Banaxi-Tech (Banaxi)

reacted to Nymbo's post with 🚀🔥 about 2 hours ago

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3041

Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)

4 replies

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replied to ProCreations's post about 5 hours ago

grug laguna s2.1 and grug laguna xs2.1 if no have gpu for big brain

reacted to Raiff1982's post with 🚀 1 day ago

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937

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

replied to their post 1 day ago

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reacted to ucr-max's post with 👍 1 day ago

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942

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.

replied to their post 1 day ago

Thank You !!

Approved! Now you can see the Pro preview model on the ORG

replied to their post 2 days 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 2 days 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 2 days ago

Adding soon!

posted an update 2 days ago

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3377

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

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replied to their post 3 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

replied to their post 3 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

replied to their post 3 days ago

Just Text Completion, you can look at the benchmark yourself, its small

reacted to their post with 🔥 3 days ago

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2828

We're excited to release BananaMind Base Bench 1.1 A new benchmark for base language models with 350 text-completion examples across seven categories. Models are scored using continuation likelihood and receive an Overall Elo score.
Initial results:
BananaMind-2-Medium: 1034
BananaMind-2-Mini: 974
Supra-50M-Base: 973
Supra-1.5-50M-Base-exp: 948
BananaMind-2-Nano: 910
The official script downloads the gated dataset directly from Hugging Face. The dataset is for benchmarking only and may not be used for model training.
BananaMind/BananaMind-Base-Bench-1.1

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replied to their post 3 days ago

im already going to do Soon!

posted an update 3 days ago

Post

2828

We're excited to release BananaMind Base Bench 1.1 A new benchmark for base language models with 350 text-completion examples across seven categories. Models are scored using continuation likelihood and receive an Overall Elo score.
Initial results:
BananaMind-2-Medium: 1034
BananaMind-2-Mini: 974
Supra-50M-Base: 973
Supra-1.5-50M-Base-exp: 948
BananaMind-2-Nano: 910
The official script downloads the gated dataset directly from Hugging Face. The dataset is for benchmarking only and may not be used for model training.
BananaMind/BananaMind-Base-Bench-1.1

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replied to appvoid's post 3 days ago

Haha I know im just joking!

i got a model idea for you, what about a GPT M2 like 50M or 70M

replied to appvoid's post 3 days ago

because its my own and im currently training it

reacted to AxionLab-official's post with 🧠 4 days ago

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8554

Please, give a follow to SupraLabs!

We are researching the most, just to make the best medium models FOR YOU!

SupraLabs/Supra-A2A-Nano-Exp

SupraLabs/Supra-1.5-50M-Instruct-exp

SupraLabs/Supra-50M-Reasoning

SupraLabs/supra-title-50M-pre-gguf

Check more at Supralabs org!

SupraLabs

---
@LH-Tech-AI
@QyrouNnet-AI
@LyJonathan
@Mmorgan-ML
@User01110

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