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Finetune. Train. Merge.

Shrijanagain

posted an update 12 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! 💡

Shrijanagain

posted an update 16 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

eienmojiki

posted an update 17 days ago

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165

Hi everyone,

I've created a Gradio space for embedding and extracting invisible watermarks in images:
👉 eienmojiki/blind-watermark-studio

It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.

Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei

:).

KingNish

posted an update 26 days ago

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We trained an open-source Mythos like cybersecurity LLM for the Build Small Hackathon meet OpenMythos

Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.

Trained on: H100s from Modal

The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.

Everything is open:
🤖 Demo → build-small-hackathon/OpenMythos
🧠 Model → build-small-hackathon/OpenMythos
📦 CVE Dataset → build-small-hackathon/CVE_Vulnerailities_Detailed
📄 ArXiv Dataset → himanshu17HF/ArvixImport-Filtered-Final

Try it out and let us know where it breaks 🙏

2 replies

Abhaykoul

posted an update 26 days ago

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270

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.

Shrijanagain

posted an update about 2 months ago

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2621

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

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

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

1aurent

authored a paper 3 months ago

Voxtral TTS

Paper • 2603.25551 • Published Mar 26 • 63

Shrijanagain

posted an update 4 months ago

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SOME NEW HINDI + ENGLISH DATASETS

🔗
- sKT-Ai-Labs/HIN
- sKT-Ai-Labs/SKT-MIX
- sKT-Ai-Labs/ST-H

Download and Use And Train Models

You Can Alsoo Use ST-x-LIGHTING Module For Faster Training

pip install ST-x-LIGHT-V11

2 replies

Shrijanagain

posted an update 4 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

Nymbo

posted an update 4 months ago

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7627

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

1aurent

authored a paper 5 months ago

Ministral 3

Paper • 2601.08584 • Published Jan 13 • 63

Nymbo

posted an update 6 months ago

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3637

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.

Nymbo

posted an update 7 months ago

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2850

🚨 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

KingNish

posted an update 7 months ago

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3821

Muon vs MuonClip vs Muon+Adamw

Muon has gone from an experiment to a mainstream optimizer, but does it hold up for fine‑tuning? We ran head‑to‑head tests on Qwen3‑4B (10k+ high‑quality instruction rows) to find out.

Short story: Pure Muon converged fastest at the start, but its gradient‑norm spikes made training unstable. MuonClip (Kimi K2’s clipping) stabilizes long pretraining runs, yet in our small‑scale fine‑tune it underperformed, lower token accuracy and slower convergence. The winner was the hybrid: Muon for 2D layers + AdamW for 1D layers. It delivered the best balance of stability and final performance and even beat vanilla AdamW.

Takeaway: for small-scale fine-tuning, hybrid = practical and reliable.

Next Step: scale to larger models/datasets to see if Muon’s spikes become catastrophic or if clipping wins out.

Full Blog Link: https://huggingface.co/blog/KingNish/optimizer-part1

KingNish

posted an update 7 months ago

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2850

I tested Muon vs MuonClip vs Muon+AdamW for fine-tuning LLMs
Just published a blog on that, Read here 👉 https://huggingface.co/blog/KingNish/optimizer-part1

1 reply

Nymbo

posted an update 8 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

Nymbo

posted an update 8 months ago

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I've added an 11th tool to the Nymbo/Tools MCP server, it's for your Obsidian_Vault. I'd argue it's far more context-efficient than any other Obsidian MCP I've seen, and doesn't require any plugins. Also some big improvements to the Web_Search and Web_Fetch tools.

# Obsidian_Vault Tool

It's basically a read-only version of the File_System tool, but it works so well for navigating Obsidian without unnecessary context. It supports recursive (full-text) search across the entire vault, and supports offset so the agent can "scroll" through a document without re-consuming tokens.

Run the server locally and set the OBSIDIAN_VAULT_ROOT environment variable to your vault's root path. If you don't use Obsidian, this is perfectly usable as simply a read-only filesystem.

# Web_Search Improvements

The Web_Search tool previously just used DuckDuckGo as a backend search engine, but now it also supports Bing, Brave, Yahoo, and Wikipedia. Default engine is auto which provides results from all backends in recommended order. Still doesn't require any kind of API or auth for Web_Search.

There's also a new date filter to limit results to those created in the past day, week, month, or year. Oh, and uhh, SafeSearch is now off by default :)

# Web_Fetch Improvements

As context-efficient as the Markdown mode is for web browsing, sometimes it does lose important context in the conversion from HTML to Markdown. So I've added a new HTML mode to the Web_Fetch tool that basically executes a cURL request on the URL, returning the full HTML page if necessary.

# A Note on Claude Skills

I've been having fun with the new File_System and Shell_Command tools. Using Claude Skills doesn't currently work in the public HF space because of environment restrictions, but using Skills works perfectly well running locally.

Happy building ~

Nymbo

posted an update 9 months ago

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Two new tools added to the Nymbo/Tools MCP server, File_System and Shell_Exec. You can theoretically do basically anything with these two tools, and it should enable support for many Claude Skills.

GPT-5-Codex proves that for many cases, shell commands really are all you need, and Claude Skills seem to lean into this. The thing is, nothing about the design of Claude Skills actually restricts them to proprietary models!

# File_System

There's a new directory inside the repo called Filesystem, that's the agent's "root". It can perform the following actions : list, read, write, append, mkdir, move, copy, delete, info, help. It's able to keep this all within the scope of one tool call by making the Action field required and all other fields optional. Using a filesystem shouldn't require 15 different tools.

Files created in the public HF space live in the space's running container, and gets cleared when the space is restarted. When running the server locally, files are actually stored on disk.

# Shell_Exec

What good is a filesystem if you can't execute commands in that filesystem? This tool automatically detects if the server is running on Windows or Linux, and suggests using the appropriate shell (PowerShell/Bash). Both of these new tools require that the agent uses relative paths, rather than absolute paths. I could be convinced to back pedal on this.

# Closing Thoughts

The File_System and Shell_Exec tools aren't super polished yet, I'll continue to improve the agent's instructions and UX of using the new tools. Most of my testing was done with gpt-oss-20b and if it messes up, it gets the gist after one failed tool call. It should work perfectly fine for the GPU poor.

1 reply

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fluently's activity