud movement on satellite imagery.
Let me tell you about the problem we are trying to solve:
The idea is using a conditional diffusion pipeline to "outpaint" the future, one frame at a time. To test our approach's work, we first solve for the much "simpler" Moving MNIST dataset.
Given three previous frames, the model predicts the 4th.