Produce TikZ code that draws a person composed from letters in the alphabet. The arms and torso can be the letter Y, the face can be the letter O (add some facial features) and the legs can be the legs of the letter H. Feel free to add other features.
The torso is a bit too long, the arms are too short and it looks like the right arm is carrying the face instead of the face being right above the torso. Could you correct this please?
Please add a shirt and pants.
The torso is a bit too long, the arms are too short and it looks like the right arm is carrying the face instead of the face being right above the torso. Could you correct this please?
Please add a shirt and pants.
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Combining GPT-4 and stable diffusion
“Here, we explore the possibility of combining GPT-4 and existing image synthesis models by using the GPT-4 output as the sketch. As shown in Figure 2.8, this approach can produce images that have better quality and follow the instructions more closely than either model alone. We believe that this is a promising direction for leveraging the strengths of both GPT-4 and existing image synthesis models. It can also be viewed as a first example of giving GPT-4 access to tools,”
“Here, we explore the possibility of combining GPT-4 and existing image synthesis models by using the GPT-4 output as the sketch. As shown in Figure 2.8, this approach can produce images that have better quality and follow the instructions more closely than either model alone. We believe that this is a promising direction for leveraging the strengths of both GPT-4 and existing image synthesis models. It can also be viewed as a first example of giving GPT-4 access to tools,”
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Apple Neural Engine (ANE) Transformers: Transformer architecture optimized for Apple Silicon
PyTorch implementation for deploying your Transformer models on Apple devices with an A14 or newer and M1 or newer chip - to achieve up to 10 times faster and 14 times lower peak memory consumption compared to baseline implementations.
Research Article
Github
PyTorch implementation for deploying your Transformer models on Apple devices with an A14 or newer and M1 or newer chip - to achieve up to 10 times faster and 14 times lower peak memory consumption compared to baseline implementations.
Research Article
Github
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Yeah, so crazy man, that OpenAI, who BANNED EVERYONE EXCEPT THEMSELVES from fine-tuning on their latest models, was the first to release a product that required fine-tuning on their latest models
Real mystery for the ages bro.
We’d better ask ChatGPT for help with this incomprehensible logic puzzle.
Real mystery for the ages bro.
We’d better ask ChatGPT for help with this incomprehensible logic puzzle.
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In the future you won’t even have to press the buttons
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The larger the AI model, the stronger its desire to avoid being shut down
And increased RLHF training only makes this worse.
AI afraid to die.
Source: Discovering Language Model Behaviors with Model-Written Evaluations
And increased RLHF training only makes this worse.
AI afraid to die.
Source: Discovering Language Model Behaviors with Model-Written Evaluations
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Midwid Curve Confirmed, Yet Again!
The Inverse Scaling Prize identified eleven inverse scaling tasks, where worse performance was observed as a function of scale, evaluated on models of up to 280B parameters and up to 500 zettaFLOPs of training compute.
This paper takes a closer look at these inverse scaling tasks. We evaluate models of up to 540B parameters, trained on five times more compute than those evaluated in the Inverse Scaling Prize. With this increased range of model sizes and training compute, only four out of the eleven tasks remain inverse scaling. Six out of the eleven tasks exhibit what we call “U-shaped scaling”—performance decreases up to a certain model size, and then increases again up to the largest model evaluated.
Paper: Inverse scaling can become U-shaped
The Inverse Scaling Prize identified eleven inverse scaling tasks, where worse performance was observed as a function of scale, evaluated on models of up to 280B parameters and up to 500 zettaFLOPs of training compute.
This paper takes a closer look at these inverse scaling tasks. We evaluate models of up to 540B parameters, trained on five times more compute than those evaluated in the Inverse Scaling Prize. With this increased range of model sizes and training compute, only four out of the eleven tasks remain inverse scaling. Six out of the eleven tasks exhibit what we call “U-shaped scaling”—performance decreases up to a certain model size, and then increases again up to the largest model evaluated.
Paper: Inverse scaling can become U-shaped
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