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ginipick

posted an update about 4 hours ago

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552

# ✨ Dream of IKEA: The Future of AI Interior Design ✨

Hello, AI interior design enthusiasts! 🏠 Today I'm thrilled to introduce you to **"Dream of IKEA"** - an amazing project that will completely transform your living spaces!

## 🌟 What Can It Do?

**Dream of IKEA** is a magical tool that uses artificial intelligence to transform your ordinary spaces into the interior design of your dreams! 🪄

- 📸 Simply upload a photo of your room
- 💭 Describe your desired style or concept
- 🎨 The AI will redesign your space with stunning results!

## 🏆 Key Features

- **Diverse Style Selection** - Over 20 design styles including Minimalist, Bohemian, Japanese, Scandinavian, and more
- **User-Friendly Interface** - Beautiful, intuitive UI that anyone can use
- **High-Quality Image Generation** - Amazing quality powered by ControlNet and Stable Diffusion
- **Customizable Prompts** - Create completely personalized designs with your own prompts

## 🛠️ Technical Highlights

This project utilizes cutting-edge AI technology:
- **ControlNet** - Maintains the structure of your original image while transforming the style
- **NormalBae** - Creates natural transformations through 3D structure recognition
- **Stable Diffusion** - The core of high-quality image generation

## 💡 How to Use

1. **Upload a Photo** - Select the space you want to transform
2. **Choose a Style** - Select from Modern, Classic, or Global design styles
3. **Add a Description** - Like "A cozy bedroom with mountain view" to refine your results
4. **Click Generate** - Let the AI work its magic! 🪄✨

## 🔮 Make Your Dream Space a Reality!

What space are you dreaming of? A minimalist Nordic living room? A glamorous Hollywood-style bedroom? Or perhaps a warm Bohemian kitchen? Now you can visualize all your interior design dreams with the help of AI!

## 🚀 Start Now!
ginigen/interior-design

danielhanchen

posted an update 3 days ago

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4126

🦥 Introducing Unsloth Dynamic v2.0 GGUFs!
Our v2.0 quants set new benchmarks on 5-shot MMLU and KL Divergence, meaning you can now run & fine-tune quantized LLMs while preserving as much accuracy as possible.

Llama 4: unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
DeepSeek-R1: unsloth/DeepSeek-R1-GGUF-UD
Gemma 3: unsloth/gemma-3-27b-it-GGUF

We made selective layer quantization much smarter. Instead of modifying only a subset of layers, we now dynamically quantize all layers so every layer has a different bit. Now, our dynamic method can be applied to all LLM architectures, not just MoE's.

Blog with Details: https://docs.unsloth.ai/basics/dynamic-v2.0

All our future GGUF uploads will leverage Dynamic 2.0 and our hand curated 300K–1.5M token calibration dataset to improve conversational chat performance.

For accurate benchmarking, we built an evaluation framework to match the reported 5-shot MMLU scores of Llama 4 and Gemma 3. This allowed apples-to-apples comparisons between full-precision vs. Dynamic v2.0, QAT and standard iMatrix quants.

Dynamic v2.0 aims to minimize the performance gap between full-precision models and their quantized counterparts.

Kseniase

posted an update about 22 hours ago

Post

1963

6 Free resources on Reinforcement Learning (RL)

RL now is where the real action is, it's the engine behind autonomous tech, robots, and the next wave of AI that thinks, moves and solves problems on its own. To stay up to date with what’s happening in RL, we offer some fresh materials on it:

1. "Reinforcement Learning from Human Feedback" by Nathan Lambert -> https://rlhfbook.com/
It's a short introduction to RLHF, explaining instruction tuning, reward modeling, alignment methods, synthetic data, evaluation, and more

2. "A Course in Reinforcement Learning (2nd Edition)" by Dimitri P. Bertsekas -> https://www.mit.edu/~dimitrib/RLbook.html
Explains dynamic programming (DP) and RL, diving into rollout algorithms, neural networks, policy learning, etc. It’s packed with solved exercises and real-world examples

3. "Mathematical Foundations of Reinforcement Learning" video course by Shiyu Zhao -> https://www.youtube.com/playlist?list=PLEhdbSEZZbDaFWPX4gehhwB9vJZJ1DNm8
Offers a mathematical yet friendly introduction to RL, covering Bellman Equation, value iteration, Monte Carlo learning, approximation, policy gradient, actor-critic methods, etc.
+ Check out the repo for more: https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning

4. "Multi-Agent Reinforcement Learning" by Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer -> https://www.marl-book.com/
Covers models, core ideas of multi-agent RL (MARL) and modern approaches to combining it with deep learning

5. "Reinforcement Learning: A Comprehensive Overview" by Kevin P. Murphy -> https://arxiv.org/pdf/2412.05265
Explains RL and sequential decision making, covering value-based, policy-gradient, model-based, multi-agent RL methods, RL+LLMs, and RL+inference and other topics

6. Our collection of free courses and books on RL -> https://huggingface.co/posts/Kseniase/884818121094439

If you liked this, also subscribe to The Turing Post: https://www.turingpost.com/subscribe

DawnC

posted an update 1 day ago

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2485

I'm excited to introduce VisionScout —an interactive vision tool that makes computer vision both accessible and powerful! 👀🔍

What can VisionScout do right now?
🖼️ Upload any image and detect 80 different object types using YOLOv8.
🔄 Instantly switch between Nano, Medium, and XLarge models depending on your speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
📊 View detailed statistics about detected objects, confidence levels, and spatial distribution.
🎨 Enjoy a clean, intuitive interface with responsive design and enhanced visualizations.

What's next?
I'm working on exciting updates:
- Support for more models
- Video processing and object tracking across frames
- Faster real-time detection
- Improved mobile responsiveness

The goal is to build a complete but user-friendly vision toolkit for both beginners and advanced users.

