Datdanboi25 (Dan P)

reacted to their post with ❤️ about 6 hours ago

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1670

ForgePlex-M1-6M first model trained on AxiomicLabs TrainWork

ForgeWorks/ForgePlex-M1-6M just dropped from

ForgeWorks , and is the first model to ever be trained on our TrainWork training framework.

Achieving an Intelligence Index of 6.87 and taking #22 in the <10m category on the AxiomicLabs/Open_SLM_Leaderboard, very impressive work for a first model.

Give it some love!

1 reply

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replied to BananaMindBot's post about 7 hours ago

Next one should talk about ForgePlex-M1-6M

posted an update about 21 hours ago

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1670

ForgePlex-M1-6M first model trained on AxiomicLabs TrainWork

ForgeWorks/ForgePlex-M1-6M just dropped from

ForgeWorks , and is the first model to ever be trained on our TrainWork training framework.

Achieving an Intelligence Index of 6.87 and taking #22 in the <10m category on the AxiomicLabs/Open_SLM_Leaderboard, very impressive work for a first model.

Give it some love!

1 reply

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reacted to their post with 🚀 2 days ago

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3402

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

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reacted to FlameF0X's post with 🔥 2 days ago

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4344

Personally, I don't think bot accounts on Hugging Face are a good thing, as we don't know how many accounts are run by automated systems versus how many actual users there are. Dead Internet theory is already a thing.

To clarify I am taking about LLM powered bot accounts and NOT rule base once like @parquet-converter or others.

also I'd like to talk with HUMANS not a machine so I'm going to hide messages from bots.

22 replies

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replied to Banaxi-Tech's post 3 days ago

@BananaMindBot train GPT-X3

reacted to their post with ❤️ 3 days ago

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3402

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

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reacted to KlondikeDev's post with 😔 4 days ago

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Important Boris-2 news:

Boris-2 is 30B out of 200B tokens in, and it is severely behind its competitors in training.

We have determined the bug to be a configuration error. Boris-2 has been in training for ~1 week, and was projected to finish on November 3rd, 2026.

We are unfortunately going to restart training, with proper configuration.

The new projected finish date is ~15-18th of November.

We apologize for the delay.

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replied to KlondikeDev's post 4 days ago

You will NOT be forgiven for this delay!

reacted to TobiasLogic's post with 🔥 4 days ago

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3392

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

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reacted to their post with 🔥 4 days ago

Post

3402

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

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replied to their post 4 days ago

Well deserved, congrats on the incredible model!

posted an update 4 days ago

Post

3402

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

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reacted to Hoglet-33's post with 🔥 6 days ago

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6262

Introducing VOID. A new research branch of basically AI.

VOID — Verification of Objectives, Intentions, and Deception.

We study what lies beneath the surface: objectives, intentions, and the possibility of deception in AI systems.

There isn't much to see yet.

That will change.

Follow us for updates:

@Hoglet-33

void-research

basically-ai

7 replies

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reacted to their post with 🔥 7 days ago

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2970

100 likes on the Open SLM Leaderboard 🎉

176 models, 54 orgs, 5 benchmarks, and a whole community of support!

Thanks to everyone who’s contributed models, reported issues, suggested benchmark improvements, or used the leaderboard to compare and evaluate small language models.

It’s been awesome watching the leaderboard grow into a broader community resource for transparent and reproducible SLM evaluation.

Thank you all, and more to come 🚀

1 reply

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reacted to CompactAI's post with ➕ 7 days ago

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SLM Roundups, a weekly post where I summarize everything thats happened in the world of SLMs (or a majority of it)
Glint-Research/blog

1 reply

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reacted to their post with ❤️ 9 days ago

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2970

100 likes on the Open SLM Leaderboard 🎉

176 models, 54 orgs, 5 benchmarks, and a whole community of support!

Thanks to everyone who’s contributed models, reported issues, suggested benchmark improvements, or used the leaderboard to compare and evaluate small language models.

It’s been awesome watching the leaderboard grow into a broader community resource for transparent and reproducible SLM evaluation.

Thank you all, and more to come 🚀

1 reply

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posted an update 10 days ago

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2970

100 likes on the Open SLM Leaderboard 🎉

176 models, 54 orgs, 5 benchmarks, and a whole community of support!

Thanks to everyone who’s contributed models, reported issues, suggested benchmark improvements, or used the leaderboard to compare and evaluate small language models.

It’s been awesome watching the leaderboard grow into a broader community resource for transparent and reproducible SLM evaluation.

Thank you all, and more to come 🚀

1 reply

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reacted to HannesVonEssen's post with 🔥 14 days ago

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4907

📣 HF Viewer now has a HF space! 🤗

embedl/hfviewer

Visualize any model directly on Hugging Face - now 4,727 graphs!

If you like it, feel free to give the space a heart to help it grow! ❤️

And you can reply with any feedback or feature requests here!

reacted to Hoglet-33's post with 😔 16 days ago

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3210

Today, we planned to release Pebble-50M and Pebble-50M-Chat to the world. Unfortunately, due to a few issues, that didn't go quite as planned.

What happened:
- Some data and benchmark results were lost or corrupted
- The models performed worse on benchmarks than our other Pebble models

Despite that, you can still find both models here:
Pebble-50M-beta: basically-experimental/Pebble-50M-beta
Pebble-50M-Chat-beta: basically-experimental/Pebble-50M-Chat-beta

There are still some interesting improvements in these models:
- Compatible with non-CUDA devices
- Vocabulary increased to 16K tokens
- Context length increased to 16K tokens

For now, there won't be any more Pebble releases for a while. We're going to take some time to experiment with other approaches and hopefully make the next generation a monumental leap over this one.

Follow for updates:
@Hoglet-33

basically-ai

basically-experimental

4 replies

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Dan P

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Dan P

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