LLMs

What’s the best way for an end user to organize and explore millions of latent space features?

I’ve found tens of thousands of interpretable features in my experiments, and frontier labs have demonstrated results with a thousand times more features in production-scale models. No doubt, as interpretability techniques advance, we’ll see feature maps... See more

• Query the RAG anyway and let the LLM itself chose whether to use the the RAG context or its built in knowledge


• Query the RAG but only provide the result to the LLM if it meets some level of relevancy (ie embedding distance) to the question


• Run the LLM both on it's own and with the RAG response, use a heuristic (or another LLM) to pick the best answer

... See more

Here's my read on the situation:

* The TAM is massive, still so many businesses trying to figure out AI

* If you do deployments you’ll need to spend a of time hand holding clients through scoping projects (not unlike other dev works) since the material is so new

* Lot’s of opportunity in education

* The hard part isn’t the expertise, it’s distribution... See more

Humans are bad at coming up with search queries. Humans are good at incrementally narrowing down options with a series of filters, and pointing where they want to go next. This seems obvious, but we keep building interfaces for finding information that look more like Google Search and less like a map.

All information tools have to give users some... See more

Fine-Tuning for LLM Research by AI Hero

This repo contains the code that will be run inside the container. Alternatively, this code can also be run natively. The container is built and pushed to the repo using Github actions (see below). You can launch the fine tuning job using the examples in the https://github.com/ai-hero/llm-research-examples... See more

What is Substrate?

Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models – tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more

In general, I see LLMs to be used in two broad categories: data processing, which is more of a worker use-cases, where the latency isn't the biggest issue but rather quality, and in user-interactions, where latency is a big factor. I think for the faster case a faster fallback is necessary. Or you escalate upwards, you first rely on a smaller more... See more

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