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Nymbo

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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posted an update about 1 hour ago

I rewrote a bunch of my MCP tools as three agent skills: `web-search`, `speech-kokoro`, and `hf-inference`. https://huggingface.co/datasets/Nymbo/Skills # web-search `web-search` is the real star here. It's basically my `Web_Search` + `Web_Fetch` MCP tools turned into a portable skill and requires no authentication. For normal web search it can use DuckDuckGo, Bing, Brave, Yahoo, or Wikipedia, and in `auto` mode it walks through multiple backends with fallback + deduping. News search does the same thing across DuckDuckGo, Bing, and Yahoo, with a smart fallback to general web search if news results come up empty. It also supports images, videos, and book search, max result limits, day/week/month/year filters, backend selection, page/offset pagination, and output as plain text, Markdown, or JSON. Search finds the right page, then fetch can pull that URL back as clean Markdown, raw HTML, or extracted links, with `--max-chars` and `--offset` so long pages can be read in chunks instead of dumping the whole internet into context. # speech-kokoro `speech-kokoro` is local text-to-speech using Kokoro-82M. It can generate from direct text or a text file, list available Kokoro voices, use a different voice id, set voice/speed/output path, and handle longer text by segmenting and stitching the audio back together. It uses CUDA if PyTorch sees CUDA, otherwise it falls back to CPU. No OpenAI Audio API, no hosted TTS service, just local speech generation. # hf-inference `hf-inference` wraps Hugging Face serverless inference for image and video generation. For images, the default model is `Tongyi-MAI/Z-Image-Turbo`. For videos, the default is `Wan-AI/Wan2.2-T2V-A14B`. You can swap in any compatible model id, set negative prompts, width/height, steps, CFG scale, seeds, provider order, and pass extra JSON for weird model/provider-specific options. It checks `HF_READ_TOKEN` / `HF_TOKEN` from the environment. --- Download them here: https://github.com/Nymbo/Public-Skills

updated a dataset about 1 hour ago

Nymbo/Skills

published a dataset about 1 hour ago

Nymbo/Skills

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posted an update about 1 hour ago

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I rewrote a bunch of my MCP tools as three agent skills: web-search, speech-kokoro, and hf-inference. Nymbo/Skills

# web-search

web-search is the real star here. It's basically my Web_Search + Web_Fetch MCP tools turned into a portable skill and requires no authentication.

For normal web search it can use DuckDuckGo, Bing, Brave, Yahoo, or Wikipedia, and in auto mode it walks through multiple backends with fallback + deduping. News search does the same thing across DuckDuckGo, Bing, and Yahoo, with a smart fallback to general web search if news results come up empty.

It also supports images, videos, and book search, max result limits, day/week/month/year filters, backend selection, page/offset pagination, and output as plain text, Markdown, or JSON.

Search finds the right page, then fetch can pull that URL back as clean Markdown, raw HTML, or extracted links, with --max-chars and --offset so long pages can be read in chunks instead of dumping the whole internet into context.

# speech-kokoro

speech-kokoro is local text-to-speech using Kokoro-82M.

It can generate from direct text or a text file, list available Kokoro voices, use a different voice id, set voice/speed/output path, and handle longer text by segmenting and stitching the audio back together. It uses CUDA if PyTorch sees CUDA, otherwise it falls back to CPU. No OpenAI Audio API, no hosted TTS service, just local speech generation.

# hf-inference

hf-inference wraps Hugging Face serverless inference for image and video generation.

For images, the default model is Tongyi-MAI/Z-Image-Turbo. For videos, the default is Wan-AI/Wan2.2-T2V-A14B. You can swap in any compatible model id, set negative prompts, width/height, steps, CFG scale, seeds, provider order, and pass extra JSON for weird model/provider-specific options. It checks HF_READ_TOKEN / HF_TOKEN from the environment.

