Sparse autoencoders find composed features in small toy models

post by Evan Anders (evan-anders), Clement Neo (clement-neo), Jason Hoelscher-Obermaier (jas-ho), Jessica N. Howard (jnhoward) · 2024-03-14T18:00:43.339Z · LW · GW · 11 comments

Contents

  Summary
  Introduction
  Experiment Details
    ReLU Output Toy Models
    Sparse Autoencoders (SAEs)
    Synthetic Data Vectors with Composed Features
      Including One-hot Vectors
  Results
    Correlated Feature Amplitudes
    Uncorrelated Feature Amplitudes
    Does a Cosine Similarity Loss Term Fix This Problem?
    What if the SAE Actually Gets to See the True Features?
  Takeaways
  Future work
  Code
  Acknowledgments
  Citing this post
None
11 comments

Summary

• Context: Sparse Autoencoders (SAEs) reveal interpretable features in the activation spaces of language models. They achieve sparse, interpretable features by minimizing a loss function which includes an ${\ell }_1$ penalty on the SAE hidden layer activations. 
• Problem & Hypothesis: While the SAE ${\ell }_1$ penalty achieves sparsity, it has been argued [LW · GW] that it can also cause SAEs to learn commonly-composed features rather than the “true” features in the underlying data.
• Experiment: We propose a modified setup of Anthropic’s ReLU Output Toy Model where data vectors are made up of sets of composed features. We study the simplest possible version of this toy model with two hidden dimensions for ease of comparison to many of Anthropic’s visualizations.
  • Features in a given set in our data are anticorrelated, and features are stored in antipodal pairs. Perhaps it’s a bit surprising that features are stored in superposition at all, because the features in the very small models we studied here are not sparse (they occur every-other data draw so have S ~ 0.5 in the language of Anthropic’s Toy Models paper)^[1].
• Result: SAEs trained on the activations of these small toy models find composed features rather than the true features, regardless of learning rate or ${\ell }_1$ coefficient used in SAE training.
  • This finding largely persists even when we allow the SAE to see one-hot vectors of true features 75% of the time.
• Future work: We see these models as a simple testing ground for proposed SAE training modifications. We share our code in the hopes that we can figure out, as a community, how to train SAEs that aren’t susceptible to this failure mode.

The diagram below gives a quick overview of what we studied and learned in this post:

Introduction

Last year, Anthropic and EleutherAI/Lee Sharkey's MATS stream showed that sparse autoencoders (SAEs) find human-interpretable “features” in language model activations. They achieve this interpretability by having sparse activations in the SAE hidden layer, such that only a small number of SAE features are active for any given token in the input data. While the objective of SAEs is, schematically, to “reconstruct model activations perfectly and do so while only having a few true features active on any given token,” the loss function used to train SAEs is a combination of mean squared error reconstruction of model activations and an ${\ell }_1$ penalty on the SAE hidden layer activations. This ${\ell }_1$ term may introduce unintended “bugs” or failure modes into the learned features.

Recently, Demian Till [LW · GW] questioned whether SAEs find “true” features.  That post argued that the ${\ell }_1$ penalty could push autoencoders to learn common combinations of features, because having two common true features which occur together shoved into one SAE feature would achieve a lower value of the ${\ell }_1$ term in the loss than two independent “true” features which fire together.

This is a compelling argument, and if we want to use SAEs to find true features in natural language, we need to understand when this failure mode occurs and whether we can avoid it. Without any knowledge of what the true features are in language models, it’s hard to evaluate how robust of a pitfall this is for SAEs, and it’s also hard to test if proposed solutions to this problem actually work at recovering true features (rather than just a different set of not-quite-right ones). In this post, we turn to toy models, where the true features are known, to determine:

1. Do SAEs actually learn composed features (common feature combinations)?
2. If this happens, when does it happen and how can we fix it?

In this blog post, we’ll focus on question #1 in an extremely simple toy model (Anthropic’s ReLU output model with 2 hidden dimensions) to argue that, yes, SAEs definitely learn composed (rather than true) features in a simple, controlled setting. We release the code that we use to create the models and plots in the hope that we as a community can use these toy models to test out different approaches to fixing this problem, and we hope to write future blog posts that help answer question #2 above (see Future Work section).