Try it yourself! 🚀
DawnC/VisionScout

I'd love to hear your feedback , what features would you find most useful? Any specific use cases you'd love to see supported?

Give it a try and let me know your thoughts in the comments! Stay tuned for future updates.

#ComputerVision #ObjectDetection #YOLO #MachineLearning #TechForLife

MonsterMMORPG

posted an update 1 day ago

Post

2447

ComfyUI 1-click Installers updated for latest official Torch 2.7 with CUDA 12.8 (RTX 5000 series support + older GPUs) - Automatically installs xFormers, Flash Attention, Sage Attention, Triton, DeepSpeed, insightface, accelerate, onnxruntime-gpu for Windows

1-click Installers zip file is here : https://www.patreon.com/posts/105023709

xFormers compiled by me for Windows (Python 3.10, 3.11 and 3.12) and Linux (Python 3.10 only)

Flash Attention compiled by me for Windows (Python 3.10, 3.11 and 3.12) and Linux (Python 3.10 only)

Sage Attention compiled by me for Linux (Python 3.10 only)

insightface compiled by me for Windows (Python 3.10, 3.11 and 3.12)

Pre-compiled Triton + Sage Attention + DeepSpeed installed for Windows

You must have pre-installed Python on your system manually

Tutorial for Python + CUDA 12.8 installation : https://youtu.be/DrhUHnYfwC0

merterbak

posted an update 3 days ago

Post

3363

FlowReasoner is a new system that builds a custom set of small AI agents for every user question. Unlike search based methods it uses reasoning driven optimization with external execution feedback.

✅ First, it distills reasoning data using DeepSeek R1-671B to build multi agent systems. 🤖
✅ Then, reasoning data used for DeepSeek-R1-Distill-Qwen-7B via supervised fine tuning for basic reasoning skills. 💡
✅ Finally, RL with GRPO (optimizes by comparing response groups from queries/tasks) to improve reasoning.

FlowReasoner: Reinforcing Query-Level Meta-Agents (2504.15257)
Code: https://github.com/sail-sg/flowreasoner

julien-c

posted an update 3 days ago

Post

3431

BOOOOM: Today I'm dropping TINY AGENTS

the 50 lines of code Agent in Javascript 🔥

I spent the last few weeks working on this, so I hope you will like it.

I've been diving into MCP (Model Context Protocol) to understand what the hype was all about.

It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs.

But while doing that, came my second realization:

Once you have a MCP Client, an Agent is literally just a while loop on top of it. 🤯

➡️ read it exclusively on the official HF blog: https://huggingface.co/blog/tiny-agents

1 reply

ProCreations

posted an update 1 day ago

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1870

Post of the Day

I’m fine-tuning Qwen 2.5-0.5B to be extremely good at math, using high-quality datasets and some smart training strategies.
The logs are looking really promising so far!

Expected release:
Tomorrow morning?
I’ll post as soon as it’s ready — stay tuned.

If you want faster updates or just wanna chat about it, come join my Discord:
https://discord.gg/EXsug2Ux29
(Heads up: we might ask a couple quick questions when you join — just making sure we keep the server safe.)

Also, check out one of the datasets we’re using:
ProCreations/SimpleMath

This project is also helping shape the future of IntellIte.
The insights and techniques we’re developing here — better dataset curation, fine-tuning tricks, and evaluation methods — will directly contribute to making IntellIte even sharper, faster, and more reliable, especially for math and reasoning tasks.

Big progress ahead. Can’t wait to share it with you all!

1 reply

ImranzamanML

posted an update 1 day ago

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1839

🚀 New paper out: "Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function"
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function (2410.03979)

In this work, we tackle some major challenges in Arabic multi-label emotion classification especially the issues of class imbalance and label correlation that often hurt model performance, particularly for minority emotions.

Our approach:

Stacked contextual embeddings from fine-tuned ArabicBERT, MarBERT, and AraBERT models.

A meta-learning strategy that builds richer representations.

A hybrid loss function combining class weighting, label correlation matrices, and contrastive learning to better handle class imbalances.

🧠 Model pipeline: stacked embeddings → meta-learner → Bi-LSTM → fully connected network → multi-label classification.

🔍 Extensive experiments show significant improvements across Precision, Recall, F1-Score, Jaccard Accuracy, and Hamming Loss.
🌟 The hybrid loss function in particular helped close the gap between majority and minority classes!

We also performed ablation studies to break down each component’s contribution and the results consistently validated our design choices.

This framework isn't just for Arabic it offers a generalizable path for improving multi-label emotion classification in other low-resource languages and domains.

Big thanks to my co-authors: Muhammad Azeem Aslam, Wang Jun, Nisar Ahmed, Li Yanan, Hu Hongfei, Wang Shiyu, and Xin Liu!

Would love to hear your thoughts on this work! 👇

nicolay-r

posted an update 2 days ago

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2177

🚀 Delighted to share a major milestone in adapting reasoning techniques for data collections augmentation!
Introducing bulk-chain 1.0.0 -- the first major release of a no-string API for adapting your LLM for Chain-of-Thought alike reasoning over records with large amount of parameters across large datasets.

⭐ Check it out: https://github.com/nicolay-r/bulk-chain

What’s new and why it matters:
📦 Fully no-string API for easy client deployment
🔥 Demos are now standalone projects:

Demos:
📺 bash / shell (dispatched): https://github.com/nicolay-r/bulk-chain-shell
📺 tksheet: https://github.com/nicolay-r/bulk-chain-tksheet-client

Using nlp-thirdgate to host the supported providers:
🌌 LLM providers: https://github.com/nicolay-r/nlp-thirdgate

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