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.

Take a look at the link in the blogposts. Here is a github link as well: https://github.com/guidelabs/steerling. The model weights are on huggingface, so you can play with it.

Great questions. We weren't quite explicit about the training data attribution process. We'll discuss this in more detail in future work. We can track down which parts of the training data were interpolated to create that sentence. For those training data sentences, we then compare the concepts between generated and training.

We can attribute to exact sentences and chunks in the training data. For the first release, we are sharing only concept similarities. Over the coming weeks, we'll share and discuss how you can actually map to the exact training sentence and chunk with the model.

For a technical overview of how some of these models work, check this link out: https://www.guidelabs.ai/post/prism/

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.

Down to the very exact text chunk in a document! Check this out for an idea of what smaller versions of this style of model can do: https://www.guidelabs.ai/post/prism/. We'll have more to say soon about it. We can trace any generation to 11B chunks (not documents, but actual chunks in the training data).

Yes, that is the post that has the most up to date details of the model architecture. Take a look at this: https://github.com/guidelabs/steerling. It has the scaffolding for what you need :)

You are exactly right, it is guiding the model, during training, with concepts and the dictionary. This is important because dictionary learning for interpretability (post hoc) is not currently reliable: https://www.arxiv.org/abs/2602.14111

It is impossible to completely get rid of hallucinations. However, this can tell you exactly why the model hallucinated.

Exactly! We will have a future post that shows this more granularly over the coming weeks. Here is a post we wrote on how this works at smaller scale: https://www.guidelabs.ai/post/prism/

Our decomposition allows us to answer question like: for 84 percent of the model's representation, we know it is relying on this concept to give an answer.

We can also trace its behavior to the training data that led to it, so that can show us where some of these concepts are formed from.

Thanks for pointing this out. LLama 3 8B was trained on ~15T tokens. The Qwen models on 15-18T tokens as well. We trained on 1.35T tokens, and are within shot of these models on benchmarks. We expect to, at the very minimum, match these models' performance when we scale our token budget.

One side effect that we are excited about is that interpretable model training might make for a data efficient training process.

Good point. Historically, people have thought that there is a interpretability vs quality/performance tax. This is not true; at least not in this case.

Here are a bunch of questions you can answer without any quality degradation with interpretable models: 1) what part of the input context led to the output chunk that the model generated? 2) what part of the training data led to the output chunk?

In this case, we go more invasive, and actually constrain the model to also use human understandable concepts in its representations. You might think this leads to quality trade-offs. However, if you allow for the model to discover its own concepts as well (as long as they are not duplicates of the concepts you provided it), you don't see huge degradation.

I agree with the other commenters that this now gives us a huge boost in debugging the model.

sounds great! Will follow up via email.

Thanks, it is certainly a first step.

You are missing a few things, but you got some things right.

1) The is not an SAE in the way you think. It is a combination of a supervised + unsupervised layer that is constrained. An SAE is typically completely unsupervised, and applied post hoc. Here, we supervise 33k of the concepts with concepts that we carefully curated. We then have an unsupervised component (similar to a topk SAE) that we constrain to be independent from the supervised concepts. We don't do any of this post hoc by the way; this is a key constraint. I"ll get back to this. We train that unsupervised layer along with the model during pre-training.

2) Are the concepts or features causally influential for the output? We directly use the combination of the concepts for the lm head, which is a linear transform (with activation), so we can tell you, in closed form, the effect of ANY concept on the output logit for any token (or group of tokens) generated. It is not just causally related, it is constrained to do so.

3) Other points: we also make it so that you can trace the model outputs to the training data. This is an underrated interpretability knob. You know where, and what data, caused your model to learn a particular feature.

This is already a long comment, but I want to close on why our approach sidesteps all the issues with SAEs. - If you train an SAE twice, on the same data + model, you'll get two different feature(s). - In fact, there is no reason, why the model should pick features that are causally influential for the output. - ALL of these problems stem from the fact that the SAE is trained AFTER you already trained your model. Training from scratch AND with supervision allows you to sidestep these issues, and even learn more disentangled representations.

Happy to more concretely justify the above. Great observations!

It does :) We constrained the model to do exactly this during training: https://www.guidelabs.ai/post/scaling-interpretable-models-8....

You got it exactly right. Guilty as charged. Over the coming weeks, we will be showcasing exactly how you can debug all of these examples.

I agree that attribution is most useful for debugging and auditing. This is a prime usecase for us. We have a post with exciting results lined up to do this. Should be out in a week, we wanted to even just get the initial model out :)

Great questions. We have several posts in the works that will drill down more into these things. The model was actually designed to answer these questions for any sentence (or group of tokens it generates).

It can tell you which specific text (chunk) in the training data that led to the output the model generated. We plan to show more concrete demos of this capability over the coming weeks.

It can tell you where in the model's representation it learned about science, art, religion etc. And you can trace all of these to either to input context, training data, or model's representations.

Most interpretability techniques haven't yet to be shown to be useful for everyday model pipelines. However, the field is working hard to change this.

Completely agree with all your points!

Here is what this model does: it `rewrites` the model's activations (during pre-training) into supervised + unsupervised concepts that are then decoded into tokens. So at pre-training, we constrained the model with 33k supervised concepts (e.g., sports, toxicity, alignment, demographic variables), and then have more (101k) unsupervised concepts for the model to learn as well.

Overall, the architecture and loss functions of this model allow you to answer the following questions: 1) Which token in the context caused a chunk (group of tokens) to be generated? 2) which high level concept (supervised or unsupervised) caused the 3) perhaps more interestingly, in a single forward pass, we can tell you which training chunk led to the output of the model as well.

We do all of this for the single steerling model which is 8B parameters trained on 1.5T tokens. First time any model of this scale has achieved this level of interpretability by design.

would be happy to answer more questions.

op here. Important point, but I disagree. We see explainability/interpretability as a CORE need for AI safety. We believe you can't align/audit/debug/fix a system that you don't understand.

Just to give you some answers for what we can do:

1) We can find the training data that is causing a model to output toxic/unwanted text and correct it. 2) We know what high level concepts the model is relying on for any group of tokens it generates, hence, reducing that generation is as simple as toggling the effect of the output on that concept.

Most of the AI safety techniques fall under finetuning. Our model allows your to do this without fine-tuning. You can toggle the presence of .

For example, wouldn't you like to know why a model is being sycophantic? Or Sandbagging? Is it a particular kind of training data that is causing this? Or is it some high level part of the model's representations? For any of this, our model can tell you exactly why the model generated that output. Over the coming weeks, we'll show exactly how you can do this!

op here, I mostly agree with your comment! However, our model does more than this. For any chunk the model generates, it can answer: which concept, in the model's representations, was responsible for that token(s). In fact, we can answer the question: what training data caused the model to be generated too! We force this to be a constraint as part of the architecture and the loss function for our you train the model. In fact, you can get are the high level reasons for a model's answer on complex problems.