adebayoj

Hey HN! We're Guide Labs and we just launched Clarity, an AI platform powered by our Steerling-8B model.

You can: Click any chunk of output the model generates and see the concepts/ideas behind it. Trace the outputs back to the training data. You can also: Amplify or suppress concepts to control behavior.

Would love your feedback: happy to answer questions.

Hey HN we recently released Steerling-8B, an 8B model designed to be interpretable from the ground up. The model has ~100K concept slots it fills on its own during training, and we can read off what each one means by projecting into vocabulary space.

The model figured out things like British vs. American spelling, second-person pronouns across 6+ languages, and even broken Unicode.

Take a look, and let us know what you think.

This part of the claim is involved, so we have future posts to clarify this. And yes, you can remove a prototype and generate again. We show examples in that prism post.

In prism, for any token the model generates, you can say, it generated this token based on these sources. During training, the model is 'forced' to match all the prototypes to specific tokens (or group of tokens) in the data. The prototype itself can actually be exactly match to a training data point. Think of it like clustering, the prototype is a stand-in for training data that looks like that prototype, we force (and know) how much the model will rely on that prototype for any token the model generates.

The demo in the post is not as granular because we don't want to overwhelm folks. We'll show granular attribution in the future.

It is not hype. You can try the model on huggingface yourself to see its capabilities. My reply here was clarifying that the examples we showed were ones where the model didn't make a mistake. This is intentional, because over the next few weeks, we will show how the concepts, and attribution we enable can allow you to fix this mistakes more easily. All the claims in the post are supported by evidence, no marketing here.

We train the model with `explanations`. Most training asks the model to predict the next token or group of tokens. Our training says, predict the next group of tokens (causal diffusion), but also these tokens should be about {sports/art/coding/etc}. So in addition to token supervision, the model gets concept level supervision. The model is forced to more quickly learn these high level concepts.

You got it exactly right :) And you can update the attribution.md to have it NOT rely on opensource projects that have been compromised. Imagine asking claude code to write a package/function in the style of a codebase that you care about or force it to ALWAYS rely on some internal packages that you care about. The possibilities are endless when you insert such knobs into models.