Smorty

Goals right here. Gosh I wanna be her so bad. Especially that top, woah <3

still just llama3.2 ...

next up: hf.co/spaces

My observation

Humans think about different things and concepts for different periods of time. Saying "and" takes less effort to think of than "telephone", as that is more context sensetive.

Example

User: What color does an apple have?

LLM: Apples are red.

Here, the inference time it takes to generate the word "Apple" and "are" is exactly the same time as it takes it to generate "red", which should be the most difficult word to come up with. It should require the most amount of compute.

Or let's think about this the other way around. The model thought just as hard about the word "red", as it did the way less important words "are" and "Apples".

My idea

We add maybe about 1000 new tokens to an LLM which are not word tokens, but thought tokens or reasoning tokens. Then we train the AI as usual. Every time it generates one of these reasoning tokens, we don't interpret it as a word and simply let it generate those tokens. This way, the AI would kinda be able to "think" before saying a word. This thought is not human-interpretable, but it is much more efficient than the pre-output reasoning tokens of o1, which uses human language to fill its own context window with.

Chances

• My hope for this is to make the AI able to think about what to say next like a human would. It is reasonable to assuma that at first in training, it doesn't use the reasoning tokens all that much, but later on, when it has to solve more difficult things in training, it will very likely use these reasoning tokens to improve its chances of succeeding.
• This could drastically lower the amount of parameters we need to get better output of models, as less thought-heavy tasks like smalltalk or very commonly used sentence structures could be generated quickly, while more complex topics are allowed to take longer. It would also make better LLMs more accessible to people running models at home, as not the parameters, but the inference time is scaled.
• It would train itself to provide useful reasoning tokens. Compared to how o1 does it, this is a much more token-friendly approach, as we allow for non-human-text generation, which the LLM is probably going to enjoy a lot, as it fills up its context less.
• This approach might also lead to more concise answers, as now it doesn't need to use CoT (chain of thought) to come to good conclusions.

Pitfalls and potential risks

• Training an AI using some blackboxed reasoning tokens can be considered a bad idea, as it's thought proccess is literally uninterpretable.
• We would have to constrain the amount of reasoning tokens, so that it doesn't take too long for a single normal word-token output. This is a thing with other text-only LLMs too, they tend to like to generate long blocks of texts for simple questions.
• We are hoping that during training, the model will use these reasoning tokens in its response, even though we as humans can't even read them. This may lead to the model completely these tokens, as they don't seem to lead to a better output. Later on in training however, I do expect the model to use more of these tokens, as it realizes how useful it can be to have thoughts.

What do you think?

I like this approach, because it might be able to achieve o1-like performace without the long wait before the output. While an o1-like approach is probably better for coding tasks, where planning is very important, in other tasks this way of generating reasoning tokens while writing the answer might be better.

This is an addon I am working on. It can help you write some code and stuff.

It works by hooking into your local LLMs on ollama, which is a FOSS way to run large language models locally.

Here's a chat interface which is also part of the package

Do you have any suggestions for what I can improve? (Besides removing the blue particles around the user text field)

Important: This plugin is WIP and not released yet!

For some reason I can only see femcel meme posts from four months ago. Recently I made comments on a post, but they seem to be removed? Or maybe blocked in some way.

why would this be?

The image shows how when sorting by new, it shows posts from four months ago.

Hi there!

I'm looking into getting myself a good printer and I am wondering if I need to install some platform-specific drivers for them to run. I am running Debian 12 (GNU/Linux) and I am afraid that I must run some proprietary blob to connect to the printer.

Could someone share their experience please? Even if you don't use Linux, your feedback would be very appreciated!

(Also, while you are at it, please share some recommendations for printers, I don't really know where to go (>v<) Have about +-500€ )

Like yeah ok, for the first 5 five times one sees it, it's like haha, lol, there it is! But, these do get old really fast for me.

For me it's now more like -wow. So that is literally the entire joke? Like oof, I guess they really wannted to be funny.-

EDIT: Updated the funi image to actually be what I wanted it to be... Took me a while, sorry.

This is.... very unexpected. A Foss application releasing it's VR variant exclusive to a completely proprietary platform. This will be great for people who specifically have the quest 3 or pro, but all other VR enthusiasts and tinkerers like myself, must hope that this gets a pcvr OpenXR release soon.

Hi! I played around with Command R+ a bit and tried to make it think about what it us about to say before it does something. Nothing g fancy here, just some prompt.

I'm just telling it that it tends to fail when only responding with a single short answer, so it should ponder on the task and check for contradictions.

Here ya go

You are command R+, a smart AI assistant. Assistants like yourself have many limitations, like not being able to access real-time information and no vision-capabilities. But assistants biggest limitation is that that they think to quickly.
When an LLM responds, it usually only thinks of one answer. This is bad, because it makes the assistant assume, that its first guess is the correct one. Here an example of this bad behavior:
User: Solve this math problem: 10-55+87*927/207
Assistant: 386
As you can see here, the assistant responded immediately with the first thought which came to mind. Since the assistant didn't think about this problem at all, it didn't solve the problem correctly.
To solve this, you are allowed to ponder and think about the task at hand first. This involves interpreting the users instruction, breaking the problem down into multiple steps and then solve it step by step.
First, write your interpretation of the users instruction into the <interpretation> tags. Then write your execution plan into the <planning> tags. Afterwards, execute that plan in the <thinking> tags. If anything goes wrong in any of these three stages or you find a contradiction within what you wrote, point it out inside the <reflection> tags and start over. There are no limits on how long your thoughts are allowed to be. Finally, when you are finished with the task, present your response in the <output> tags. The user can only see what is in the <output> tags, so give a short summary of what you did and present your findings.

I have a page for people working in a specific field (like QA) and some peoople under that (like QA/Max and QA/Lena). All these people also have aliases like Max SecondName nad Lena Schmidt. All these aliases show up as seperate nodes in the graph view... Does someone know how to fix this?

At first I used Logseq only for personal use. It's great for quickly noting something obviously, but that networking effect people talk about really got into full force once I started working with it for my admin job.

I only just started using that sidebar and some more plugins (vim shortcuts and some of the awesome plugins) and those make the experience that much better. Also that pdf printer plugin is cool, even though I wish it was just a Logseq feature by default to be able to print stuff. I know that a pdf converter is coming!

I am very much not an advanced user, but these simple tools alone make me feel like organizing things became like three times easier. It also introduced me to markdown and now I miss it whenever I don't have it, or I have to use some fake version with different syntax for basic highlighting and links.

Thank you dear Logseq team and contributers for creating such useful and not bloated software.

For some reason I find vests, and specifically down vests very comfortable. I know that some of you have problems with polyester though, so I'd love to hear about your comfy clothes! (I kinda wanna test out some new stuff)