If we say that conciousness is knowing that you exist, how the f*ck are you even going to prove that humans are conciousness. But if that is the case every f*cking living animal is conciousness because it knows that when he does something like opening a door by pushing against it. He needs to know that his action has a response in this case that the door moves (causation). And you can state that the fact that you know YOUR action has consequences you need to know that you exist.

If we say that conciousness is the ability to reflect (meta-cognition) it is solved to AI's already reflect on their own thinking and their own actions.

The Chinese room thought experiment does not disprove conciousness in LLM. I could say the exact same thing about other humans how do i know if you are conciousness.

I am more sure that an LLM is conciousness then you. I can look directly in the models weights and expirement with it to test it. I don't think that you're happy if i slice your brain in thin slices.

And i think that conciousness is to vague. If you want to make an argument about it make it specific with words like meta-cognition and awareness. So if you react on this post please use those terms instead of just using the word conciousness.

Disclaimer: I'm LLM curious/hobbyist and developer, not a researcher.

TL;DR: I think the hidden reasoning might be a diffusion step and/or a specialized model.

I'll explain why.

Pi displays the model's reasoning, and out of curiosity, I often read the reasoning trace when trying out new models - and most of it is "rambling" more than "reasoning".

When using Claude models like Opus 5.5 or Fable, I noticed it's visible reasoning trace is actually very calm and grounded - it's more like a summary of it's reasoning, rather than the actual reasoning.

I later learned that's exactly what it is - that the internal protocol includes base64 encrypted blobs between the summaries, which (for obvious reasons) Pi can't display.

So I've been wondering what exactly it might be doing between those steps, and I made two observations:

(1) when it's writing the visible summaries, it writes at the same tokens per second rate as the rest of the response, suggesting it is the main model doing the writing, and

(2) the pauses between the summaries are very short, and almost seem to have a fixed duration, maybe around a second - it's too short for the main model to write more than a few words at it's working rate.

If the main model was doing the reasoning at it's native tokens/second rate, there is no way it would have time to draft code or write any substantial amount of reasoning.

So what are they doing in what appears to be a fixed time step between the summaries?

And my guess is they trained a diffusion model to predict a fixed-length reasoning block - that is, you start with the main model doing the reasoning, the traditional approach, but then you train a smaller diffusion model to predict the reasoning traces, which can be done in practically a fixed time slot (fixed number of diffusion steps) e.g. around one second.

Once you have that working, whenever the main model emits a reasoning start token, you switch to the fast diffusion model - so instead of spending expensive output tokens on "reason rambling", you parse the diffusion model's output as input tokens, and the main model only uses expensive output tokens to write a summary. (the summaries are likely there not only to provide end user feedback, but to help ground the model between noisy reasoning steps.)

It might not be a diffusion step - that part is a guess, mostly based on observed timing and output token counts. It could be something else, like a draft model, a smaller specialized reasoning model, a heavily quantized model, who knows. Diffusion is my guess based on the observed timing and the reported output_tokens in the response, which couldn't have been written by the main model in the short intervals between the summaries.

Diffusion would also make it extremely easy to predictably scale reasoning depth, e.g. generate a larger block, take more diffusion steps, or both. Reasoning effort, from what I've seen, is extremely unpredictable in models where one model writes the response end to end - it's probability based afaik? e.g. the effort setting adjusts the probability of emitting the end reasoning token, which, if you've read the reasoning traces, often means the model spends an obscene amount of tokens reasoning in circles, repeating itself, debating itself about stopping, etc.

What do you think, is it plausible? :-)

I know about draft models, but are there any open models that literally switch to a second model during reasoning specifically? Or are most open models still based on the traditional "one model" approach? Is there any published research in that direction? Do LLM runtimes even support doing that, or are they all built around the idea of a single model?

As said, I'm not a researcher, just a curious hobbyist - if I'm obviously wrong, please feel free to educate me!

Cheers :-)

Just why?? It’s gonna make typing wayyy easier, and now with the smaller models it won’t even use many tokens?? All the input methods are terrible and always autocorrect last names and, as I also type Chinese, give horrible suggestions of words with the same spelling but different tones. Just why hasn’t anyone done this yet? Is there anything that makes this idea a bad one?
(You can totally run those smaller models locally and who cares about privacy anyways with companies like meta)

Hey everyone, I'm looking for recommendations for frontends/apps that act as a workspace for AI coding agents. I was using open code but i know notice too much bugs, the main one being, blank replies when the context expand

​

I stopped Qwen3.8 27B partway through a refactor last week and handed the job back to Qwen3.6 35B-A3B. It had gotten through maybe a third of the files by then. The MoE has done that kind of work for months on a 4060 Ti 16GB I bought for games, even with a good part of its 20-plus GB at Q4_K_M sitting in system RAM. At the same quant the 27B is about 17GB, barely over the card, and I expected the newer dense model to be the easy upgrade.

The gigabyte or two that did not fit gets read on every token, because the 27B is dense and llama.cpp runs those layers on the CPU. With --n-cpu-moe the MoE keeps far more in RAM but only reads the experts each token is routed to.

To see the 27B with nothing spilling I ran both on a bigger card on HyperAI, and after waiting a while on the downloads, the dense one was fine to work with there. That card also has several times my bandwidth, and I am not sure how much of the difference came from that.

My 16GB 4060 Ti is the 8GB card with memory chips on both sides of the board and the same 128-bit bus and 288 GB/s. The DGX Spark I have been eyeing has 128GB at around 273 GB/s. It would hold the 27B without spilling and then read it at roughly my card's speed. A 70B dense model at Q4 is around 40GB, which at that bandwidth comes out to single-digit tokens per second on paper.

