Now here's a broader picture of how input embeddings are combined with Keys, Queries & Values to obtain the actual attention scores. After acquiring keys, queries, and values, we merge them to create a new set of context-aware embeddings. Check this out
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GPT Mentions Enable Seamless AI App Deployment for Free
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Wild! With the new mention capability for GPTs, I can create an app with Grimoire, and seamlessly ask DesignerGPT to deploy it.
— Pietro Schirano (@skirano) 27 janvier 2024
No change in instructions needed – it's pure intelligence at work. ✨
You can deploy all your ideas for free, with just a mention of @ DesignerGPT. pic.twitter.com/GyNBlBBYvxWild! With the new mention capability for GPTs, I can create an app with Grimoire, and seamlessly ask DesignerGPT to deploy it. No change in instructions needed – it's pure intelligence at work. You can deploy all your ideas for free, with just a mention of @ DesignerGPT.
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DesignerGPT Enables Code Deployment on Replit Servers
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What I love about this is that you don't even need a custom GPT. You can code some ideas in a regular chat sent to DesignerGPT, and boom – your code gets deployed on @Replit servers! Incredible.
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Free ChatGPT Plus and Gemini Ultra Code Interpreter
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– free chatgpt plus
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Running Data Through Neural Network Multiple Times
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thanks! but there’s no data loader here, and I’m not doing training; just running the same data through the network many times
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PyTorch Model Inference Optimization with torch.no_grad()
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this isn't the case bc i'm using the same image every time; my code looks like this for _ in range(n_iters): with http://
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GPU Inference Batch Size Scaling: Why Limited Speedup?
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when doing inference on lots of samples, how much speed up can you expect from increasing the batch size? context: i'm doing inference for lots of images using resnet100 on an a6000 gpu. increased batch size 32 -> 2048 (64x!) and only getting a 20% speedup. how is this possible?
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Accelerating Matrix Multiplication for LLM Efficiency
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Most of an LLM’s memory & compute are consumed by matrix multiplication operations. Today on the blog, learn about techniques used to accelerate mixed-input matrix multiplication for increased efficiency w/ performance close to peak hardware capabilities ↓
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Ludwig v0.9.3 Release: Phi-2 Support and LLM Improvements
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Announcing #Ludwig v0.9.3! Support for microsoft/phi-2 Ensure correct padding token for #Phi and Pythia models Cast LLMEncoder output to torch.float32, freeze final layer at init Enable IA3 adapters Add batch size tuning for #LLMs https://
pbase.ai/3HAOxCs