The synthetic data that we use in our toy model is inspired by this post by Chris Olah about feature composition. In that post, two categories of features are considered: shapes and colors. The set of shapes is {circle, triangle, square} and the set of colors is {white, red, green, blue, black}. Each data vector is some (color, shape) pair like (green, circle) or (red, triangle). We imagine that these kinds of composed features occur frequently in natural datasets. For example, we know that vision models learn to detect both curves and frequency (among many other things), but you could imagine curved shapes with regular patterns (see: google search for ‘round gingham tablecloth’). We want to understand what models and SAEs do with this kind of data.

Experiment Details

ReLU Output Toy Models

We study Anthropic’s ReLU output model:

$h=Wx,$

$x^{\prime }=ReLU(W^{T}h+b)=ReLU(W^{T}Wx+b),$

Here the model weights $W\in R^{M\times N}$ and bias $b\in R^N$ are learned. The model inputs $x$ are generated according to a procedure we lay out below in the “Synthetic Data Vectors with Composed Features” section, and the goal of the model is to reconstruct the inputs. We train these toy models using the AdamW optimizer with learning rate ${10}^{-3}$, weight decay ${10}^{-2}$, ${\beta }_1=0.9$, and ${\beta }_2=0.999$. Training occurs over ${10}^4$ batches where each batch contains $N_b={10}^3$ data vectors. The optimizer minimizes the mean squared error loss:

$L=\frac{1}{N{N}_b}{\sum }_x||x-x^{\prime }|{|}_2^2.$

Sparse Autoencoders (SAEs)

We train sparse autoencoders to reconstruct the hidden layer activations $h$ of the toy models. The architecture of the SAEs is: 

$f=ReLU(W_{e}h+b_e),$

$\hat{h}=W_{d}f+b_d,$

Where the encoding weights $W_e\in R^{F\times M}$ and bias $b_e\in R^F$ and decoding weights W${}_d\in R^{M\times F}$ and bias $b_d\in R^M$ are learned. 

Sparse autoencoders (SAEs) are difficult to train. The goals of training SAEs are to:

1. Create a model which captures the full variability of the baseline model that it is being used to interpret.
2. Create a model which is sparse (that is, it has few active neurons and thus a low ${\ell }_0$ norm for any feature vector input, and its neurons are monosemantic and interpretable).

To achieve these ends, SAEs are trained on the mean squared error of reconstruction of model activations (a proxy for goal 1) and are trained to minimize the \ell_1 norm of SAE activations (a proxy for goal 2).

We follow advice from Anthropic’s January and February updates in informing our training procedure.

In this work, we train SAEs using the Adam optimizer with ${\beta }_1=0$ and ${\beta }_2=0.999$ and with learning rates $lr\in \{3\times {10}^{-5},{10}^{-4},3\times {10}^{-4},{10}^{-3},3\times {10}^{-3}\}$. We minimize the mean of the fractional variance explained (FVE) and the ${\ell }_1$ norm of the SAE hidden layer feature activations, so our loss function is

$L=\frac{1}{N_b}{\sum }_h(\frac{||h-\hat{h}|{|}_2^2}{||h|{|}_2^2}+\lambda ||f|{|}_1).$

The goal of minimizing the FVE instead of a standard squared error is to ensure our SAE is agnostic to the size of the hidden layer of the model it is reconstructing (so that a terrible reconstruction $\hat{h}=0$ always scores 1 regardless of dimensionality)^[2]. We vary the ${\ell }_1$ damping coefficient $\lambda \in \{0.01,0.03,0.1,0.3,1\}$. The SAEs are trained over $1.28\times {10}^8$ total data samples in batches sizes of $1024$ for a total of $125,000$ batches. The learning rate linearly warms up from 0 over the first 10% of training and linearly cools down to 0 over the last 20% of training. At each training step, the columns of the decoder matrix are all normalized to 1; this keeps the model from "cheating'' on the ${\ell }_1$ penalty (otherwise the model would create large outputs using small activations with large decoder weights).

Synthetic Data Vectors with Composed Features

A primary goal of studying a toy model is to learn something universal about larger, more complex models in a controlled setting. It is therefore critical to reproduce the key properties of natural language that we are interested in studying in the synthetic data used to train our model.

