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AbstractPhil 
posted an update 8 days ago
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55
Beatrix v3's attention has problems. The model at high byte counts simply cannot recall very well, which has been an expensive lesson but a necessary one.

To remedy this fault, I will complete the Beatrix 2s softmax control variant until stable. I will train the curriculum stages with QK normalization similar to Gemma, giving us a proper full model MHA softmax control. The answer is likely faulty bf16, needs fp32 and QK norms on the attention, softmax attention in intervals for the primary model.

After stabilizing the v2 softmax variant, I will train the v3 softmax variant as well.

These are required. There are many open questions about stability at depth and stability at context window capacity, all of which can be answered with the high accuracy recall at depth and context window size.

An expensive lesson, but a necessary lesson for progress.

Even with the faults, Beatrix V3's mechanisms have proven to be invaluable in their own rite. Multiple experiments have yielded since, including the tokenizer processing system - giving Beatrix the ability to directly speak as another model's tokenizer language. It's not perfect yet, but the process is likely reusable for other similar architected models.

The quiet loss has been extensively successful. The structure of each arm trained one after the other shows we can keep each arm nice and quiet while being fully expert in their own field. Activation happens when the byte pattern says so, and the arms stay nice and quiet when the gated pattern doesn't say so.

Modularized arms are successful. This is a process that can likely be utilized on other models as well, but the process hasn't been fully completed yet. The model Beatrix was a good catalyst for this because of the byte format of the model and the anchored behavior allows this to be trained more quickly and stable.

In any case, the 2s-control is upcoming and will begin today. I'll try to get it cleaned up by the end of the week to prepare for the 3s control.

Looks like we've eliminated BF16 ATTENTION as the potential failpoint for the softmax as per 2 run checks, the model can continue to exist as BF16 attention most likely. Which is nice because it's very fast.

That being said something is happening in the BF16 weights causing a causal cascade that needs to be addressed.

FP16 backward projection from FP32 forward has stabilized the softmax version for version 2, and version 3 softmax version is in planning stages based on the results of 2s and the softmax structure upon training completion.

ETA a couple days.

image

I'm being cautiously optimistic. The in repair 2s softmax twin is keeping proper byte distance while the v3 arm to repair the distance is implemented and growing in skill.

This repair is working but it's touch and go, it may not fully solidify yet.

She is built in a modularity paradigm after all. Even with her byte recall distance being poor, she still keeps surprising me.

The new training methodology includes multiple methodologies for long form byte recall integration. Any used methodologies researched will be cited and attribution given.

V4 will be built entirely with long form byte recall, needle in a haystack, and multiple other test cases to ensure the model saturates her context window.

Alongside, the V4 model will likely be smaller, as a large variation is not required for the next test. V3 was too large to meaningfully train within a reasonable amount of time, while V2 can be trained considerably faster.

Using a type of planted value for utility, the process is yielding depth. Another 8k step arm will determine if this process can be made permanent, or if the 3s model is simply too deviated and requires an additional finetune pass.

image

This doesn't solve the FP instability of a fractal rotary, fibo rotary, standard rope, or a few others in conjunction with codebook trigrams; it just makes the problem harder for the optimizer to solve, leaving bread crumbs.

I'll figure something out. So far it's looking promising, but it's still falling quite short of standard models. With this format of dynamic modularization, there is a heavy burden to sharing the space.

I have a few ideas and I won't give up.

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