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Links table

1. introduction
2. background
3. Implementing a mixture of depths

   • 3.1. Determine an account budget

   • 3.2. Guidance around transformers

   • 3.3. Guidance charts

   • 3.4. Implementation of guidance

   • 3.5. Samples and 3.6. Training methods

4. results
   • 4.1. Training, ISOFLOP comparisons
   • 4.2. Automatic evaluation evaluation and 4.3. A mixture of depth and experts (situation)
5. Discussion and references

4. Results

4.1. Training, ISOFLOP comparisons

We first trained models with a relatively small stirring budget (6E18) to define optimum scales (see Figure 3). In general, we found that MD transformers pull the basic ISOFLOP curve “down and right. This means that the optimal Ministry of Defense transformer achieves a lower loser than the optimal basic line, and also has more parameters. The fortunate result of this effect is the presence of smaller Mod models, although it is not the same is the optimal ISOFLOP for excessive preparation, but it is nevertheless better than the optimal basis model although they are faster in the step. For example, the parameter variable exceeds 220m (Figure 3) based on the optimal foundation line (also 220 meters, Figure 3 No. 1), but it is more than 60 % faster in training. It is important, when operating on equivalent devices, these two model variables take almost the same amount of wall time for training (Figure 3).

We tested directing each block or each other block, using capacities from 12.5 % to 95 % of the total sequence. Although directing each other block was very important to strong performance, we found that reducing aggressive capabilities was better (gradual improvements were observed when the capacity was reduced to 12.5 % of the total sequence, the corresponding to 87.5 % of the symbols that revolve around the blocks, With the deterioration of performance, behind this point). Therefore, it appears that the network is strong for large discounts of capacity as long as there is a frequent chance for full self -account and MLP accounts.

The directive is very important, as the Ministry of Defense transformers that use random guidance (which were implemented using higher operation 𝑘 on the weights of the router from which samples were taken from Gaousi distribution) perform largely worse than both the baseline and the natural MD transformer ( Figure 3).

As shown in Figure 4 is the ISOFLOP analysis of 6e18, 2E19 and 1E20 total. The trend of the MD transformers of volatility on more than the baseline is continuing with these larger flipping budgets. It is worth noting that there are MD variables that exist significantly faster in the ISOFLOP -PITORTION line (measured as steps per second when training on equivalent devices) with a lower loss (in Figure 4, we film natural fluctuations for each pass instead of the wall -a step in In itself, but from our experiences the two are closely related.

Wise speed gains come from two sources. First, the stirring rate for each parameter is lower than the foundation lines because some of the symbols are directed around the blocks. Therefore, for the size of a specific model, the transformer requires less volatility for each pass. Second, since Mod ISOFLOP-Optimal transformers are larger and a lower loss than the ISOFLOP-Optimal foundation, there are smaller MD variables that work well or better than the ISOFLOP-Optim foundation. Smaller. In general, then, there are the Ministry of Defense transformers that are performing it as well as the ISOFLOP-Optimal foundation and are faster in the step, because they use fewer fluctuations for each teacher and because they use fewer parameters.

Figure 4 also reveals another important discovery: The optimal MD transformer uses the largest number of fluctuations for each pass, such as the optimal foundation line. This discovery allows to predict directly to the MD size transformer, which will perform optimally for a specific ISOFLOP training budget: one only needs to adjust the size of the model for a specific MD formation (i.e., capacity frequency and steering frequency) to produce a model that uses the largest possible number that flies for each pass as well as such as Isflop-Optimal, and will have an optimal MD variable for this Composition. Experimental, we find that it is better to add a depth than adding the width when adding fluctuation to the form.

However, although the fluctuations for each of the front pass determines the model that will be the optimal ISOFLOP, it does not predict whether the optimum loss will improve in the foundation line (see Figure 3. It is best to use 12.5 % capacity blocks, each other block.

We have noticed that MD transformers have memory savings in relation to larger sizes foundation, with some variables that require fewer total devices (i.e. smaller TPU, TPU). We did not study this widely, but we expect that as one reference to the larger models, these savings can be important when choosing the typical variables of training, and can have great positive effects regarding the size of KV cache during automatic samples.

Figure shows 5 guidance decisions for the trained Ministry of Defense with interlocking steering blocks. Despite the aggressive guidance about the blocks, transformers are able to achieve performance improvements for basic lines. We note the patterns that may require more study; It seems that some symbols are involved in each block along the depth of the transformer, while others decide to guidance around the blocks whenever possible. Initial analyzes indicate that the symbols that interact with blocks are more frequently associated with the output predictions that have a higher entropy, which may correspond to the difficult predictions of it.

4.2. Automatic automatic evaluation

We evaluated the Ministry of Defense variables during automatic samples (see Figure 6). Each model was tested on the same data that includes 256,000 sequences (500 meters). When switching from the upper guidance method to the predictive guidance method, we have noticed the deterioration of little performance. As in the preparation of training, the Ministry of Defense variables that work better than the ISOFLOP-Optimal foundation, while it requires fewer fluctuations for each pass forward. These results indicate that saving the account provided by the Ministry of Defense transformers must be translated beyond the preparation of the training.

4.3. A mixture of depth and experts (situation)

The Ministry of Defense technology can be combined naturally with MEE models (together from models) in addition to vanilla transformers. In Figure 7, we offer the results that show that the performance improvements provided by the MD compound with the results of MEE. We have tried two types: in the interim mode, which directs the distinctive symbols about or towards the blocks before the step of self -interest, and the integrated situation, which puts the direction of MD by integrating “non -operation” experts between traditional MLP experts. The first is useful because it allows the symbols to overcome the self -assault step, while the latter is useful because it simplifies the guidance mechanism. We have noticed that implementing the situation in the integrated way was clearly better than just reducing the ability of experts in the traditional MEE models, and relying on dropping the distinctive symbol to implement the remaining guidance. We believe that this is because with the integrated position mechanism, the distinctive symbols are explicitly learning to choose the remaining path around experts, instead of preferring an expert but it is dropped when it is implemented as lowering the capabilities.