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BerkanDogan  new activity 7 days ago
HF1BitLLM/README:Running Llama3-8B-1.58-100B-tokens on CPU
Murban35  new activity 17 days ago
HF1BitLLM/README:Running Llama3-8B-1.58-100B-tokens on CPU
BerkanDogan  new activity 17 days ago
HF1BitLLM/README:Running Llama3-8B-1.58-100B-tokens on CPU
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Shrijanagain 
posted an update 4 days ago
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Welcome Researcher and Developers!

SKT AI Labs, we are pushing the boundaries of AI architecture and research—and today, we are thrilled to open our doors to the global research community!

​We warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.

​🧪 What You Can Explore:

We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.

---------- CHECK OUT:

SPACE : SKT-NRS/RD
EXPERIMENT : sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1

​🤝 Your Feedback Shapes the Future :

​If it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.

​If it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.

​We firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!

You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.

​Let's innovate and build together! 💡
BerkanDogan 
in HF1BitLLM/README 7 days ago

Running Llama3-8B-1.58-100B-tokens on CPU

9
#2 opened over 1 year ago by
chiauho
Shrijanagain 
posted an update 7 days ago
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🚀 Big News for the AI Community! 🔥

We’re excited to release NRS_QWEN_MYTHOS_1M — a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, we’ve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.

🔥 Why This Model is a Game-Changer:
✅ 100x Reasoning Capacity — Exceptional deep logical thinking and complex problem-solving
✅ 1 Million Token Context — Perfect for massive codebases, long documents, and multi-turn agentic workflows
✅ Advanced Thinking Mode — Native <think> tags for true step-by-step Chain-of-Thought reasoning
✅ Tool-Use Ready — Optimized for Python execution, Web Search, and self-correction
✅ Blazing Fast — Runs smoothly on consumer GPUs like RTX 3090/4090

Technical Highlights:

Base: Qwen 3.5 9B
Tuning: NRS-specific high-quality reasoning data
Context: 1M Tokens (YaRN Scaling)
License: NRS DOCS

Whether you’re a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning — this model is built for you.

👉 Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M

Drop a comment: What will you build with it first? 👇
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity
Murban35 
in HF1BitLLM/README 17 days ago

Running Llama3-8B-1.58-100B-tokens on CPU

9
#2 opened over 1 year ago by
chiauho
Abhaykoul 
posted an update 18 days ago
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Shipped v0.1.2 of vtx — a minimalist coding agent for the terminal.

Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.

Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.

What ships out of the box:

→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist

This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.

Apache 2.0. uv tool install vtx-coding-agent and you're running.

GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent

Built in the open. Feedback, extensions, and PRs welcome.
alielfilali01 
posted an update about 1 month ago
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Plans in HTML > Plans in Markdown
Shrijanagain 
posted an update about 1 month ago
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We are pleased to announce that the W-IMG Vision Dataset infrastructure is officially live.

The complete asset infrastructure is now accessible on Hugging Face for internal validation and architecture scaling targets.

Dataset Endpoint - sKT-Ai-Labs/W-IMG

#SovereignAI #ComputerVision #MachineLearning #OpenSource
Parveshiiii 
posted an update 3 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 3 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

Shrijanagain 
posted an update 3 months ago
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sKT-Ai-Labs

Join fast we will soon published tokens and all join and get started because we will soon off join request button if you want you can join fast guys
  • 1 reply
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Shrijanagain 
posted an update 3 months ago
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​🚀 Bharat AI Revolution ka Hissa Banein! 🇮🇳

​Kya aap Bharat ko AI ki duniya mein ek nayi pehchan dilana chahte hain ?

SKT AI Labs sirf ek naam nahi, ek mission hai—desh ko digital shakti dene ka aur "Viksit Bharat" ke sapne ko sach karne ka.

​Humse Kyun Judein?

​1. Desh ka Apna AI: Hum aise models bana rahe hain jo khas taur par Bharat ki zarooraton aur bhashaon ke liye hain.

​2. Open Collaboration: Hamare Hugging Face repository par hamare kaam ko dekhein, test karein aur apna yogdan dein.

3. Technological Growth: Agar aap student hain, developer hain ya tech enthusiast hain, toh hamare saath naya seekhne aur grow karne ka yeh behtareen mauka hai.

​Join here

sKT-Ai-Labs
🔗
sKT-Ai-Labs

​Aaiye, saath milkar Bharat AI Revolution ko aage badhate hain! 💻🔥

​#SKTAILabs #DigitalIndia #AIRevolution #ViksitBharat #TechInnovation #JoinTheMission
Shrijanagain 
posted an update 3 months ago
Parveshiiii 
posted an update 3 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
Shrijanagain 
posted an update 3 months ago
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​We are thrilled to announce the launch of SKT-OMNI-CORPUS-2T, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch.
​Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge.

​💎 Key Highlights:

​•• Massive Scale: Targeting a multi-terabyte architecture for 2T-level tokenization.

•• ​Pure Quality: Curated from 500+ Elite Sources

•• ​Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training.

​🤝 Open for Collaboration!

​We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together.

​Explore the Dataset on Hugging Face:

🔗 https://huggingface.co/datasets/Shrijanagain/SKT-OMNI-CORPUS-146T-V1

DSR -- 🔗 https://huggingface.co/datasets/Shrijanagain/SKT-DSRx10000

​#AI #MachineLearning #OpenSource #IndicAI #SKTAILABS #LLM #BigData #HuggingFace #InnovationIndia
Goekdeniz-Guelmez 
submitted a paper to Daily Papers 4 months ago
Goekdeniz-Guelmez 
authored a paper 4 months ago
Parveshiiii 
posted an update 5 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 5 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 5 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
Goekdeniz-Guelmez 
authored a paper 6 months ago