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Structure based drug discovery
Tonic 
posted an update 8 days ago
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👋Hey there folks ,

I wanted to use a Jev model in a practical way , and I came up with this OCR pipeline using decision-models to evaluate OCR quality .

Check it out :

Tonic/OCR-with-JEV
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Parveshiiii 
posted an update 12 days ago
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Most AI image detectors work fine in the lab and fail on social media.

The reason is simple. Real photos get compressed, resized and re-uploaded again and again, and detectors trained on clean images read that damage as a sign of fakery. A real photo that has passed through a few platforms often gets flagged as AI-generated.

AIRealNet-2 is built to fix that. It is the upgrade to our original Modotte/AIRealNet, trained on images collected from platforms like Instagram and X, so it has seen the kinds of quality loss and compression real images go through online.

The training set has 98k images, split 50/50 between AI-generated and real, so the model has no class bias to lean on. Personal photos were excluded by design, including AI-edited ones.

The architecture is unchanged: a SwinV2 Tiny backbone with a binary classification head, about 0.2B parameters, working at 256x256 resolution. It stays fast and easy to deploy for research or production.

Model: Modotte/AIRealNet-2
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Parveshiiii 
posted an update 23 days ago
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Give your LLM a window to the web 🔎

I built Seekify, a Python tool that turns web search into clean, LLM-ready input for free.

Collecting sources for an LLM workflow usually means juggling search APIs, scraping pages, and cleaning up messy text. Seekify handles that in one place:

- No search API key needed
- Structured results with titles, URLs, and snippets
- Optional page text extraction so your model gets real content, not just links

Search the web, collect sources, and feed them straight into your pipeline, so you spend less time gathering data and more time building.

It's open source, and I'd love your feedback, ideas, and contributions.

👉 https://github.com/Parveshiiii/Seekify

If it's useful, a ⭐ on GitHub helps a lot!
Parveshiiii 
posted an update about 1 month ago
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Most NSFW classifiers break the second an image touches the internet.

They look great on pristine benchmarks, but in the wild, every social platform aggressively recompresses, downsamples, and degrades images.
The moment JPEG or WebP compression artifacts show up, confidence collapses and false positives spike.

SafeScan was built to survive actual platform pipelines.
Trained on 34,000 images under almost every major social media compression profile using a Vision Transformer backbone (google/vit-base-patch16-224). Instead of blunt binary filtering, it breaks decisions down across 5 clear categories:

• safe
• drawing
• sexy
• hentai
• porn

The result is a moderation model that actually generalizes to real-world internet feeds instead of fragile, uncompressed datasets.
Open-weight and available on Hugging Face:

Model: Parveshiiii/SafeScan
Parveshiiii 
posted an update about 1 month ago
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Most deepfake audio detectors are quietly cheating.

They don’t really listen to the speech — they just look at how long the embedding vector is. Once they figure that out, accuracy looks great on paper and falls apart in the wild.

AIRealNet-Audio was built to stop that shortcut.
It forces every feature onto the unit hypersphere (twice) so the model can only use direction, not magnitude. Trained on speech from 100+ different TTS and voice-cloning systems, plus real human recordings under heavy compression and noise.

The result is a detector that actually has to learn the artifacts instead of gaming the feature space.

Model: Modotte/AIRealNet-Audio
Tonic 
posted an update 6 months ago
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🙋🏻‍♂️ Hey there folks ,

Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.

Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.

meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response .

I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.

At least that's the concept !

check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth


- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
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Tonic 
posted an update 7 months ago
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🙋🏻‍♂️ Hey there folks,

since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !

Check this one out :
NuTonic/sat-bbox-metadata-sft-v1

the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .

hope you like it ! 🚀
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Tonic 
posted an update 7 months ago
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🙋🏻‍♂️ Hey there folks ,

I'm sharing huggingface's largest dataset of annotated statelite images today.

check it out here : NuTonic/sat-image-boundingbox-sft-full

I hope you like it , the idea is to be able to use this with small vision models 🚀
Parveshiiii 
posted an update 7 months ago
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🚀 Sonic: A lightweight Python audio processing library with tempo matching, BPM detection, time-stretching, resampling & track blending — now with GPU (CUDA) acceleration for 10x speed!

Perfect for quick remixes, batch edits or syncing tracks.

👉 https://github.com/Parveshiiii/Sonic

#Python #AudioProcessing #OpenSource #PyTorch
Parveshiiii 
posted an update 7 months ago
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Excited to announce my latest open-source release on Hugging Face: Parveshiiii/breast-cancer-detector.

This model has been trained and validated on external datasets to support medical research workflows. It is designed to provide reproducible benchmarks and serve as a foundation for further exploration in healthcare AI.

Key highlights:
- Built for medical research and diagnostic study contexts
- Validated against external datasets for reliability
- Openly available to empower the community in building stronger, more effective solutions

This release is part of my ongoing effort to make impactful AI research accessible through **Modotte**. A detailed blog post explaining the methodology, dataset handling, and validation process will be published soon.

You can explore the model here: Parveshiiii/breast-cancer-detector

#AI #MedicalResearch #DeepLearning #Healthcare #OpenSource #HuggingFace

Parveshiiii 
posted an update 8 months ago
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Just did something I’ve been meaning to try for ages.

In only 3 hours, on 10 billion+ tokens, I trained a custom BPE + tiktoken-style tokenizer using my new library microtok — and it hits the same token efficiency as Qwen3.

Tokenizers have always felt like black magic to me. We drop them into every LLM project, but actually training one from scratch? That always seemed way too complicated.

