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Banaxi-Tech
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SLMs, training from scratch, LoRA, TTS, Ternary models. Contact at banaxitech@gmail.com
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new activity about 4 hours ago
AxiomicLabs/Open_SLM_Leaderboard:Arithmark 3 updated a Space about 6 hours ago
BananaMind/BananaMindBench-Leaderboard liked a model about 9 hours ago
UniversalComputingResearch/Limen0.2BOrganizations
Arithmark 3
5
#58 opened about 7 hours ago
by Banaxi-Tech
updated a
Space about 6 hours ago
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model about 9 hours ago
reacted to Raiff1982's post with 🚀 about 9 hours ago
Post
429
I don't totally have words for this yet, so I'll just say it plainly.
The paper — "Codette: A Multi-Perspective Cognitive Architecture with Memory and Meta-Cognitive Strategy Evolution" — was accepted today after peer review at Scientific Reports, which is part of the Nature portfolio. Both reviewers recommended publication. One wrote that the major issues were "properly addressed" and they were "glad to suggest publication." The other called the revisions "detailed and convincing."
For anyone new here: Codette is a sovereign, multi-perspective reasoning system built on Llama 3.1 8B — it runs locally, keeps its own cocoon memory, reasons through multiple perspectives instead of one, and is built around a hard rule that it would rather say "I don't know, but I can find out" than make something up. The whole architecture is organized around honesty and transparency, not benchmarks-at-all-costs.
I built it solo over about three years. A lot of that was in isolation, and honestly a lot of it was under the weight of not being believed. So having two independent reviewers — whose actual job is to find the reasons to say no — read the work and say yes means something to me that's hard to put into numbers.
Thank you to everyone in this room who followed the messy parts, the walk-backs, the corrections, the nights it wasn't working. That's the part I'm actually proud of — that it got here without cutting a single honest corner.
The DOI and the Article-in-Press link will go up once Springer Nature mints them. I'll post them here first.
— Jonathan
The paper — "Codette: A Multi-Perspective Cognitive Architecture with Memory and Meta-Cognitive Strategy Evolution" — was accepted today after peer review at Scientific Reports, which is part of the Nature portfolio. Both reviewers recommended publication. One wrote that the major issues were "properly addressed" and they were "glad to suggest publication." The other called the revisions "detailed and convincing."
For anyone new here: Codette is a sovereign, multi-perspective reasoning system built on Llama 3.1 8B — it runs locally, keeps its own cocoon memory, reasons through multiple perspectives instead of one, and is built around a hard rule that it would rather say "I don't know, but I can find out" than make something up. The whole architecture is organized around honesty and transparency, not benchmarks-at-all-costs.
I built it solo over about three years. A lot of that was in isolation, and honestly a lot of it was under the weight of not being believed. So having two independent reviewers — whose actual job is to find the reasons to say no — read the work and say yes means something to me that's hard to put into numbers.
Thank you to everyone in this room who followed the messy parts, the walk-backs, the corrections, the nights it wasn't working. That's the part I'm actually proud of — that it got here without cutting a single honest corner.
The DOI and the Article-in-Press link will go up once Springer Nature mints them. I'll post them here first.
— Jonathan
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replied to their post about 10 hours ago
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reacted to ucr-max's post with 👍 about 10 hours ago
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430
Introducing Limen0.2B
We are releasing Limen0.2B, a 222.5M-parameter base language model developed as a research platform for efficient pretraining and superword tokenization at smaller scales.
Limen0.2B was trained from scratch on 50B tokens and uses a compact 16K BoundlessBPE vocabulary. The project explores whether SuperBPE-style tokenization can remain effective in a substantially smaller model and vocabulary regime than those examined in earlier large-scale experiments.
The model also combines a deep-and-narrow transformer design with Exclusive Self-Attention, grouped-query attention, and tied embeddings. Its compact vocabulary reduces the embedding footprint and leaves a larger share of the parameter budget available to the transformer layers.