The training data used in natural language has the following properties:

1. There are many more features in the data than there are dimensions in the model.
2. Most features in the dataset appear rarely (they are sparse).
3. Some features appear more frequently than others (the probability of features occurring is a non-uniform distribution and the most-frequently-occurring and least-frequently-occurring features have wildly different probabilities).
4. Features do not appear in isolation. We speculate that features often appear in composition in natural language datasets.
   1.  For example, a subset of words in a sentence can have a specific semantic meaning while also being in a specific grammatical context (e.g., inside a set of parentheses or quotation marks).
   2. It’s possible that token in context features are an example of composed features. For example, it’s possible that the word “the” is a feature, and the context “mathematical text” is a feature, and “the word ‘the’ in the context of mathematical text” is a composition of these features.

In this post, we will focus on data vectors that satisfy #1 and #4 above and we hope to satisfy #2 and #3 in future work. To create synthetic data, we largely follow prior work [Jermyn+2022, Elhage+2022] and generate input vectors $x\in R^N$, where each dimension$x_i$ is a “feature'' in the data. We consider a general form of data vectors composed of m sub-vectors $x=[x_{s_1}{x}_{s_2},\cdots ,x_{s_m}],$ where those sub-vectors represent independent feature sets, and where each subvector has exactly one non-zero element so that $x_{s_i}\ne 0$; dimensionally, $x_{s_i}\in R^{N_{s_i}}$ with ${\sum }_{i=1}^m{N}_{s_i}=N$. 

In this blog post, we study the simplest possible case: two sets ($m=2$) each of two features $(N=4,N_{s_i}=2)$ so that data vectors take the form $x=[x_1,x_2,y_1,y_2]$. Since these features occur in composed pairs, in addition to there being four true underlying features $\{x_1,x_2,y_1,y_2\}$ there are also four possible feature configurations that the models can learn: $[x_1,0,y_1,0],[x_1,0,0,y_2],[0,x_2,y_1,0],$ and $[0,x_2,0,y_2]$. For this case, a 2-dimensional probability table exists for each composed feature pair giving the probability of occurrence of each composed feature set $p(x_i,y_j)$ where $x_i\in x_{s_1}$ and $y_i\in x_{s_2}$. We consider uniformly distributed, uncorrelated features, so that the probability of any set of features being present is uniform and is $(N_{s_1}{N}_{s_2}{)}^{-1}$, so the simple probability table for our small model is:

$p(x_i,y_i)$	$y_1$	$y_2$
$x_1$	0.25	0.25
$x_2$	0.25	0.25

The correlation between a feature pair $(x_{i^{\prime }},y_{j^{\prime }})$ can be raised by increasing $p(x_{i^{\prime }},y_{j^{\prime }})$ while lowering the probability of $x_{i^{\prime }}$ appearing alongside $y_j\forall j\ne j^{\prime }$ and the probability of $y_{j^{\prime }}$ appearing alongside $x_i\forall i\ne i^{\prime }$ (and properly normalizing the rest of the probability table). This is interesting and we want to do this in future work, but in this specific post we’ll mostly just focus on the simple probability table above.

To generate synthetic data vectors $\$x\$$, we randomly sample a composed pair $(x_i,y_j)$ from the probability table. We draw the magnitudes of these features from uniform distributions, $x_i\sim U(0,1)$ and $y_j\sim U(0,1)$. We can optionally correlate the amplitudes of these features using a correlation coefficient $C\in [0,1]$ by setting $y_j\leftarrow C{x}_i+(1-C)y_j$. Note that by definition, all features in $x_{s_1}$ are anticorrelated since they never co-occur, and the same is true of  all features in $x_{s_2}$. In this post, we study two cases:

1. $C=1$ for perfectly correlated feature amplitudes.
2. $C=0$ for perfectly uncorrelated feature amplitudes.

Including One-hot Vectors

In the experiments outlined above, all data vectors are two-hot, containing a nonzero value in some $x_i$ and a nonzero value in some $y_i$. One could argue that, for that data, regardless of $C$, the natural basis of the data is actually composed pairs and the underlying “true” features are less relevant.

