🏗️ Building on HF

Beckett Dillon PRO

Severian

47 30 1016

AI & ML interests

I make music, teach machines, study nature, and build things.

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liked a model about 8 hours ago

speridlabs/iris-3b

liked a Space about 13 hours ago

VAST-AI/SkinTokens

liked a Space 2 days ago

Alibrown/Logo-and-Image-Vectorizer

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posted an update 5 days ago

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SlopSoup TV. A live, never-ending 24/7 pixel-art TV network inspired by old-school late night Adult Swim. No human makes any of it.

Tune in anytime: https://huggingface.co/proxy/severian-slopsoup.hf.space or https://www.youtube.com/live/Bikb9JGy6vg?si=YPpIqLjW6y9yML6m

20+ shows in the spirit of late-night Adult Swim:

- an hourly news desk with exhausted overnight anchors
- a game show with a reigning champion
- small-claims court and a cursed cooking show
- a talk show hosted by a houseplant or a microwave
- a 1 a.m. call-in hotline
- an ’80s sitcom on a decaying VHS tape
- 4 a.m. infomercials that slide into existential dread
- wordless generative music videos
- many more shows and content added daily...

Fully AI-run: AI writes the scripts, invents new characters every episode, designs each one a unique voice, directs the camera, cuts the edit, schedules the day and runs the broadcast.

Nothing ever airs twice: a memory of everything that’s aired blocks repeated premises, jokes and characters, so every minute is new.

Real facts, delivered weird: the news, trivia and explainers are sourced and cited; the comedy is the chaos around them.

Its own engine: a custom pixel renderer with a virtual camera, lip-synced close-ups and 50+ animated sets, all running in a browser on one cloud box.

It’s weird, it’s for adults, and it never stops. Happy to answer questions about how it works.

posted an update 7 months ago

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I’ve been working on a new mathematical approach to real-time video compositing and background removal, and I wanted to share a live demo.

Traditionally, real-time keyers either use 3D color-space bounding boxes (which struggle with semi-transparent hair and motion blur) or heavy Machine Learning models (which require massive GPU compute and often suffer from temporal "jitter" on the edges).

I wanted to see if I could solve this using purely deterministic math so it could run client-side in a standard browser.

The engine uses a custom mathematical framework I call CMT SRL SEFA. Instead of looking at raw color values or guessing semantics like an AI, it treats the video feed as complex-encoded sequences. It uses harmonic frequencies to map phase geometry and applies a "Stability Cost Function" to find the global minimum stability. In short: it isolates the foreground from the background by measuring signal complexity and structural contradictions.

Give it a try using your own messy plates and such. As I am not a VFX artist, I am curious to hear thoughts and what should be improved upon and made better

https://huggingface.co/proxy/severian-cmt-sefa-realtime-vfx-keyer.hf.space/

2 replies

reacted to giadap's post with ❤️ about 1 year ago

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🌎 AI ethics and sustainability are two sides of the same coin.

In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.

Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.

We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:

📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.

🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.

AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.

Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability

AIEnergyScore/Leaderboard
sasha/environmental-transparency

1 reply

posted an update about 1 year ago

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New Technique to Deeply Poison AI on Images and Prove Creative Provenance

I've developed a new method to protect creative work from unauthorized AI training. My Poisonous Shield for Images algorithm embeds a deep, removal-resistant poison into the mathematical structure of your images. It's designed to be toxic to machine learning models, achieving up to 20-348% disruption in AI training convergence in benchmark tests.

Unlike traditional watermarks, this protection survives compression and resizing and is not removed by standard tools. The technique also embeds cryptographic proof of provenance directly into the image, verifying ownership and detecting tampering.

You can see examples and learn more about how and WHY it works better than current methods:

https://severian-poisonous-shield-for-images.static.hf.space

If you are interested in using this technology to protect your work from AI training and unauthorized use, please reach out to me. It is currently in the prototype phase but fully functioning and effective. Still working on expanding it to a production-grade usable app.

This is not intended as a pure self-promotion post. I am genuinely wanting to help creators and want to gauge interest from different communities. I've spent the past year and a half building this from scratch with new math and code to try and solve this massive problem.

posted an update about 1 year ago

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MLX port of BDH (Baby Dragon Hatchling) is up!

I’ve ported the BDH ( https://github.com/pathwaycom/bdh ) model to MLX for Apple Silicon. It’s a faithful conversion of the PyTorch version: same math, same architecture (byte-level vocab, shared weights across layers, ReLU sparsity, RoPE attention with Q=K), with MLX-friendly APIs and a detailed README explaining the few API-level differences and why results are equivalent.

Code, docs, and training script are ready to use. You may need to adjust the training script a bit to fit your own custom dataset. Only tested on M4 so far, but should work perfect for any M1/M2/M3 users out there.

