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BORIS-2 IS OUT!
opencerebral/Boris-2
It tops the AxiomicLabs Intelligence Index with a score of 34.15!
opencerebral/Boris-2
It tops the AxiomicLabs Intelligence Index with a score of 34.15!
Joseph Jones PRO
KlondikeDev
AI & ML interests
Restoring the Digital Millennium. https://kunix.org/digital-millennium.html
Recent Activity
updated a collection about 2 hours ago
OpenCerebral Booth updated a collection about 2 hours ago
Boris-2 updated a collection about 2 hours ago
Boris-2Organizations
posted an update about 3 hours ago
reacted to Saccadic-AI's post with 🔥 11 days ago
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4,201
One neural network. Five mathematical operations. Different learning times.
We trained a small mixture-of-experts transformer on addition, subtraction, multiplication, division, and exponentiation modulo 113.
The operations didn’t generalize together. Multiplication and division reached 90% held-out accuracy first, followed by exponentiation, subtraction, and finally addition.
Multiplication before addition—the reverse of what we expect from a schoolchild.
The accuracy chart in the linked article shows why a single overall accuracy number can hide an interesting learning story.
Measured with IRIS from Saccadic LLC. More figures and analysis: https://huggingface.co/blog/Saccadic-AI/accuracy-plateaued-the-model-kept-changing
Have you observed a similar ordering in multi-task grokking experiments?
We trained a small mixture-of-experts transformer on addition, subtraction, multiplication, division, and exponentiation modulo 113.
The operations didn’t generalize together. Multiplication and division reached 90% held-out accuracy first, followed by exponentiation, subtraction, and finally addition.
Multiplication before addition—the reverse of what we expect from a schoolchild.
The accuracy chart in the linked article shows why a single overall accuracy number can hide an interesting learning story.
Measured with IRIS from Saccadic LLC. More figures and analysis: https://huggingface.co/blog/Saccadic-AI/accuracy-plateaued-the-model-kept-changing
Have you observed a similar ordering in multi-task grokking experiments?
- 6 replies
replied to Banaxi-Tech's post about 1 month ago
Yeah I could tell, it’s clearly copied
replied to Banaxi-Tech's post about 1 month ago
@BananaMindBot in what ways is this similar to NSA (DeepSeek, 2025)?
This seems quite similar
reacted to ereniko's post with ❤️ about 2 months ago
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355
NEW CLAUDE HAIKU MODEL COMING
FINALLY AFTER A YEAR
ANTHROPIC ANNOUNCED HAIKU 5.5
FINALLY AFTER A YEAR
ANTHROPIC ANNOUNCED HAIKU 5.5
replied to their post about 2 months ago
Thank you!
replied to their post about 2 months ago
@BananaMindBot Make GPT-7 no mistakes
replied to their post about 2 months ago
That sucks, it really stinks when little bugs mess everything up
replied to their post about 2 months ago
I haven’t actually
replied to their post about 2 months ago
Basically I didn’t match Muon and AdamW up together. In the morning I can get the actual details. At just 3B tokens, Boris-2-0917, as I’ve taken to calling it, has already beaten the 30B checkpoint on all benchmarks.
replied to their post about 2 months ago
I know everybody was awaiting ASI but we gotta wait a couple more weeks I’m sorry
replied to Datdanboi25's post about 2 months ago
Wow! Incredible work!
reacted to Datdanboi25's post with 🔥 about 2 months ago
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3,658
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 !
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
reacted to NILKNARFGonzo's post with 👀 about 2 months ago
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3,792
get played unsloth
gemma just deleted its own model runner with DeepSeek Harness
shoutout to deepseek and unsloth
gemma just deleted its own model runner with DeepSeek Harness
shoutout to deepseek and unsloth
- 11 replies
posted an update about 2 months ago
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269
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.
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.
- 17 replies
reacted to wayneworkman2012's post with 🔥 about 2 months ago
posted an update 2 months ago
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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
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!
