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Nymbo PRO

Nymbo

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https://www.nymbo.net/

AI & ML interests

I like unrestricted, free, utilitarian stuff. I tend to archive good spaces because I don't trust y'all to keep them good :) Most spaces with runtime errors just need a restart. Paused spaces should work but require a GPU, duplicate to use.

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reacted to MiniMax-AI's post with 🔥 about 6 hours ago

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. 🚀 https://huggingface.co/MiniMaxAI

updated a collection about 21 hours ago

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liked a model about 21 hours ago

InternScience/Agents-A1

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reacted to MiniMax-AI's post with 🔥 about 6 hours ago

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2036

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

2 replies

reacted to AxionLab-official's post with ❤️🧠🚀🔥 18 days ago

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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

1 reply

reacted to owensong's post with 🔥 19 days ago

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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

reacted to evalstate's post with 🤗 about 1 month ago

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Hugging Face MCP Server v0.3.17
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

SEP-2640 "Skills Over MCP" support added (early access)

3 replies

reacted to qgallouedec's post with 🔥 2 months ago

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Shipped hf-sandbox! 🥡

🧪 Running an eval that executes model-generated C on a few thousand prompts? You probably don't want any of that on your laptop.
Just shipped hf-sandbox, a Modal-style sandbox API on top of Hugging Face Jobs. Spin up an isolated, ephemeral container, run untrusted code, get the result back. No Docker on your laptop, no infra to manage.

Just pip install hf-sandbox.

Early days (v0.1); feedback and issues very welcome:
👉 https://github.com/huggingface/hf-sandbox

1 reply

reacted to victor's post with 🤗🔥 3 months ago

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Want to share my enthusiasm for zai-org/GLM-5.1 here too 🔥

I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:

- One-shot car physics with real drift mechanics (this is hard)

- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen

- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters

- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!

- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed

You are going to hear about this model a lot in the next months - open source let's go - and thanks z-ai🚀🚀

5 replies

replied to victor's post 3 months ago

gib prompt

reacted to danielhanchen's post with 👀 3 months ago

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A new way to use Unsloth.

Coming soon...

posted an update 4 months ago

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We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.

Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.

3 replies

reacted to Tonic's post with 🔥 5 months ago

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🙋🏻‍♂️hello my lovelies ,

it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.

repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw

you can also run it locally and see for yourself :

docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest

just a few quite minor details i'll take care of but i wanted to share here first

2 replies

reacted to Javedalam's post with 🔥 5 months ago

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KittenTTS Nano — Tiny, Expressive, Practical

KittenTTS Nano is a lightweight, CPU-only text-to-speech model designed to prove that natural, expressive voices don’t require massive cloud stacks or GPUs. At roughly ~15M parameters, it runs fast on modest hardware, supports multiple expressive voices, and exposes simple controls for pacing and tone. This makes it ideal for edge devices, demos, and anyone who wants full control over TTS without latency, lock-in, or infrastructure overhead.

Try it here

Javedalam/KittenTTS

The model page

KittenML/kitten-tts-nano-0.2

2 replies

reacted to IlyasMoutawwakil's post with 🔥 5 months ago

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3313

Transformers v5 just landed! 🚀
It significantly unifies and reduces modeling code across architectures, while opening the door to a whole new class of performance optimizations.

My favorite new feature? 🤔
The new dynamic weight loader + converter. Here’s why 👇

Over the last few months, the core Transformers maintainers built an incredibly fast weight loader, capable of converting tensors on the fly while loading them in parallel threads. This means we’re no longer constrained by how parameters are laid out inside the safetensors weight files.

In practice, this unlocks two big things:
- Much more modular modeling code. You can now clearly see how architectures build on top of each other (DeepSeek v2 → v3, Qwen v2 → v3 → MoE, etc.). This makes shared bottlenecks obvious and lets us optimize the right building blocks once, for all model families.
- Performance optimizations beyond what torch.compile can do alone. torch.compile operates on the computation graph, but it can’t change parameter layouts. With the new loader, we can restructure weights at load time: fusing MoE expert projections, merging attention QKV projections, and enabling more compute-dense kernels that simply weren’t possible before.

Personally, I'm honored to have contributed in this direction, including the work on optimizing MoE implementations and making modeling code more torch-exportable, so these optimizations can be ported cleanly across runtimes.

Overall, Transformers v5 is a strong signal of where the community and industry are converging: Modularity and Performance, without sacrificing Flexibility.

Transformers v5 makes its signature from_pretrained an entrypoint where you can mix and match:
- Parallelism
- Quantization
- Custom kernels
- Flash/Paged attention
- Continuous batching
- ...

Kudos to everyone involved! I highly recommend the:
Release notes: https://github.com/huggingface/transformers/releases/tag/v5.0.0
Blog post: https://huggingface.co/blog/transformers-v5

3 replies

reacted to raincandy-u's post with 🔥 6 months ago

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🤗 Just released Rain-100M, an experimental ~97M-parameter Qwen3-style language model trained from random initialization.

Repo: raincandy-u/Rain-100M

Data: HuggingFaceFW/fineweb-edu, ~3B tokens, English only

Tokenizer: custom 16k BPE, context length 4096

Architecture: 12 Transformer layers, hidden size 768, 12 heads, MLP 2048, SiLU, bf16

Rain-100M is a raw base model (not instruction-tuned or safety-aligned), aimed at small-scale research, debugging training pipelines, and CPU/edge experiments. If you run evaluations, finetunes, or visualizations with it, I would be very interested in your results!

3 replies

posted an update 6 months ago

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Genuine recommendation: You should really use this AutoHotKey macro. Save the file as macros.ahk and run it. Before sending a prompt to your coding agent, press Ctrl + Alt + 1 and paste your prompt to any regular chatbot. Then send the output to the agent. This is the actual, boring, real way to "10x your prompting". Use the other number keys to avoid repeating yourself over and over again. I use this macro prolly 100-200 times per day. AutoHotKey isn't as new or hype as a lot of other workflows, but there's a reason it's still widely used after 17 years. Don't overcomplicate it.

; Requires AutoHotkey v1.1+

; All macros are `Ctrl + Alt + <variable>`

^!1::
    Send, Please help me more clearly articulate what I mean with this message (write the message in a code block):
return

^!2::
    Send, Please make the following changes:
return

^!3::
    Send, It seems you got cut off by the maximum response limit. Please continue by picking up where you left off.
return

In my experience the past few months, Ctrl + Alt + 1 works best with Instruct models (non-thinking). Reasoning causes some models to ramble and miss the point. I've just been using GPT-5.x for this.

reacted to prithivMLmods's post with 🤗 6 months ago

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Update: TRELLIS.2 (Text to 3D, Image to 3D) Gradio with Rerun Embedded demo with improved visualization of the 3D model previewer is now available on Hugging Face. Generate assets and view them in the 3D viewer, powered and streamlined with Microsoft’s TRELLIS.2 and Tongyi-MAI’s Z-Image-Turbo models.

🤗 TRELLIS.2 (Demo): prithivMLmods/TRELLIS.2-Text-to-3D
🕹️ GitHub: https://github.com/PRITHIVSAKTHIUR/TRELLIS.2-Text-to-3D-RERUN
🕹️ Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations

To know more about it, visit the app page or the respective model page!

reacted to ronantakizawa's post with 👍 7 months ago

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Thank you @clem (Co-Founder & CEO of Hugging Face) for sharing my dataset on X / Twitter!

ronantakizawa/github-top-developers

#github #dataset

4 replies

Nymbo PRO

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