---

Download them here:
https://github.com/Nymbo/Public-Skills

reacted to evalstate's post with 🤗 2 days ago

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

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

2 replies

reacted to qgallouedec's post with 🔥 25 days 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 🤗🔥 about 2 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 about 2 months ago

gib prompt

reacted to danielhanchen's post with 👀 2 months ago

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A new way to use Unsloth.

Coming soon...

posted an update 3 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.

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reacted to Tonic's post with 🔥 4 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

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reacted to Javedalam's post with 🔥 4 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 🔥 4 months ago

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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 🔥 4 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!

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posted an update 5 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 🤗 5 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 👍 5 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

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posted an update 6 months ago

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🚨 New tool for the Nymbo/Tools MCP server: The new Agent_Skills tool provides full support for Agent Skills (Claude Skills but open-source).

How it works: The tool exposes the standard discover/info/resources/validate actions. Skills live in /Skills under the same File_System root, and any bundled scripts run through Shell_Command, no new infrastructure required.

Agent_Skills(action="discover")  # List all available skills
Agent_Skills(action="info", skill_name="music-downloader")  # Full SKILL.md
Agent_Skills(action="resources", skill_name="music-downloader")  # Scripts, refs, assets

I've included a music-downloader skill as a working demo, it wraps yt-dlp for YouTube/SoundCloud audio extraction.

Caveat: On HF Spaces, Shell_Command works for most tasks, but some operations (like YouTube downloads) are restricted due to the container environment. For full functionality, run the server locally on your machine.

Try it out ~ https://www.nymbo.net/nymbot

posted an update 6 months ago

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🚀 I've just shipped a major update to the Nymbo/Tools MCP server: the Agent_Terminal, a single "master tool" that cuts token usage by over 90%!

Anthropic found 98.7% context savings using code execution with MCP, Cloudflare published similar findings. This is my open-source implementation of the same idea.

# The Problem

Traditional MCP exposes every tool definition directly to the model. With 12 tools, that's thousands of tokens consumed *before the conversation even starts*. Each tool call also passes intermediate results through the context window — a 10,000-row spreadsheet? That's all going into context just to sum a column.

# The Solution: One Tool to Rule Them All

Agent_Terminal wraps all 12 tools (Web_Search, Web_Fetch, File_System, Generate_Image, Generate_Speech, Generate_Video, Deep_Research, Memory_Manager, Obsidian_Vault, Shell_Command, Code_Interpreter) into a single Python code execution gateway.

Instead of the model making individual tool calls, it writes Python code that orchestrates the tools directly:

# Search for Bitcoin price
result = Web_Search("current price of bitcoin", max_results=3)
print(result)

Don't know what tools are available? The agent can discover them at runtime:

print(search_tools('image'))  # Find tools by keyword
print(usage('Generate_Image'))  # Get full docs for a specific tool

The individual direct tool calls are all still there, but they can be disabled if using the Agent_Terminal. Try it now - https://www.nymbo.net/nymbot

1 reply

replied to nroggendorff's post 6 months ago

I'm baffled by the silence from staff regarding this...

replied to nroggendorff's post 6 months ago

This has to be a JOKE, I still haven't seen anything official clarifying what the limits even are. I'm so burnt about the state of this site. What's even the point anymore if spaces are this scarce? Why wouldn't I just let my code live on GitHub like everything else? It's too expensive to have functional spaces in the first place now unless its runs on CPU or Zero. 20x ($2) inference credits/month is a JOKE. Literally the only thing HF brings to the table anymore is zerogpu, and that's not enough lmao.

Edit: sorry for venting in your thread, o7

reacted to nroggendorff's post with 🧠 6 months ago

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I am now being charged for paused and unstarted spaces out of the blue.
UPDATE: The problem seems to be resolved, but I won't be able to make any new models or datasets, or test any training scripts for the foreseeable future.

The unstarted spaces I can get behind. I would've appreciated a warning email first, but whatever. However, every time I restart the active usage goes up, despite all of my spaces being moved to CPU (free), and being paused.

13 replies

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