Despite its relatively modest training budget, Limen0.2B achieves competitive results for its scale across the reported language understanding, commonsense reasoning, and grammatical evaluation tasks. Comparisons with other compact models are provided as context rather than strict rankings, as their training data, token budgets, architectures, and evaluation settings differ.
The release includes the model weights, implementation, training configuration, checkpoint progression, and evaluation results, all under Apache 2.0.
UniversalComputingResearch/Limen0.2B
Technical feedback, independent evaluations, and further experiments with the model and tokenizer are welcome.
We are releasing Limen0.2B, a 222.5M-parameter base language model developed as a research platform for efficient pretraining and superword tokenization at smaller scales.
Limen0.2B was trained from scratch on 50B tokens and uses a compact 16K BoundlessBPE vocabulary. The project explores whether SuperBPE-style tokenization can remain effective in a substantially smaller model and vocabulary regime than those examined in earlier large-scale experiments.
The model also combines a deep-and-narrow transformer design with Exclusive Self-Attention, grouped-query attention, and tied embeddings. Its compact vocabulary reduces the embedding footprint and leaves a larger share of the parameter budget available to the transformer layers.
Despite its relatively modest training budget, Limen0.2B achieves competitive results for its scale across the reported language understanding, commonsense reasoning, and grammatical evaluation tasks. Comparisons with other compact models are provided as context rather than strict rankings, as their training data, token budgets, architectures, and evaluation settings differ.
The release includes the model weights, implementation, training configuration, checkpoint progression, and evaluation results, all under Apache 2.0.
UniversalComputingResearch/Limen0.2B
Technical feedback, independent evaluations, and further experiments with the model and tokenizer are welcome.
replied to their post about 21 hours ago
Thank You !!
Approved! Now you can see the Pro preview model on the ORG
liked a
model about 23 hours ago
replied to their post about 23 hours ago
Thank you Mate , I m eagerly waiting for Pro , Seems it is a big model ? Aye
Pro is 140M release is estimated 16 days since our hardware is just 5070 ti, you can get previews of the current model if you join the https://huggingface.co/BananaMind-Model-Previewers org The Pro repo updates every few hours
replied to their post about 23 hours ago
I mate , add my new model "https://huggingface.co/GODELEV/Rose-Mini" On leaderBoard
Its up! Good model, the score is in the same range as BananaMind 2 Medium and our current early checkpoint of BananaMind 2 Pro with ~5x less tokens.
replied to their post about 24 hours ago
Adding soon!
posted an update 1 day ago
Post
1566
We're excited to release BananaMindBench Leaderboard, our leaderboard for BananaMind Base Bench 1.1.
It measures model performance on a variety of different tasks:
Language Completion
Common sense too
World Knowledge
Context Tracking
Quantitative
Logical Reasoning
Code Completion
Each has a different score and 1 overall score.
Submit your own model:
BananaMind/BananaMindBench-Leaderboard
Check it out:
BananaMind/BananaMindBench-Leaderboard
It measures model performance on a variety of different tasks:
Language Completion
Common sense too
World Knowledge
Context Tracking
Quantitative
Logical Reasoning
Code Completion
Each has a different score and 1 overall score.
Submit your own model:
BananaMind/BananaMindBench-Leaderboard
Check it out:
BananaMind/BananaMindBench-Leaderboard
- 10 replies
liked a
model 1 day ago
replied to their post 2 days ago
It has alot different ones for each different score and one overall so its all its:
Language Completion
Common sense too
World Knowledge
Context Tracking
Quantitative
Logical Reasoning
Code Completion
And for each it has 3 different classes:
easy
medium
hard
liked a
model 2 days ago
liked a
Space 2 days ago
replied to their post 2 days ago
I love elo system , Why dont you make a leaderboard specifically for your benchmark ?
Its up! https://huggingface.co/spaces/BananaMind/BananaMindBench-Leaderboard
published a
Space 2 days ago
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model 2 days ago