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appvoid

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https://ko-fi.com/appvoid

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singularity through byte-level tokens and small language models - apps and models on dotlabs

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new activity about 4 hours ago

AxiomicLabs/Open_SLM_Leaderboard:void.1 update

liked a model about 7 hours ago

MinimaLabs/KLM-1.5

updated a model about 7 hours ago

dotlabs/void.1

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reacted to Datdanboi25's post with 🚀 1 day ago

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3,590

THE SLM FRONTIER ADVANCES!

bench-labs/cagliostro-v3 just hit an Intelligence Index of 26.13 on the AxiomicLabs/Open_SLM_Leaderboard a 146M-param model trained completely from scratch on a single consumer GPU. That's 2nd place overall, and as far as I can tell, the most capable SLM trained on consumer hardware to date. Beating SmolLM-135m on 1/8th of the data is just silly levels of efficiency.

Big congrats to the @BenchLabs team and specifically @TobiasLogic !

6 replies

replied to their post 9 days ago

@appvoid do you actually have a B200?

uh ofc not

replied to Banaxi-Tech's post 9 days ago

more models is fine, but focus on usability and you should be fine

posted an update 14 days ago

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195

Byte-level is surprisingly powerful, don't believe me? have a look at this model

dotlabs/void.1

7 replies

posted an update 19 days ago

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218

byte-level, deep networks, flow-matching, matryoshka...

posted an update 22 days ago

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180

Much of the misunderstanding on loops and byte-level models comes from fear on inventing the wheel, not reinventing it

posted an update 23 days ago

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150

How much should OpenAI offer for a name like Dot Labs?

1 reply

posted an update 27 days ago

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174

Reality has not limits if you know how to shape it, one step at a time.

1 reply

reacted to TobiasLogic's post with 🚀 27 days ago

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3,596

We’ve been cooking something new at Bench Labs.

Introducing Cagliostro-v3, our new 146M parameter language model trained completely from scratch.

The run isn’t even finished yet.

At the current checkpoint:

• 146M parameters
• 72.7B / 75B tokens trained
• 26.27 Open SLM Index
• 43.80 ArithMark-3
• Trained on a single RTX 5090
• ~90K to 103K tokens/sec during training
• ~9 days for the full run
• Apache 2.0

For some context, SmolLM2-135M scores 27.13 on the same Index after being trained on roughly 2 trillion tokens.

Cagliostro-v3 is currently at 26.27 with only ~72.7B.

That’s around 27x fewer training tokens.

The model also currently Hold the number 3rd spot for ArithMark-3, scoring 43.80

This wasn’t achieved by just throwing more tokens at the model. A huge part of v3 has been figuring out architecture, data mixture, and training dynamics at this scale.

The model uses a custom 30-layer decoder architecture with grouped-query attention and cross-head subspace attenuation, SwiGLU, RMSNorm, RoPE, tied embeddings, and a warmup-stable-decay training schedule.

During cooldown we also substantially shifted the data mixture toward higher-quality synthetic textbook and mathematics data, with the mathematics share increasing from 10% to 28%.

And everything is open.

The repository contains the training history with checkpoints pushed roughly every 30 minutes, so you can inspect how the model evolved throughout training rather than only seeing the final weights.

This is still a pre-final checkpoint. We have roughly 2.3B tokens left and the learning-rate cooldown is still running.

So 26.27 isn’t the final number.

Really excited to see where the last part of the run lands.

Cagliostro-v3:
bench-labs/cagliostro-v3
Built by Bench Labs.

Open SLM Leaderboard:
AxiomicLabs/Open_SLM_Leaderboard

8 replies

posted an update 28 days ago

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How old were you when you discovered good AI agents favor the Strategy Design Pattern over any other?

1 reply

posted an update about 1 month ago

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134

LLMS for edge devices?
What if we go the reliable route instead of the speed route?
What if we make it run on sbcs with few megabytes available?

That's the idea for the next model.

Keep in tune.

replied to AtAndDev's post about 1 month ago

congrats!

posted an update about 1 month ago

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144

We trained a 10.9M byte-level recurrent Transformer on L3 and L6. (Loop 3 and Loop 6)

Yet L4/L5 improved too, L8 held up, and the L3→L6 gain grew during training.

Same weights. More compute. Better predictions.

This is a new architecture for effective compute after several steps beyond original training!

We mixed and matched components like time and mhc into an ouro-like byte-level language model and the result is BET, a byte-level step-elastic transformer that can run computation steps without significant degradation.

One of the coolest parts of this training was discovering how Gradient Descent decided to use the first layer as what we would consider a scratchpad! Totally destroyed for the decoder but somehow makes total sense for the next layer!

I believe looped-transformers are the future of edge computing and this is a first step towards it.

Blogpost: https://medium.com/@appvoidofficial/byte-level-elasticity-182fe2ed1d2f

appvoid/bet-10m

reacted to KlondikeDev's post with ❤️ about 1 month ago

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2,167

The SLM Consortium has begun work on a safety dataset for Small Language Models, with the creation of the dataset being headed by @wayneworkman2012

The dataset will focus on refusals and redirects surrounding dangerous or extreme sexual content, designed to be shaped sized appropriately for SLMs, without significantly lowering benchmark performance.

More info will be out soon!

slmconsortium

3 replies

replied to their post about 2 months ago

Well I don’t care if it’s AGI or not I just find it super helpful in real practice

It would be for me if my credits wouldn't drain even light mode 🥲

replied to their post about 2 months ago

it is not AGI

For me it is not, but it's pretty close for me though

replied to their post about 2 months ago

Ill wait for the glm distilled xD

I think we are years away though

replied to their post about 2 months ago

Wild if true

posted an update about 2 months ago

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3,969

We got gpt6 before gta6

15 replies

replied to their post about 2 months ago

Feeling the same :)

appvoid

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