LLMs

The Gemini API context caching feature is designed to reduce the cost of requests that contain repeat content with high input token counts.

When to use context caching

Context caching is particularly well suited to scenarios where a substantial initial context is referenced repeatedly by shorter requests. Consider using context caching for use cases... See more

Mem0: The Memory Layer for Personalized AI

Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.

Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the embedchai

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Deploying a Generative AI model requires more than a VM with a GPU. It normally includes:

• Container Service : Most often Kubernetes to run LLM Serving solutions like Hugging Face Text Generation Inference or vLLM.


• Compute Resources : GPUs for running models, CPUs for management services


• Networking and DNS : Routing traffic to the appropriate

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When we deliver a model we make sure we don't reach X seconds of latency in our API. Before even going into performance of LLMs for classification, I can tell you that with the current available tech they are just infeasible.

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LinuxSpinach

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5h ago

^ this. And especially classification as a task, because businesses don’t want to pay llm... See more

TorchMultimodal (Beta Release)

Introduction

TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale. It provides:

• A repository of modular and composable building blocks (models, fusion layers, loss functions, datasets and utilities).


• A repository of examples that show how to combine these building

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Ensuring availability during peak traffic by maintaining all GPU instance types could lead to prohibitively high costs. To avoid the financial strain of idle instances, we implemented a “standby instances” mechanism. Rather than preparing for the maximum potential load, we maintained a calculated number of standby instances that match the... See more

One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more

My $0.02 is that a lot of the future research/work there will be figuring out how to identify effective sub-graphs to provide additional context, to avoid having to pass in the entire graph. As well as trying to identify ontology-less structures in real-time, which includes NER and RE, as well as named entity/relationship... See more

The quality of dataset is 95% of everything. The rest 5% is not to ruin it with bad parameters.