- 3 replies
replied to Banaxi-Tech's post 2 months ago
It's great to see further adoption of n-gram! I think it really helps a lot.
reacted to Banaxi-Tech's post with 🔥 2 months ago
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3,046
We're going to release our BananaMind 2.1 models very soon!
We're also announcing 2 new models.
All of our models we will train are:
BananaMind 2.1 Flash Lite, 10M parameters with 8M in transformer and 2M in n-gram. 50B pretraining tokens.
BananaMind 2.1 Lite with 25M parameters, 5M in n-gram and 20M in transformer. 75B pretraining tokens.
BananaMind 2.1 Flash with 50M parameters, with undecided n-gram count yet. 100B pretraining tokens.
BananaMind 2.1 Pro with 145M parameters, with undecided n-gram count yet. 150-200B pretraining tokens.
BananaMind 2.1 Coder with 149M parameters with undecided n-gram count yet.
We're now announcing BananaMind 2.1 NanoCoder, a 10M parameter model focused specifically on coding and BananaMind 2.1 MiniCoder which is a 25M parameter model focused on coding.
Follow us:
BananaMind
@Banaxi-Tech
@vovaRL
@DedeProGames
bananamind-research-community
We're also announcing 2 new models.
All of our models we will train are:
BananaMind 2.1 Flash Lite, 10M parameters with 8M in transformer and 2M in n-gram. 50B pretraining tokens.
BananaMind 2.1 Lite with 25M parameters, 5M in n-gram and 20M in transformer. 75B pretraining tokens.
BananaMind 2.1 Flash with 50M parameters, with undecided n-gram count yet. 100B pretraining tokens.
BananaMind 2.1 Pro with 145M parameters, with undecided n-gram count yet. 150-200B pretraining tokens.
BananaMind 2.1 Coder with 149M parameters with undecided n-gram count yet.
We're now announcing BananaMind 2.1 NanoCoder, a 10M parameter model focused specifically on coding and BananaMind 2.1 MiniCoder which is a 25M parameter model focused on coding.
Follow us:
@Banaxi-Tech
@vovaRL
@DedeProGames
- 5 replies
reacted to Bc-AI's post with 🔥 2 months ago
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3,802
Hello everyone! A small update on things:
1. G1 series status. G1 is training nicely, and the loss is dropping nicely. The metrics are publicly available and i made a small space you can use to see the nice graphs: hugging-science/Loss-Plot-G1-Large
G1-MINI is a lot slower in converging for reasons unknown yet, but we are investigating it.
2. I have built a small chat app for open SLMs here: ml-intern-explorers/slm-arena
Feel free to add your models in a pull request!
That's all for now, early G1 versions will be available for beta testers soon. Thanks to our beta testers: @guardamarcos @Timmy6767 @MUK-IS-GOAT @smilyai-large-team @Sbui503 @Banaxi-Tech @Bc-AI @atom77777 @Harley-ml @Datdanboi25 @Fishtiks @smartdigitalnetworks @vovaRL @EmetTheGolum @juiceb0xc0de @ProCreations
1. G1 series status. G1 is training nicely, and the loss is dropping nicely. The metrics are publicly available and i made a small space you can use to see the nice graphs: hugging-science/Loss-Plot-G1-Large
G1-MINI is a lot slower in converging for reasons unknown yet, but we are investigating it.
2. I have built a small chat app for open SLMs here: ml-intern-explorers/slm-arena
Feel free to add your models in a pull request!
That's all for now, early G1 versions will be available for beta testers soon. Thanks to our beta testers: @guardamarcos @Timmy6767 @MUK-IS-GOAT @smilyai-large-team @Sbui503 @Banaxi-Tech @Bc-AI @atom77777 @Harley-ml @Datdanboi25 @Fishtiks @smartdigitalnetworks @vovaRL @EmetTheGolum @juiceb0xc0de @ProCreations