John6666 (John Smith)

reacted to AlexBodner's post with 👀 about 2 hours ago

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🚀🤖𝐃𝐨 𝐀𝐧𝐝𝐫𝐨𝐢𝐝𝐬 𝐃𝐫𝐞𝐚𝐦 𝐨𝐟 𝐄𝐥𝐞𝐜𝐭𝐫𝐢𝐜 𝐌𝐚𝐫𝐢𝐨𝐬?

Discover how we replaced the classic game engine with DIAMOND, a Neural Network that predicts every frame based on actions, noise, and past states. From training on human and RL gameplay to generating surreal hallucinations, this project shows the potential of diffusion models in creating amazing simulations. 🎮

🧵 Dive into the full story in our Twitter thread:
👉 https://x.com/AlexBodner_/status/1871566560512643567
🌟 Don’t forget to follow and leave a star for more groundbreaking AI projects!

reacted to hba123's post with 🚀 about 2 hours ago

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Blindly applying algorithms without understanding the math behind them is not a good idea frmpv. So, I am on a quest to fix this!

I wrote my first hugging face article on how you would derive closed-form solutions for KL-regularised reinforcement learning problems - what is used for DPO.

Check it out: https://huggingface.co/blog/hba123/derivingdpo

reacted to DawnC's post with ❤️ about 6 hours ago

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🌟 PawMatchAI: Making Breed Selection More Intuitive! 🐕
Excited to share the latest update to this AI-powered companion for finding your perfect furry friend! The breed recommendation system just got a visual upgrade to help you make better decisions.

✨ What's New?
Enhanced breed recognition accuracy through strategic model improvements:
- Upgraded to a fine-tuned ConvNeXt architecture for superior feature extraction
- Implemented progressive layer unfreezing during training
- Optimized data augmentation pipeline for better generalization
- Achieved 8% improvement in breed classification accuracy

🎯 Key Features:
- Smart breed recognition powered by AI
- Visual matching scores with intuitive color indicators
- Detailed breed comparisons with interactive tooltips
- Lifestyle-based recommendations tailored to your needs

💭 Project Vision
Combining my passion for AI and pets, this project represents another step toward my goal of creating meaningful AI applications. Each update aims to make the breed selection process more accessible while improving the underlying technology.

👉 Try it now: DawnC/PawMatchAI

Your likes ❤️ on this space fuel this project's growth!

#AI #MachineLearning #DeepLearning #Pytorch #ComputerVision
See translation

reacted to ginipick's post with 🚀🔥 about 6 hours ago

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🎨 GiniGen Canvas-o3: Intelligent AI-Powered Image Editing Platform
Transform your images with precision using our next-generation tool that lets you extract anything from text to objects with simple natural language commands! 🚀
📌 Key Differentiators:

Intelligent Object Recognition & Extraction
• Freedom to select any target (text, logos, objects)
• Simple extraction via natural language commands ("dog", "signboard", "text")
• Ultra-precise segmentation powered by GroundingDINO + SAM
Advanced Background Processing
• AI-generated custom backgrounds for extracted objects
• Intuitive object size/position adjustment
• Multiple aspect ratio support (1:1, 16:9, 9:16, 4:3)
Progressive Text Integration
• Dual text placement: over or behind images
• Multi-language font support
• Real-time font style/size/color/opacity adjustment

🎯 Use Cases:

Extract logos from product images
Isolate text from signboards
Select specific objects from scenes
Combine extracted objects with new backgrounds
Layer text in front of or behind images

💫 Technical Features:

Natural language-based object detection
Real-time image processing
GPU acceleration & memory optimization
User-friendly interface

🎉 Key Benefits:

User Simplicity: Natural language commands for object extraction
High Precision: AI-powered accurate object recognition
Versatility: From basic editing to advanced content creation
Real-Time Processing: Instant result visualization

Experience the new paradigm of image editing with GiniGen Canvas-o3:

Seamless integration of multiple editing functions
Professional-grade results with consumer-grade ease
Perfect for social media, e-commerce, and design professionals

Whether you're extracting text from complex backgrounds or creating sophisticated visual content, GiniGen Canvas-o3 provides the precision and flexibility you need for modern image editing!