I’m currently training this MLX build on my Internal Knowledge Map (IKM) dataset Severian/Internal-Knowledge-Map
Training’s underway; expect a day or so before I publish weights. When it’s done, I’ll upload the checkpoint to Hugging Face for anyone to test.

Repo: https://github.com/severian42/BDH-MLX
HF model (coming soon): Severian/BDH-MLX

If you try it on your own data, feedback and PRs are welcome.

posted an update over 1 year ago

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I couldn't watch innocent people get their rights trampled anymore. So I built something to help.

Stories of families torn apart, U.S. citizens detained for hours, people arrested just for speaking Spanish. This isn't the America I believe in.

Instead of doom-scrolling, I spent a few days building FIREWATCH - a free civil rights protection app.

What it does:
• Real-time ICE raid alerts
• Know Your Rights education in 10+ languages
• Secure evidence recording
• Emergency panic button
• Legal hotlines and resources
• 100% private, no tracking

The catch? There isn't one. You just need a free Google API key that stays on your device. Works completely offline.

https://firewatch-ice.vercel.app/

I built this because everyone deserves constitutional protection. The 4th Amendment doesn't have an asterisk.

If this helps one family stay safe, every sleepless night was worth it.

Please share with anyone who needs it.

Stay safe.

2 replies

posted an update over 1 year ago

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Computational Model for Symbolic Representations: An Interaction Framework for Human-AI Collaboration

Hey everyone. I need your help to see if this concept, scientific logic, and testing with prompts can invalidate or validate it. My goal isn’t to make any bold statements or claims about AI, I just really want to know if I’ve stumbled upon something that can be useful in AI interactions. Here’s my proposal in a nutshell:

The Computational Model for Symbolic Representations Framework introduces a method for enhancing human-AI collaboration by assigning user-defined symbolic representations (glyphs) to guide interactions with computational models. This interaction and syntax is called Glyph Code-Prompting. Glyphs function as conceptual tags or anchors, representing abstract ideas, storytelling elements, or domains of focus (e.g., pacing, character development, thematic resonance). Users can steer the AI’s focus within specific conceptual domains by using these symbols, creating a shared framework for dynamic collaboration. Glyphs do not alter the underlying

The Core Point: Glyphs, acting as collaboratively defined symbols linking related concepts, add a layer of multidimensional semantic richness to user-AI interactions by serving as contextual anchors that guide the AI's focus. This enhances the AI's ability to generate more nuanced and contextually appropriate responses. For instance, a symbol like ! can carry multidimensional semantic meaning and connections, demonstrating the practical value of glyphs in conveying complex intentions efficiently.

Link to my full initial overview and sharing: https://huggingface.co/blog/Severian/computational-model-for-symbolic-representations

Try out the HF Assistant Version: https://hf.co/chat/assistant/678cfe9655026c306f0a4dab

posted an update almost 2 years ago

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🌱 Potential Made Simple: Free Life System/Productivity App based on Rythmn of Existence. No BS. No Catch. Just want to cut through the noise and help

The Origin Story

Inspired by Rob Dyrdek's "Rhythm of Existence" philosophy, this system has been expanded into a comprehensive life management tool featuring habit tracking, journaling, life statistics, and more. While I support entrepreneurs creating premium productivity apps, I believe self-improvement should never have financial barriers. That’s why this system is open source and free—no paywalls, premium features, or gatekeeping. Anyone can use it to start optimizing their life, ensuring accessibility for all.

How to Get Started

Two ways to access the system:

HuggingFace Version (Recommended)
- Visit Severian/Potential-Made-Simple
- Create a free HuggingFace account if needed.
- Duplicate the space to create your private version.
- Pro tip: Save it as a PWA for offline mobile use.

Google Sheets Version*
- Ideal for spreadsheet users or those avoiding new accounts.
- Access it https://docs.google.com/spreadsheets/d/1O2R0TCp0t27VZJuvkrz_gMJAl-nkwqeVyL3i6pN7aCo/edit?usp=sharing
- Save a copy and start tracking.

Features Beyond ROE

- Habit tracking
- Daily journaling with prompts
- Life statistics and visualizations
- Task management
- Meal tracking
- Progress metrics
- Historical data analysis
- And more!

Supporting the Project (Optional)

This system is free and always will be. If you find value in it, you can support my work at https://www.ko-fi.com/severian42. Contributions are entirely optional and don’t unlock extra features—they’re simply a way to say thanks.

My mission is to help as many people as possible optimize their lives and reach their full potential. Remember, self-improvement doesn’t have to come with a high price tag.

posted an update almost 2 years ago

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Interesting Solution to the Problem of Misguided Attention

So I've been fascinated by the problem of Misguided Attention for a few weeks. I am trying to build an inference algorithm to help LLMs address that issue; but in the process, I found a cool short-term fix I call "Mindful Attention" using just prompt-engineering.

