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A quick update about posting cadence.
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Pipeline parallelism as a bandaid on memory limitations
Training on multiple GPUs (ML Systems I)
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Large models as engines of computation
Using models for more than just generation
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Distilling models makes them feasible to use
The importance of making models smaller
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Quadratic Complexity holds back the legendary transformer (Part 2)
Removing the attention block altogether
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Quadratic Complexity holds back the legendary transformer (Part 1)
The next generation of architectures.
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AMKS
Composing models together makes them powerful
A look into what chaining models together looks like
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Multi-modal models are the future
Google sticks a robot onto an LLM, or an LLM onto a robot
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