Turns out it doesn’t have to be.

microtok makes the whole process stupidly simple — literally just 3 lines of code. No heavy setup, no GPU required. I built it on top of the Hugging Face tokenizers library so it stays clean, fast, and actually understandable.

If you’ve ever wanted to look under the hood and build your own optimized vocabulary instead of just copying someone else’s, this is the entry point you’ve been waiting for.

I wrote up the full story, threw in a ready-to-run Colab template, and dropped the trained tokenizer on Hugging Face.

Blog → https://parveshiiii.github.io/blogs/microtok/
Trained tokenizer → https://huggingface.co/Parveshiiii/microtok
GitHub repo → https://github.com/Parveshiiii/microtok
Tonic 
posted an update 9 months ago
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🤔 Who would win ?

- a fully subsidized ai lab
OR
- 3 random students named
kurakurai ?

demo : Tonic/fr-on-device

if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
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Tonic 
posted an update 9 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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Parveshiiii 
posted an update 9 months ago
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Introducing Seekify — a truly non‑rate‑limiting search library for Python

Tired of hitting rate limits when building search features? I’ve built Seekify, a lightweight Python library that lets you perform searches without the usual throttling headaches.

🔹 Key highlights

- Simple API — plug it in and start searching instantly

- No rate‑limiting restrictions

- Designed for developers who need reliable search in projects, scripts, or apps

📦 Available now on PyPI:

pip install seekify

👉 Check out the repo: https:/github.com/Parveshiiii/Seekify
I’d love feedback, contributions, and ideas for real‑world use cases. Let’s make search smoother together!
Parveshiiii 
posted an update 10 months ago
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🚀 Wanna train your own AI Model or Tokenizer from scratch?

Building models isn’t just for big labs anymore — with the right data, compute, and workflow, you can create **custom AI models** and **tokenizers** tailored to any domain. Whether it’s NLP, domain‑specific datasets, or experimental architectures, training from scratch gives you full control over vocabulary, embeddings, and performance.

✨ Why train your own?
- Full control over vocabulary & tokenization
- Domain‑specific optimization (medical, legal, technical, etc.)
- Better performance on niche datasets
- Freedom to experiment with architectures

⚡ The best part?
- Tokenizer training (TikToken / BPE) can be done in **just 3 lines of code**.
- Model training runs smoothly on **Google Colab notebooks** — no expensive hardware required.

📂 Try out my work:
- 🔗 https://github.com/OE-Void/Tokenizer-from_scratch
- 🔗 https://github.com/OE-Void/GPT
Parveshiiii 
posted an update 10 months ago
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📢 The Announcement
Subject: XenArcAI is now Modotte – A New Chapter Begins! 🚀

Hello everyone,

We are thrilled to announce that XenArcAI is officially rebranding to Modotte!

Since our journey began, we’ve been committed to pushing the boundaries of AI through open-source innovation, research, and high-quality datasets. As we continue to evolve, we wanted a name that better represents our vision for a modern, interconnected future in the tech space.

What is changing?

The Name: Moving forward, all our projects, models, and community interactions will happen under the Modotte banner.

The Look: You’ll see our new logo and a fresh color palette appearing across our platforms.

What is staying the same?

The Core Team: It’s still the same people behind the scenes, including our founder, Parvesh Rawal.

Our Mission: We remain dedicated to releasing state-of-the-art open-source models and datasets.

Our Continuity: All existing models, datasets, and projects will remain exactly as they are—just with a new home.

This isn’t just a change in appearance; it’s a commitment to our next chapter of growth and discovery. We are so grateful for your ongoing support as we step into this new era.

Welcome to the future. Welcome to Modotte.

Best regards, The Modotte Team
harshinde 
updated a Space 10 months ago
Parveshiiii 
posted an update 11 months ago
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Hey everyone!
We’re excited to introduce our new Telegram group: https://t.me/XenArcAI

This space is built for **model builders, tech enthusiasts, and developers** who want to learn, share, and grow together. Whether you’re just starting out or already deep into AI/ML, you’ll find a supportive community ready to help with knowledge, ideas, and collaboration.

💡 Join us to:
- Connect with fellow developers and AI enthusiasts
- Share your projects, insights, and questions
- Learn from others and contribute to a growing knowledge base

👉 If you’re interested, hop in and be part of the conversation: https://t.me/XenArcAI
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Parveshiiii 
posted an update 12 months ago
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Another banger from XenArcAI! 🔥

We’re thrilled to unveil three powerful new releases that push the boundaries of AI research and development:

🔗 https://huggingface.co/XenArcAI/SparkEmbedding-300m

- A lightning-fast embedding model built for scale.
- Optimized for semantic search, clustering, and representation learning.

🔗 https://huggingface.co/datasets/XenArcAI/CodeX-7M-Non-Thinking

- A massive dataset of 7 million code samples.
- Designed for training models on raw coding patterns without reasoning layers.

🔗 https://huggingface.co/datasets/XenArcAI/CodeX-2M-Thinking

- A curated dataset of 2 million code samples.
- Focused on reasoning-driven coding tasks, enabling smarter AI coding assistants.

Together, these projects represent a leap forward in building smarter, faster, and more capable AI systems.

💡 Innovation meets dedication.
🌍 Knowledge meets responsibility.


Parveshiiii 
posted an update 12 months ago
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SparkEmbedding - SoTA cross lingual retrieval

Iam very happy to announce our latest embedding model sparkembedding-300m base on embeddinggemma-300m we fine tuned it on 1m extra examples spanning over 119 languages and result is this model achieves exceptional cross lingual retrieval

Model: https://huggingface.co/XenArcAI/SparkEmbedding-300m