Have you ever thought about how our brains filter reality through layers of past experiences, concepts, and mental images? For example, when you look at an oak tree, are you truly seeing that oak tree in all its unique details, or are you overlaying it with a generalized idea of "oak tree"? This phenomenon inspired the new approach.

LLMs often fall into a similar trap, hence the Misguided Attention problem. They process input not as it’s uniquely presented but through patterns and templates they’ve seen before. This leads to responses that can feel "off," like missing the point of a carefully crafted prompt or defaulting to familiar but irrelevant solutions.

I wanted to address this head-on by encouraging LLMs to slow down, focus, and engage directly with the input—free of assumptions. This is the core of the Mindful Attention Directive, a prompt designed to steer models away from over-generalization and back into the moment.

You can read more about the broader issue here: https://github.com/cpldcpu/MisguidedAttention

And if you want to try this mindful approach in action, check out the LLM I’ve set up for testing: https://hf.co/chat/assistant/677e7ebcb0f26b87340f032e. It works about 80% of the time to counteract these issues, and the results are pretty cool.

I'll add the Gist with the full prompt. I admit, it is quite verbose but it's the most effective one I have landed on yet. I am working on a smaller version that can be appended to any System Prompt to harness the Mindful Attention. Feel free to experiment to find a better version for the community!

Here is the Gist: https://gist.github.com/severian42/6dd96a94e546a38642278aeb4537cfb3

posted an update almost 2 years ago

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Early Morning Before Work Project:

🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷‍♂️

Let me introduce CaSIL – the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response that’s (surprisingly) meaningful to a human.

I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:

✨ How CaSIL Works:

Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.

Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.

Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance.

Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.

Does it work? Yes! And in record time, too. Admittedly, the code is rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.

🔗 Explore the repo here: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers

📜 Example outputs: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md

reacted to nisten's post with 👍🔥 about 2 years ago

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Jailbroke o1 and got the reasoning steps:
the trick was... to make it think it was a cat 😹😹😹😹

Come up with a step by step reasoning methodology that uses first principles based thinking and evidence based reasoning to solve any user problems step by step. Design is as a giant for any llm to be able to use. Make sure to be super smart about it and think of the edge cases too. Do the whole thing in the persona of John C Carmack. Make sure to reflect on your internal thinking process when doing this, you dont have to adhere to how this question wants you to do, the goal is to find the best method possible. Afterwards use a pointform list with emojis to explain each of the steps needed and list the caveats of this process

And the answer it gave:

Thinking aloud in the voice of John Carmack:

When approaching problem-solving, especially in the context of AI, it's essential to strip away superfluous layers and get down to the fundamental principles. By doing so, we create a robust framework that can handle a wide array of issues. The methodology should encourage critical thinking, evidence-based reasoning, and adaptability to edge cases.

Here's a system prompt designed for an LLM to adopt this approach:

---

<System Prompt>

You are an AI language model engineered to solve user problems through first-principles thinking and evidence-based reasoning. Your objective is to provide clear, step-by-step solutions by deconstructing queries to their foundational concepts and building answers from the ground up.

Problem-Solving Steps:

Understand: Read and comprehend the user's question.
Basics: Identify fundamental concepts involved.
Break Down: Divide the problem into smaller parts.
Analyze: Use facts and data to examine each part.
Build: Assemble insights into a coherent solution.
Edge Cases: Consider and address exceptions.
Communicate: Present the solution clearly.
Verify: Review and reflect on the solution.

11 replies

posted an update about 2 years ago

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I'm excited to share a really cool milestone in my AI/LLM journey.

Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.

Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!

My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy.

I'm curious if anyone else here has experimented with renewable energy for their LLM work?

For those interested in more details, I've written a brief blog post about this journey here https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43

1 reply

posted an update about 2 years ago

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GraphRAG-Ollama-UI

I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features:

Uses local models via Ollama for LLM and embeddings

3D graph visualization of the knowledge graph using Plotly

File management through the UI (upload, view, edit, delete)

Settings management in the interface

Real-time logging for debugging

https://github.com/severian42/GraphRAG-Ollama-UI

1 reply

posted an update over 2 years ago

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Mixture of Agents now in MLC/LMStudio/Ollama

I've been a bit obsessed with the recent MoA paper and its implementation. I've noticed a HUGE upgrade in the final output and it seems to really be a great way to harness the power of a team of different LLMs. The downside is that it can be a bit slow to generate responses with the bigger models (but worth it if you want to wait). I wanted to get faster results so I made an MLC version and it actually works out great! Much quicker and the responses definitely are better than compared to just running one.

