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nroggendorff  updated a Space 16 days ago
none-yet/README
nroggendorff  new activity 3 months ago
none-yet/README:can anyone help me with my project pls?
nroggendorff  updated a Space 3 months ago
none-yet/README
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Reality123b 
posted an update 6 days ago
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can you people please benchmark this thing i made? (i mean benchmark as in benchmarks) here is the link: xylaria2s.vercel.app/api/v1/chat/completions and please give benchmarks plus proofs underneath this post, please help me out
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Shrijanagain 
posted an update 16 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! 💡
nroggendorff 
updated a Space 16 days ago
Shrijanagain 
posted an update 19 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 20 days ago
KingNish 
posted an update 30 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 🙏
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prithivMLmods 
posted an update about 1 month ago
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Wan2.2-I2V-Fast with highly upscaled sequential frame sampling is now available as a Spaces demo, built using Wan2.2-I2V and FLUX.2-Klein. Try the demo using the links below.👇

➠ wan2.2-i2v-fast : prithivMLmods/wan2.2-i2v-fast
➠ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
➠ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

⤷ To learn more, visit the app page or the respective model pages.
mahwizzzz 
posted an update about 2 months ago
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Released lafzyn , built over Qwen, an Urdu language model that converts Urdu text into IPA phonetic transcription, with GGUF builds for local inference.

Release contents:
- mahwizzzz/lafzyn: full weights
- mahwizzzz/lafzyn-gguf: quantized builds

Try it out 🤗
Demo: https://huggingface.co/spaces/mahwizzzz/Lafzyn
prithivMLmods 
posted an update about 2 months ago
prithivMLmods 
posted an update about 2 months ago
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PiD — Pixel Diffusion Decoder Image Edit Upscale and Image Generation Upscale, an all-in-one demo, is now live on Spaces! Great improvements in realism-based image generation and editing are powered by FLUX.2-Klein, while image generation is paired with Z-Image, and upscaling is enabled by default!

🤗 Space: prithivMLmods/PiD-Image-Upscaler
🔗 Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

🤗 > To learn more, visit the app page or the respective model pages.
prithivMLmods 
posted an update about 2 months ago
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I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached “Space of the Week”! A few Spaces are still topping the list even after many months.

Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.

Thanks for all the community support! 🤗❤️

🔗 Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
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Shrijanagain 
posted an update about 2 months 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
prithivMLmods 
posted an update 3 months ago
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Multimodal-Edge Demo, a node-based inference canvas demo, is now live on Spaces. It features node-based Transformers for fast inference across 10+ edge-device multimodal models on the Hub, all within a single space. The series includes models from Qwen3.5, Qwen3-VL, Gemma 4, and the LFM 2.5 VL model series, with support for reasoning and grounding tasks.

🤗 Demo: prithivMLmods/Multimodal-Edge-Node
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
✅ Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps

🤗 > To learn more, visit the app page or the respective model pages.
prithivMLmods 
posted an update 3 months ago
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Now, a collection of various compression schemes for Qwen3.6 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. 👇

🔗 Qwen3.6-MoE: https://huggingface.co/collections/prithivMLmods/qwen36-35b-a3b-compressions
🔗 Qwen3.6-27B Compressions: https://huggingface.co/collections/prithivMLmods/qwen36-27b-compressions

🤗 > To learn more, visit the app page or the respective model pages.
prithivMLmods 
posted an update 3 months ago
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HY-World-2.0 — A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds is now available on Spaces, and it works both as native Gradio components and in Gradio server mode.

> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo

🤗To learn more, visit the app page or the respective model pages.
prithivMLmods 
posted an update 3 months ago
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A new comparator on Spaces showcases Standard FLUX.2 Decoder vs. FLUX.2 Small Decoder. The Small Decoder is ~1.4× faster, uses ~1.4× less VRAM, and maintains near-identical image quality. It has ~28M parameters with narrower channels [96, 192, 384, 384] vs. [128, 256, 512, 512], and the demo supports sequence generation by running both decoders simultaneously and comparing the results side by side.

🤗 Comparator: https://huggingface.co/spaces/prithivMLmods/Flux.2-4B-Decoder-Comparator
🔗 FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
🚁 Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

🤗 > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
namanvats 
posted an update 3 months ago
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Ran a small controlled study on a frozen 40-task slice of Harbor Terminal-Bench-Pro, using the same model (minimax/minimax-m2.5) with two agent harnesses: Goose and OpenHands-SDK.

Under the base setup, reducing the turn budget from 100 to 60 pushed the two harnesses in opposite directions:

* Goose: 0.450 → 0.525
* OpenHands-SDK: 0.575 → 0.500

A tweaked 60-turn setup brought OpenHands-SDK back to 0.575. At their best, both harnesses reached the same 0.575 pass rate.

What surprised me most was the token profile: in this setup, the reported token usage for OpenHands-SDK was dramatically higher than Goose while converging to the same best score.

Same model, same task slice, different harness behavior under a tighter interaction budget.

Dataset:
namanvats/harbor-goose-openhands-benchmark

Code/configs:
https://github.com/namanvats/harbor-agent-ablation
prithivMLmods 
posted an update 3 months ago
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Now, a collection of various compression schemes for Gemma 4 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. 👇

🔗Gemma 4 Compression(s)- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions
🔗Gemma 4 Uncensored [MAX] + Compression(s) - [`β ]- https://huggingface.co/collections/prithivMLmods/gemma-4-uncensored-max-compressions
🔗Gemma 4 Compression(s) - MoE- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions-moe
🔗Gemma-4 F32 GGUF- https://huggingface.co/collections/prithivMLmods/gemma-4-f32-gguf

🤗 > To learn more, visit the app page or the respective model pages.
prithivMLmods 
posted an update 3 months ago
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Now the demo for image detection based on SAM3 and Gemma-4 (*Filter) is available on Spaces, using full-fledged Transformers inference with multimodal reasoning for processed images. It also supports video segmentation (mask), video segmentation (annotation), and image click segmentation.

🤗 Demo Space: prithivMLmods/SAM3-Gemma4-CUDA
🥽 SAM3: facebook/sam3
🔗 gemma-4-E2B-it: google/gemma-4-E2B-it

To learn more, visit the app page or the respective model pages.
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prithivMLmods 
posted an update 3 months ago
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The demo for Image Detection (*Filter) based on SAM3 and Qwen-3.5 is now available on Hugging Face Spaces using Transformers inference, with multimodal reasoning for processed images, and it also supports video segmentation (mask), video segmentation (annotation), and image click segmentation.

🤗 Demo Space: prithivMLmods/SAM3-Plus-Qwen3.5
🥽 SAM3: facebook/sam3
🔗 Qwen-3.5: Qwen/Qwen3.5-2B

To learn more, visit the app page or the respective model pages.
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