GO! ginigen/CANVAS-o3

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reacted to sayakpaul's post with 🚀🔥 about 12 hours ago

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Commits speak louder than words 🤪

* 4 new video models
* Multiple image models, including SANA & Flux Control
* New quantizers -> GGUF & TorchAO
* New training scripts

Enjoy this holiday-special Diffusers release 🤗
Notes: https://github.com/huggingface/diffusers/releases/tag/v0.32.0

reacted to singhsidhukuldeep's post with 🔥 about 12 hours ago

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Exciting News in AI: JinaAI Releases JINA-CLIP-v2!

The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal:

🚀 Technical Highlights:
- Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder
- Supports 89 languages with 8,192 token context length
- Processes images up to 512×512 pixels with 14×14 patch size
- Implements FlashAttention2 for text and xFormers for vision processing
- Uses Matryoshka Representation Learning for efficient vector storage

⚡️ Under The Hood:
- Multi-stage training process with progressive resolution scaling (224→384→512)
- Contrastive learning using InfoNCE loss in both directions
- Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs
- Incorporates specialized datasets for document understanding, scientific graphs, and infographics
- Uses hard negative mining with 7 negatives per positive sample

📊 Performance:
- Outperforms previous models on visual document retrieval (52.65% nDCG@5)
- Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark
- Strong multilingual performance across 30 languages
- Maintains performance even with 75% dimension reduction (256D vs 1024D)

🎯 Key Innovation:
The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems!

Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!

reacted to Kseniase's post with 👍 about 16 hours ago

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**15 Agentic Systems and Frameworks of 2024**

This year, we started our “AI Agents and Agentic Workflows” series (https://www.turingpost.com/t/AI-Agents) to explore everything about AI agents step by step: all the vocabulary, how they work, and how to build them.
The huge interest in this series and the large number of studies conducted on agents showed that it was one of the most popular and important themes of the year. In 2025, most likely, agents will reach new highs – we will be covering that for you. Now, let’s review the agentic systems that have emerged this year.

Here is a list of 15 agentic systems and frameworks of 2024:

1. GUI Agents: A Survey (2412.13501)

2. Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level (2411.03562)

3. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)

4. MALT: Improving Reasoning with Multi-Agent LLM Training (2412.01928)

5. Agent S: An Open Agentic Framework that Uses Computers Like a Human (2410.08164)

6. Automated Design of Agentic Systems (2408.08435)

7. AgentInstruct: Toward Generative Teaching with Agentic Flows (2407.03502)

8. AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2410.18603)

9. WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents (2410.07484)

10. Generative Agent Simulations of 1,000 People (2411.10109)

11. DynaSaur: Large Language Agents Beyond Predefined Actions (2411.01747)

12. PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking (2410.12375)

13. Generative World Explorer (2411.11844)

14. Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2412.14684)

15. AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions (2410.20424)

Thanks for reading Turing Post!
Subscribe to receive new posts straight into your inbox -> https://www.turingpost.com/subscribe

reacted to DualityAI-RebekahBogdanoff's post with 👀 about 16 hours ago

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Hi again! 👋 Duality.ai just launched a lesson on how to bring your own twin into our free FalconEditor software🖥️, how to create a synthetic dataset using your twin📸, and how to test your model with your own images🎯!
https://falcon.duality.ai/secure/documentation/ex2-adv-find-twin?sidebarMode=learn

This is crucial for anyone wanting to use FalconEditor for their own projects. We will also be hosting a free photogrammetry course that uses a free workflow, independent of OS specifications, to create robust digital twins. These 2 lessons complement each other incredibly well. Sign up for the course here!
https://docs.google.com/forms/d/e/1FAIpQLSd2WsKaa1CjRM89uv3LNkZXj1TUNWrNxDrtyWny2w1OQDHn8g/viewform

reacted to nyuuzyou's post with 👍 about 16 hours ago

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🎮 GoodGame.ru Clips Dataset - nyuuzyou/goodgame

A collection of 39,280 video clips metadata from GoodGame.ru streaming platform featuring:

- Complete clip information including direct video URLs and thumbnails
- Streamer details like usernames and avatars
- Engagement metrics such as view counts
- Game categories and content classifications
- Released under Creative Commons Zero (CC0) license

This extensive clips collection provides a valuable resource for developing and evaluating video-based AI applications, especially in Russian gaming and streaming contexts.

replied to nroggendorff's post 1 day ago

Maybe you're the type who's always working your brain, so I think it would be good to rest your brain by watching something moderately interesting. If you're not doing anything, you'll end up thinking about things and that will tire you out even more.🥶

replied to nroggendorff's post 1 day ago

The Discord thing?

