[spam][crazy][fiction][random] Non-Canon MCBoss Spinoffs

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

2 part missive. part 1: coinbase hacks. part 2: tiny llm speed softtuning draft

Coinbase Hacks

It turns out that if you have a hold on funds on your account in
coinbase, if you raise your USD balance to match the hold e.g. by
closing orders in usdc, than you can transfer amounts higher than this
threshold. After the transfer you can then reopen the orders at the
same levels.

---

Tiny LLM Speed Softtuning Draft

I tried to draft a start around adapting existing LLMs to produce
output tokens in parallel rather than serially, kind of. Like
daydreaming (when we daydream we reconsider all parts as they seem
interesting).
This computer is dying and not charging likely due to a frayed
charging cable. The current draft is accessible via the arweave id in
the json below. I'm sending this now rather than a more put-together
post for that reason.
The purpose of the work is to make embedded decentralized use
workable. For example, if completed, this would let the Petals network
generate an entire paragraph in under a second. Usually it takes under
a second to generate a single token.
It was more relaxing than expected to work on this.
Note: The goal of adapting language models to produce their entire
context in a single pass is definitely doable, but the approach below
of a single token softprompt is very simplified and unlikely to fully
converge.
The reason this approach is more doable for me is it keeps things very
similar to existing work. The context is extended with dummy
embeddings for generating the extra tokens. Language models already
produce dense parallel output, it is simply conventional to only use
the last token.
Ways to make it more powerful:
- Insert more sequence embeddings before the output is collected. This
lets it perform more computation.
- Remove the causal mask. This lets information flow in both
directions for twice as much computation, but involves modifying the
inference architecture or library code a tiny bit.
- Rather than training a single embedding, train one for each
position. You can even train one for each underlying absolute position
embedding, as well as one for each relative position, and sum them.
- Alternatively, use e.g. LoRA or finetuning instead of soft prompts.
- Train the softprompt, adapter, or finetuning for use in a recurrent
manner and let it spend a few passes adjusting the output. (You can
also add another head to train e.g. a confidence % to decide whether
it is done recurring).

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