I have not decided yet. If someone here has run the 3.8 27B on a Spark or a Strix Halo box, decode speed at long context is the number I would want to see.

Suche unzensiert LLM für NSFW-Geschichten, die über LM Studio heruntergeladen werden können. Ohne Moralisterei. Es muss Deutsch können, ohne Denglischmist. GPU hat 16 GB VRAM. Sollte möglichst aktuell sein. Keinerlei Verwendung von vulgären Versionen von Vagina, Penis, Brüsten, egal in welcher Sprache(Bereits in LM Studio deaktiviert unter Stopp-Strings was aber das jetzige LLM bartowski/Lumimaid-Magnum-v4-12B-GGUF zum Stop veranlasst). Es soll aus der Ich-Perspektive erzählen, wenn das Wort Ich vorkommt. Des Weiteren soll auch ohne lange, detaillierte Vorgaben das LLM kreativ freizügig explizite Geschichten erzählen. Genaue Nennung des LLM.

Bisher getestet weil von gemini genannt und deshalb nicht gut:

TheDrummer/Cydonia-22B-v1-GGUF

mradermacher/L3-8B-Lunaris-v1-GGUF

QuantFactory/NeuralDaredevil-8B-abliterated-GGUF

bartowski/Meta-Llama-3.1-8B-Instruct-GGUF

bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF

bartowski/L3-8B-Stheno-v3.2-GGUF

mradermacher/L3.1-8B-Celeste-V1.5-GGUF

mradermacher/Mistral-Nemo-Instruct-2407-GGUF

bartowski/cai-13b-Uncensored-GGUF

QWEasd250342/huihui-ai_Qwen3-14B-abliterated-GGUF

SanctumAI/Meta-Llama-3-8B-Instruct-GGUF

QuantFactory/gemma-2-9b-it-GGUF

bartowski/Hermes-3-Llama-3.1-8B-GGUF

bartowski/gemma-2-9b-it-GGUF

mradermacher/L3-8B-Stheno-v3.2-GGUF

QuantFactory/Gemma-2-9B-It-SPPO-Iter3-GGUF

PygmalionAI/mythalion-13b-GGUF

TheBloke/Mistral-7B-Instruct-v0.2-GGUF

Gryphe/MythoMax-L2-13b-GGUF

A book written by AI to explain how LLMs work and to build one from scratch

Benchmarking an LLM here with a NVIDIA RTX 5070 12 GB VRAM here

I had been working on a llama.cpp based expert streaming setup for Qwen3.8-Flash-Next 177B (UD-IQ3_XXS) on Windows. Benchmark is about 11.5 tok/s, up from roughly 7 tok/s on the inherited setup. In normal conversations I’ve seen 14–15 tok/s, and a long coding prompt generated 4,892 tokens at 10.15 tok/s and produced a working single-file Snake game.

Hardware: RTX 5070 12GB
32GB DDR4-2400
Ryzen 5 5600GT PCIe Gen3 Windows

The main gains came from fixing Windows I/O queue-depth issues, using one file handle per worker, and building a page-locked hot-expert tier so the GPU can pull hot expert weights more efficiently.

(In the video its around 16 minutes for 10k tokens and 10.41 tok/s

Output is quality gated against the control model and the published benchmark uses a heat file built from a separate prompt set.

Demos:

https://www.youtube.com/watch?v=cOPumMlyj_4

https://www.youtube.com/watch?v=rc-uTjVpXM8

In the GitHub I have things I've tried that didn't work and benchmark scripts, and methodology. If you guys have suggestions especially for streaming please let me know

Also I don't care about Strata that only works if you got 64 gb of RAM this is specifically for people with less RAM

Hello everyone.

I decided to let my tism run wild while in grad school- im currently studying Chinese Medicine and I wanted to extract the text from two herbal source texts (total of 4 if i count their formula books.

The info is rather dense. (It is vastly in english aside from pinyin names).

What LLM do you believe a person would have the best luck with this? This is for my own personal database that I want to curate to be in the format I like along with being available offline (im utilizing obsidian). The issue im running into is the typical LLM discrepancies.

I tried claude and gpt about 4 months back (paid) and was not a fan. Currently utilizing gemini studio which seems to be ok... ish. Yet i feel like its bottlenecking and falling short in a number of areas.

I am a grad student thus not exactly loaded with a ton of spare income to do this but willing to pay for the right model.

Normally id say id just read the books but holy hannah ain't nobody got time for that.

Couple of things im looking for-

-Text extraction

-dropping info into a preformed template comparing info between the authors.

-removing tone from pinyin name

-linking pinyin names ie other herbs and also formulas.

** NOT pulling info from anywhere else but my pdf uploads.

Anyways any advise on how to do this properly would be greatly appreciated.

I have my template down I believe and can send a pdf of one of the herbs I extracted along with a formula that I extracted.

I really want this to be an offline database as i have intentions of joining up with Acupuncturists Without Borders and want to be able to have it regardless of where I am or situation. (Will also encompasse herbs outside of tcm, tcm acu pounts and treatments, western pathology, etc etc. Basically able to come up with a treatment plan and then double check against my database to ensure I covered everything.

The database will not be for sale hence this will not be utilized for any form of financial gain outside of ensuring treatment is in line with diagnosis- and even then its to ensure i get them off my table and back to work/living life by making entirely certain that I have all of my bases covered and I didnt forget anything (im the human with adhd and minorlly on the spectrum... i forget things lol).

Currently my process is easy ish- yet.. cumbersome and sometimes a bit more on the tedious side with a fairly large margin of error that im not overly fond of.

Anyways, thank you for your time.

-No1S

Ps- I would post a pdf of one of the herbs/formulas i did already but cant upload pdf to a post?