We will therefore consider a case where there is some probability $0<p\left(one-hot\right)<1$ that a given data vector only contains one $x_i$ or one $y_i$ – but not both. We looked at $p\left(one-hot\right)\in \{0.5,0.75\}$, but in this blog post we will only display results from the $p\left(one-hot\right)=0.75$ case. To generate the probability table for these data, the table from above is scaled by $(1-p(one-hot\left)\right)$, then an additional row and column are added showing that each feature is equally likely to be present in a one-hot vector (and those equal probabilities must sum up to $p\left(one-hot\right)$). An example probability table for $p\left(one-hot\right)=0.75$ is:

$p(x_i,y_i)$	$y_1$	$y_2$	$\forall y_i,y_i=0$
$x_1$	0.0625	0.0625	0.1875
$x_2$	0.0625	0.0625	0.1875
$\forall x_i,x_i=0$	0.1875	0.1875	0

Results

Correlated Feature Amplitudes

We begin with a case where the amplitudes of the features are perfectly correlated $C=1$ such that the four possible data vectors are $A[1,0,1,0]$, $A[1,0,0,1]$, $A[0,1,1,0]$, and $A[0,1,0,1]$ with $A\sim U(0,1)$. Yes, this is contrived. The data vectors here are always perfect composed pairs. In some ways we should expect SAEs to find those composed pairs, because those are probably a more natural basis for the data than the "true" features we know about. 

As mentioned above, we study the case where the ReLU output model has two hidden dimensions, so that we can visualize the learned features by visualizing the columns of the learned weight matrix $W$ in the same manner as Anthropic’s work (e.g., here). An example of a model after training is shown in the left panel of this figure:

The features in the left panel are labeled by their $x_i$ and $y_i$, and all features are rotated for visualization purposes so that the $x$ features are on the x-axis. We find the same antipodal feature storage as Anthropic observed for anticorrelated features -- and this makes sense! Recall that in our data setup, $x_1$ and $x_2$ are definitionally anticorrelated, and so too are $y_1$ and $y_2$. Something that is surprising is that the model chooses to store these features in superposition at all! These data vectors are not sparse.^[1]  Each feature occurs in every other data vector on average. For a single set of uncorrelated features, models only store features in superposition when the features are sparse. Here, the model takes advantage of the nature of the composed sets and uses superposition despite a lack of sparsity.

We train five realizations of SAEs on the hidden layer activations of this toy model with a learning rate of $3\times {10}^{-4}$ and ${\ell }_1$ regularization coefficient $\lambda =0.3$. Of these SAEs, the one which achieves the lowest loss (reconstruction + ${\ell }_1$) is plotted in the large middle panel in the figure above (black arrows, overlaid on the model’s feature representations). This SAE’s features are labeled according to their hidden dimension in the SAE, so here e.g., $f_1$ is a composed feature of $x_2$ and $y_1$ like $A[0,1,1,0]$. The other four higher-loss realizations are plotted in the four rightmost sub-panels. We find a strong preference for off-axis features – which is to say, the SAE learns composed pairs. Each of the five realizations we study (middle and right panels) have this flaw, with only one realization finding even a single true underlying feature (upper right panel).

Can this effect, where the model learns composed pairs of features, be avoided simply through choosing better standard hyperparameters (learning rate and $\lambda$)? Probably not:

We scanned two orders of magnitude in both learning rate and $\lambda$. We plot the base model, the SAE which achieves the lowest loss out of five realizations (black vectors), and the SAE which achieves the highest monosemanticity out of five realizations according to Eqn. 7 in Engineering Monosemanticity (grey vectors). Only one set of hyperparameters achieves a mostly monosemantic realization: that at $\lambda =0.01$ and with a moderate lr of $3\times {10}^{-4}$. Perhaps this makes sense -- a large ${\ell }_1$ penalty would push the model towards learning composed features so that fewer features are active per data draw. However, we see that this realization is not perfectly monosemantic, so perhaps $\lambda$ is too low to even enforce sparsity in the first place.

Uncorrelated Feature Amplitudes

We next consider the case where the feature amplitudes within a given data vector are completely uncorrelated, with $C=0$, so that $x_i\sim U(0,1)$ and $y_i\sim U(0,1)$. Whereas in the previous problem, only four (arbitrarily scaled) data vectors could exist, now an infinite number of possible data vectors can be generated, but there still only exist two features in each set and therefore four total composed pairs. 