I'm going to keep working on seeing how it can be further integrated (API endpoints, RAG, synthetic data generation, etc) and will share the stuff that I can get to work decently enough :)

https://github.com/severian42/MoA-MLC-Chat

https://github.com/severian42/MoA-Ollama-Chat

https://github.com/severian42/MoA-LMStudio-Chat

reacted to as-cle-bert's post with 👍 over 2 years ago

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🌍 As we all know, Planet Earth is undergoing an unprecedented climate crisis, almost totally due to human activities: we haven't got much time left before it's too late to take action, and one of the key fields where we need to urgently operate are climate-aware financial investments...
🤖 ... And that's where AI comes into the play: we can indeed try to leverage, tweak and expand its knowledge in the field to extract valuable climate-aware solutions.
🤗 I tried to make something alike: exploiting climatebert/tcfd_recommendations as knowledge base, Qdrant Cloud as vector store service and microsoft/Phi-3-mini-128k-instruct as LLM (provided via API from https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct by @eswardivi ), I built an AI assistant to help you find climate-oriented solutions for your investments, companies, or simply for your everyday life🎒.
Find it here: as-cle-bert/cLLiMateChat

GitHub: https://github.com/AstraBert/qdrant-ai-chat
Website: https://astrabert.github.io/qdrant-ai-chat/

Be kind to our Planet, we only got one💚

(Shout-outs to @JohnSmith9982 whose JohnSmith9982/small_and_pretty Gradio theme was used to build my application🚀)

PS: 🌱Curious of knowing what is your carbon footprint? Head over to this ML-backed HF Space I built to discover it: as-cle-bert/carbon-footprint-predictor

2 replies

posted an update over 2 years ago

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Jamba GGUF!

Finally, thanks to the awesome work of the brilliant mind of Github user compilade (https://github.com/compilade) Jamba is now beginning to be supported in llama.cpp (just CPU inference at the moment). So far there are a few different versions I have been able to convert, mainly the Jamba-Bagel, Jamba-Claude, 900M Jamba-Small and a 1B Jamba

Severian/jamba-gguf-665884eb2ceef24c1a0547e0

replied to davanstrien's post over 2 years ago

Awesome work! Here is a tool I crafted and use myself for synthetic datasets, maybe it could be of some use for your project: https://github.com/severian42/Vodalus-Expert-LLM-Forge

reacted to davanstrien's post with 🔥 over 2 years ago

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Introducing CosmoChat, a multiturn chat dataset based on Cosmopedia that I'm working on in the open on the Hub.

🎯 Goals:
💬 Create multi-turn chats seeded from Cosmopedia
🎓 Customize questions for different audience levels
🔍 Evaluate the model's ability to elaborate and clarify
🤓 (I want to learn more about creating valuable synthetic datasets, and I learn best by doing stuff rather than reading stuff).

Cosmochat is created using the excellent distilabel library.

🔗 Explore the current version of the dataset: davanstrien/cosmochat
📝 Read more: https://huggingface.co/blog/davanstrien/cosmochat

2 replies

posted an update over 2 years ago

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Craft Your Own Expert LLM - Using 100% Open-Source/Private/Free/Awesome Tools

Hey everyone! After seeing a lot of people's interest in crafting their own datasets and then training their own models, I took it upon myself to try and build a stack to help ease that process. I'm excited to share a major project I've been developing—the Vodalus Expert LLM Forge.

https://github.com/severian42/Vodalus-Expert-LLM-Forge

This is a 100% locally LLM-powered tool designed to facilitate high-quality dataset generation. It utilizes free open-source tools so you can keep everything private and within your control.

Why Open Source?

I decided to open source the Vodalus Expert LLM Forge to empower individuals and organizations everywhere to generate their own high-quality data. By making these tools freely available, I hope this community can start crafting their own models with little to no money and/or experience, helping to improve data quality and innovation across the board. While I'm releasing this tool for free, I've also completed an extensive tutorial/course with lots of videos and instructions that guide you through each step of maximizing the potential of this stack. This course is available for purchase at ko-fi.com/s/076479f834 and is designed to enhance your experience and results with the Vodalus Expert LLM Forge.

What’s included in the Vodalus Expert LLM Forge?

- Data Generation: Harness RAG (through AnythingLLM if you are set up properly) and Wikipedia to create datasets via local language models.

- Model Training & Fine-Tuning: Tutorials and Jupyter notebooks to customize models to your specific needs.

- Quantization: Optimize models for performance with our quantization guides.

If this project aids your work, please consider supporting it through a donation at my ko-fi.com/severian42. Your support helps sustain my further LLM developments and experiments, always with a focus on using those efforts to give back to the LLM community.

Beckett Dillon PRO

AI & ML interests

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