reacted to nroggendorff's post with 😔 1 day ago

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im so tired

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reacted to randomhex10101's post with 👀 1 day ago

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Does anyone have any suggestions when it comes to multi-user managment for dedicated custom endpoints? Would it be possible to utilize Webhooks with HF in order to automate and delegate custom dedicated endpoint creating for each user who signs up for my service? Or would this be implausible / too complex over something like simply allocating containers? Or is there a better way to go about this that maybe I just haven't found yet from the docs? Any help will mean a lot, thank you!

reacted to wenhuach's post with 👍 1 day ago

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Are we the only providers of INT4 quantized models for Llama 3.2 VL?
OPEA/Llama-3.2-90B-Vision-Instruct-int4-sym-inc
OPEA/Llama-3.2-11B-Vision-Instruct-int4-sym-inc

reacted to singhsidhukuldeep's post with 👀 1 day ago

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Fascinating insights from @Pinterest 's latest research on improving feature interactions in recommendation systems!

Pinterest's engineering team has tackled a critical challenge in their Homefeed ranking system that serves 500M+ monthly active users. Here's what makes their approach remarkable:

>> Technical Deep Dive

Architecture Overview
• The ranking model combines dense features, sparse features, and embedding features to represent users, Pins, and context
• Sparse features are processed using learnable embeddings with size based on feature cardinality
• User sequence embeddings are generated using a transformer architecture processing past engagements

Feature Processing Pipeline
• Dense features undergo normalization for numerical stability
• Sparse and embedding features receive L2 normalization
• All features are concatenated into a single feature embedding

Key Innovations
• Implemented parallel MaskNet layers with 3 blocks
• Used projection ratio of 2.0 and output dimension of 512
• Stacked 4 DCNv2 layers on top for higher-order interactions

Performance Improvements
• Achieved +1.42% increase in Homefeed Save Volume
• Boosted Overall Time Spent by +0.39%
• Maintained memory consumption increase to just 5%

>> Industry Constraints Addressed

Memory Management
• Optimized for 60% GPU memory utilization
• Prevented OOM errors while maintaining batch size efficiency

Latency Optimization
• Removed input-output concatenation before MLP
• Reduced hidden layer sizes in MLP
• Achieved zero latency increase while improving performance

System Stability
• Ensured reproducible results across retraining
• Maintained model stability across different data distributions
• Successfully deployed in production environment

This work brilliantly demonstrates how to balance academic innovations with real-world industrial constraints. Kudos to the Pinterest team!

reacted to nicolay-r's post with 👀 1 day ago

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📢 So far I noticed that 🧠 reasoning with llm 🤖 in English is tend to be more accurate than in other languages.
However, besides the GoogleTrans and other open transparent translators, I could not find one that could be easy to use solutions to avoid:
1.🔴 Third-party framework installation
2.🔴 Text chunking
3.🔴 support of meta-annotation like spans / objects / etc.

💎 To cope problem of IR from non-english texts, I am happy to share the bulk-translate 0.25.0. 🎊

⭐ https://github.com/nicolay-r/bulk-translate

bulk-translate is a tiny Python 🐍 no-string framework that allows translate series of texts with the pre-annotated fixed-spans that are invariant for translator.

It supports 👨‍💻 API for quick data translation with (optionaly) annotated objects in texts (see figure below) in Python 🐍
I make it accessible as much as possible for RAG and / or LLM-powered app downstreams:
📘 https://github.com/nicolay-r/bulk-translate/wiki

All you have to do is to provide iterator of texts, where each text:
1. ✅ String object
2. ✅ List of strings and nested lists that represent spans (value + any ID data).

🤖 By default I provide a wrapper over googletrans which you can override with your own 🔥
https://github.com/nicolay-r/bulk-translate/blob/master/models/googletrans_310a.py

reacted to ehristoforu's post with 🤗 2 days ago

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✒️ Ultraset - all-in-one dataset for SFT training in Alpaca format.
fluently-sets/ultraset

❓ Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.

🤯 Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.

🤗 For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.

❇️ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.

reacted to luigi12345's post with 👀 2 days ago

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PERFECT FINAL PROMPT for Coding and Debugging.

Step 1: Generate the prompt that if sent to you will make you adjust the script so it meets each and every of the criteria it needs to meet to be 100% bug free and perfect.

Step 2: adjust the script following the steps and instructions in the prompt created in Step 1.

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