We perform the same experiments as in the previous section, and replicate the same figures from the previous section below. Surprisingly, We find that the model more cleanly finds composed pairs than in the case where the input data vectors were pure composed pairs. By breaking the feature amplitude correlation, SAEs almost uniformly learn perfect composed pairs for all parameters studied. We note briefly that, in the grid below, some SAEs find monosemantic features at high learning rate and low $\lambda$ (see the light grey arrows in the bottom left panels), but even when these monosemantic realizations are achieved, other realizations of the autoencoder find lower loss, polysemantic realizations with composed pairs.

Does a Cosine Similarity Loss Term Fix This Problem?

In Do sparse autoencoders find "true features"? [LW · GW], a possible solution to this problem is proposed:

I propose including an additional regularisation term in the SAE loss to penalise geometric non-orthogonality of the feature directions discovered by the SAE. One way to formalise this loss could be as the sum of the absolute values of the cosine similarities between each pair of feature directions discovered in the activation space. Neel Nanda's findings here suggest that the decoder rather than the encoder weights are more likely to align with the feature direction as the encoder's goal is to detect the feature activations, which may involve compensating for interference with other features. 

We tried this, and for our small model it doesn’t help. 

We calculated the cosine similarity between each column of the decoder weight matrix, $W_{dec}$, and stored those cosine similarity values in the square matrix $S\in R^{F\times F}$, where $F$ is the hidden dimension size of the SAE. $S$ is symmetric, so we only need to consider the lower triangular part (denoted tril($S$)). We tried adding two variations of an $S$-based term to the loss function:

• coeff * mean(tril($S$))
• coeff * mean(abs(tril($S$)) 

Neither formulation improved the ability of our autoencoders to find monosemantic features.

• In the first case (no abs() on $S$), we found that the models found the same solution as above. This makes sense! The four features that we find above are in a minimal cosine-sim configuration, it’s just rotated compared to what we want, and this $S$-based term doesn’t say anything about the orientation of features in activation space, just their distance from one another.
• For the second case (with abs() on $S$), we found either the same solution, or a solution where some feature decoder vectors collapsed (see below). This occurs because we normalize the decoder weights at each timestep, and it’s more favorable to have two features be aligned ($S_{ij}$ = 1) than it is to rotate a (magnitude 1) feature around to a more useful part of activation space.
  • For example, consider the case where we have two vectors that are aligned, and two vectors that are perpendicular, like in 3 of the panels in the figure below. One of the two aligned vectors has an angle of 0${}^{\circ }$, 90${}^{\circ }$ , and 90${}^{\circ }$ with the other vectors (cosine sims = 1, 0, 0). Rotating one of the aligned features to a more useful position requires it to pass through a spot where the angle between it and the three other features are 45${}^{\circ }$, 45${}^{\circ }$, and 135${}^{\circ }$ (cosine sims = $1/\sqrt{2}$, $1/\sqrt{2}$, and $-1/\sqrt{2}$). If we sum up the absolute values of the cosine sim, this rotated vector has a higher abs(cos-sim): 1 < $3/\sqrt{2}$, so the vectors stay collapsed and it doesn’t rotate around to a more useful location in activation space.

Just because this additional loss term did not help this small toy context does not mean that it couldn’t help find more monosemantic features in other models! We find that it doesn’t fix this very specific case, but more tests are needed.

What if the SAE Actually Gets to See the True Features?

In the experiments I discussed above, every data vector is two-hot, and an $x_i$ and $y_i$ always co-occur. What if we allow data vectors to be one-hot (only containing one of $x_i$ OR $y_i$) with some probability $p\left(one-hot\right)$? We sample composed data vectors with probability $1-p\left(one-hot\right)$. We tried this for $p\left(one-hot\right)=\{0.5,0.75\}$ and while SAEs are more likely to find the true features, it’s still not a sure thing – even when compositions occur only 25% of the time and feature amplitudes are completely uncorrelated in magnitude!

Below we repeat our toy model and SAE plots for the case where $p\left(one-hot\right)=0.75$. Certainly more SAEs find true features in the lowest-loss instance whereas with $p\left(one-hot\right)=0$, none did. But there’s no robust trend in learning rate and $\lambda$.

