The Ledger
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Bachman: Models claiming 256k+ context use windowed transformers, discarding data
“Anybody who says they're using a transformer
With a context length of, you know, 256,000 or more, they're not using a true transformer.
What they're using is a windowed transformer that essentially throws out a huge amount of its information at various layers …”
Bachman: Power Retention Delivers 100x Inference Speedup at 64k Context
“And at 64 K tokens, We get something like a 10 X speed up at training, but at inference time, because you're not only saving flops at inference time, but also paging in and out of memory of the KV cache, you actually get a hundred X speed ups from power retent…”
Bachman: Power Retention models match original base model performance
“They'll come out with a nice shiny new, a power retention architecture that has the same performance on whatever data set they want as the original base model did.”
Bachman: StarCoder-3B converted to Power Retention matches baseline loss in two hours
“After just 10,000 steps of training, which this training one took about two hours, this orange curve, you see that it fully matches the original loss.”
Bachman: Big Foundation Models Will Train on Power Retention Within a Year
“After that, I think, you know, probably within six months to a year, we're going to start to see the really big foundation models being trained in this way.”
Bachman: Compute-optimal models on internet text don't need long context
“In general, most internet text has mostly short-term structure. There's just not that much value in capturing long-term structure, and so compute optimal models on internet text actually don't have that long context, and so, of course, you're perfectly fine us…”
Bachman: Manifest AI is releasing Power Retention architecture with fixed-size memory
“Power retention is the specific variant that we're about to release.
And it basically works by instead of the memory constantly growing, this constantly
Growing KVCache.
You have a memory that is a fixed size and each new token simply gets compressed into this…”
Bachman: Manifest Switched Power Retention from Triton to Custom CUDA
“Actually, our initial version of power retention was written in Triton, but we realized quickly that it just didn't offer the flexibility to really squeeze the performance that we wanted out of the GPU. So we took a step back and dove into CUDA.”
Bachman: PowerCoder-3B reaches 35% HumanEval accuracy versus StarCoder's 30%
“In the end, this converges to, I believe, about 35% accuracy on human eval, whereas the star coder baseline was about 30%.”
Bachman: Power Retention avoids quadratic compute scaling during long-context training
“So yeah, but we don't pay a quadratic cost. If you were looking at the star coder baseline, it would get even more, more expensive way more quickly.”
Bachman: 90% of documents in web pre-training datasets are under 2k context
“If you take like any old, like the pile or one of the more modern ones, basically any common crawl derived, scrape, open web text, any of these things, you're going to find that almost all documents are very short. There's some long context documents, but the …”
Bachman: Manifest AI built Manifesto to repair repositories using PowerCoder-3B
“We made a tool called Manifesto, where basically you can point it at any repo, including a very large repo, and it will basically fix any in that repo, or do its best to, of course. It's only a three billion parameter model, so, you know, it has its limitation…”