Nymbo (Nymbo)

reacted to Enderchef's post with 👍🔥 1 day ago

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1708

GPT-X2.5-135M is finally released! 🚀
The new flagship from Axiomic Labs takes 3rd on the open SLM leaderboard trailing only the SmolLMs, check it out and follow us:
AxiomicLabs/GPT-X2.5-135M

reacted to Enderchef's post with 🔥 2 days ago

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3372

🚀 Supra2 100M is out, and multiple other SLM orgs are gaining power!
Following takes a press. Please follow:

fromziro

SupraLabs

AxiomicLabs

8 replies

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reacted to Banaxi-Tech's post with 🔥 2 days ago

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2292

We're excited to release BananaMind 2 Pro Preview, our best model yet.
Trained on ~52B tokens it performs extremely good for its token and size class.
We trained it on a single 5070 Ti in about 11 days.
Check it out at BananaMind/BananaMind-2-Pro-Preview.
Sadly we need to delay BananaMind 2 Micro until the launch of the final BananaMind 2 Pro.
We will release the final checkpoint with 100B tokens in ~11 days.
Go and fine-tune it!
We've also released BananaMind 2 Pro Preview Chat which is the instruct version of it!
BananaMind/BananaMind-2-Pro-Preview-Chat

Follow us to know when the final releases and support us at

BananaMind
@Banaxi-Tech

5 replies

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reacted to AxionLab-official's post with 🚀🔥 3 days ago

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3413

Tommorow we are releasing Supra2-Pro(100M params), our flagship, get ready!

(follow SupraLabs to get tuned in!)

SupraLabs

9 replies

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reacted to LH-Tech-AI's post with 🤗🔥 5 days ago

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2366

Announcing The Supra2 Family And Supra2-100M

Today, we are announcing a brand-new series of SupraLabs models: Supra2
This series will feature various models, including such as:
- 🐜 Supra2-Nano (0.4M) → The smallest Supra2 model.
- 🤏 Supra2-Small (1.4M) → The tiny model that runs everywhere.
- 💪 Supra2-Medium (25M) → Our medium class model in the Supra2 family. The powerful midsizer.
- 🔥 Supra2-Pro (100M): base, instruct, reasoning, code, math and more! → The most capable model yet! A real allrounder for all your everyday tasks.
- 🎨 Supra2-IMG → our generative text-to-image model
...and many more...

Current progress:
- Nano (0.4M) and Small (1.4M): in training; almost done. Baseline set.
- Medium (25M): coming soon...
- Pro (100M): in training; finishes in 66 hours - Monday, 3rd August 2026, 12:00AM
- IMG: coming soon...

You can support us with a like and follow if you want!
Don't miss our next release! Stay tuned...

30 replies

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reacted to AxionLab-official's post with 🔥 5 days ago

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2219

Thanks for 300 followers in SupraLabs!!! That means alot to SupraLabs!

@lesageethan
@LH-Tech-AI

3 replies

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reacted to their post with 👍 7 days ago

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5530

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)

6 replies

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reacted to owensong's post with 🔥 10 days ago

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6872

I just released Inflect v2, pushing complete TTS below 4M parameters.

Inflect-Nano-v2 fits every learned inference component, including waveform generation, into 3.96M parameters (15.97 MB FP32), making it one of the smallest publicly available complete TTS models. Inflect-Micro-v2 stays under 10M at 9.36M parameters (37.53 MB), using the extra capacity for clearer, more natural speech.

This was not about building a toy that technically produces audio. I wanted to see how much useful TTS could actually fit at this scale.

Nano stayed surprisingly close to Micro across the reported release metrics, but if you want to hear Inflect at its best, start with Micro:
owensong/Inflect-Micro-v2

Text goes in and 24 kHz speech comes out without a second learned model. Both models have PyTorch and ONNX releases: PyTorch runs on CPU or CUDA, while the browser demo runs ONNX locally through WebGPU with a WASM fallback. The Inflect code and weights are released under Apache 2.0.

Try Nano and Micro side by side:
owensong/Inflect-v2

Some people may remember the much rougher Inflect-Nano-v1 experiment. V2 was rebuilt and retrained to address its biggest weaknesses: metallic audio, unstable timing, weak prosody, and difficulty with unseen text. Nano-v2 is smaller while sounding substantially cleaner and more stable.

In an anonymous community blind test across nine voices, Micro and Nano placed second and third by normalized preference rate. Full results, samples, limitations, and inference instructions are on the model pages.

Inflect-Nano-v2:
owensong/Inflect-Nano-v2

I built Inflect independently with a limited training budget. If you test it, give it something difficult: unusual names, numbers, abbreviations, symbols, or awkward punctuation. Send me the exact text when something breaks. If you build something with it, I would genuinely love to see it.

1 reply

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reacted to their post with 🚀🔥 12 days ago

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5530

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)

6 replies

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posted an update 12 days ago

Post

5530

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)

6 replies

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reacted to MiniMax-AI's post with 🔥 27 days ago

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4851

Huge news from MiniMax: we’ve secured a $2B funding round, paired with a formal long-term commitment from our CEO IO to allocate 1% of total company equity from his personal holdings to support the global open-source AI community over the next four years.

This capital backs our continuous open model releases, community tooling and transparent frontier AI research. We’re just getting started on our open-source roadmap toward accessible AGI.

If you build with open foundation models and want to push frontier AI together, come join us.
Intelligence with Everyone. 🚀

MiniMaxAI

3 replies

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reacted to AxionLab-official's post with ❤️🧠🚀🔥 about 2 months ago

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8573

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

https://huggingface.co/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

2 replies

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reacted to owensong's post with 🔥 about 2 months ago

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7486

I just released Inflect-Nano-v1, an ultra-small 4.63 parameter text-to-speech model.

The main idea is simple: instead of only making the acoustic model tiny and relying on a larger external vocoder, Inflect-Nano-v1 keeps the complete text-to-waveform stack under 5M parameters.

Quick facts:
- 4.63M total inference parameters
- 3.46M acoustic model
- 1.17M vocoder
- 24 kHz audio
- English-only
- Single male voice
- Runs locally with a simple PyTorch inference script

Why I made it:
Most modern TTS models are much larger, and even many “small TTS” projects depend on a separate vocoder. I wanted to see how far a complete tiny TTS stack could be pushed while still producing usable speech.

It is not SOTA, and I am not trying to claim it competes with large TTS systems. The interesting part is the size-to-functionality ratio.

What works:
It can generate arbitrary English speech locally, and the model is small enough to be interesting for:

- local voice assistants
- embedded/edge experiments
- browser or WASM-style TTS exploration
- efficient inference research
- tiny-model baselines

Limitations:
The quality is still limited. It can sound robotic, stumble on difficult unseen text, and the vocoder is still a clear bottleneck. Long or unusual prompts are less reliable.

So I would frame this as a research/demo release, not a production TTS engine.

I’d love feedback from people interested in:
- tiny speech models
- vocoders
- local TTS
- efficient inference
- embedded speech synthesis
- improving small-model generalization

If people find it useful, I’m interested in putting more training budget into a stronger v2.

Model page:
owensong/Inflect-Nano-v1

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