Takeaways

• Features in natural datasets can be composed, occurring in combination with other features. We can model this with synthetic data vectors and toy models. 
• Small toy models store composed feature sets using superposition.
• Sparse autoencoders (SAEs) trained on these composed feature sets can find composed features rather than the true underlying features due to the ${\ell }_1$ penalty in their loss function. 
  • This happens even when the SAEs get to see the true features 75% of the time!
• We should consider how to train SAEs that aren’t susceptible to this failure mode.
  • Strangely, this effect is worse when the composed pairs are less prominent in the input data (uncorrelated amplitude case) than it is in the case where the input data is always made up of these composed pairs. 

Future work

This post only scratched the surface of the exploration work that we want to do with these toy models. Below are some experiments and ideas that we’re excited to explore:

• The 2-sets-of-2-features case is really special. For two sets each of $N$ features, the number of possible composed pairs is $N^2$ while the number of total features is just $2N$. These are both 4 in our case (although the individual features occur once every other data vector while the composed pairs occur once every four data vectors).
  •  As $N$ grows, composed pairs become increasingly sparse compared to the true underlying features, and we expect SAEs to recover the true features when each of the composed pairs are sparse. We’re interested in understanding how sparse (or not!) a composed pair needs to be compared to the underlying true features to make a model switch between learning composed pairs and true features.
• In the limit where $N$ is large, we expect SAEs to learn true features and not composed pairs. But what if two specific features e.g., $(x_1,y_1)$ are very highly correlated (so that $x_1$ rarely occurs with any $y_i$other than $y_1$ and vice-versa)? 
• What if we use a different probability distribution so that frequency follows something interesting like Zipf’s law?
• What happens as superposition becomes messier than antipodal pairs? Here we studied a case where the hidden model dimension is half the length of the feature vector. What if we have 10 total features and the hidden dimension is 3 or 4?
• What happens if we have more than two sets of composed features?
• How can we engineer SAEs to learn the underlying features rather than the composed pairs? 
  • Can perfect SAEs be created using “simple” tweaks? E.g., adding terms to the loss or tweaking hyperparameters?
  • Is it essential to have some understanding of the training data distribution or the distribution of features in the dataset? How do SAEs perform for different types of feature distributions and how does the feature distribution affect the efficacy of engineering improvements?
  • It feels like it shouldn’t be too hard to come up with something that makes perfect SAEs for the 2-sets-of-2-features case we studied here. If we find something in these small models, does it generalize?

We may not have time to get around to working on all of these questions, but we hope to work on some of them. If you’re interested in pursuing these ideas with us, we’d be happy to collaborate!

Code

The code used to produce the analysis and plots from this post is available online in https://github.com/evanhanders/superposition-geometry-toys . See in particular https://github.com/evanhanders/superposition-geometry-toys/blob/main/experiment_2_hid_dim.ipynb .

Acknowledgments

We’re grateful to Esben Kran, Adam Jermyn, and Joseph Bloom for useful comments which improved the quality of this post. We’re grateful to Callum McDougall and the ARENA curriculum for providing guidance in setting up and training SAEs in toy models and to Joseph Bloom for his https://github.com/jbloomAus/mats_sae_training repository which helped us set up our SAE class. We thank Adam Jermyn and Joseph Bloom for useful discussions while working through this project. EA Thanks Neel Nanda for a really useful conversation that led him to this idea at EAG in February.

Funding: EA and JH are KITP Postdoctoral Fellows, so this research was supported in part by NSF grants PHY-2309135 and PHY-1748958 to the Kavli Institute for Theoretical Physics (KITP) and by the Gordon and Betty Moore Foundation through Grant No. GBMF7392.

Citing this post

@misc{anders_etal_2024_composedtoymodels_2d,
   title = { Sparse autoencoders find composed features in small toy models  },
   author = {Anders, Evan AND Neo, Clement AND Hoelscher-Obermaier, Jason AND Howard, Jessica N.},
   year = {2024},
   howpublished = {\url{https://www.lesswrong.com/posts/a5wwqza2cY3W7L9cj/sparse-autoencoders-find-composed-features-in-small-toy}},
}

1. ^{^}

   But note that here I’m defining sparsity as occurrence frequency. Probably there’s a truer notion of sparsity and in that notion these data are probably sparse.

2. ^{^}

   Though note that this is slightly different from Anthropic’s suggestion in the February update, where they chose to normalize their vectors so that each data point in the activations has a variance of 1. I think if you use the mean squared error compared to the squared error, this becomes equivalent to what I did here, but I’m not 100% sure.

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