John6666 (John Smith)

reacted to Kseniase's post with 👍 about 6 hours ago

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8 Free Sources about AI Agents:

Agents seem to be everywhere and this collection is for a deep dive into the theory and practice:

1. "Agents" Google's whitepaper by Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic -> https://www.kaggle.com/whitepaper-agents
Covers agents, their functions, tool use and how they differ from models

2. "Agents in the Long Game of AI. Computational Cognitive Modeling for Trustworthy, Hybrid AI" book by Marjorie McShane, Sergei Nirenburg, and Jesse English -> https://direct.mit.edu/books/oa-monograph/5833/Agents-in-the-Long-Game-of-AIComputational
Explores building AI agents, using Hybrid AI, that combines ML with knowledge-based reasoning

3. "AI Engineer Summit 2025: Agent Engineering" 8-hour video -> https://www.youtube.com/watch?v=D7BzTxVVMuw
Experts' talks that share insights on the freshest Agent Engineering advancements, such as Google Deep Research, scaling tips and more

4. AI Agents Course from Hugging Face -> https://huggingface.co/learn/agents-course/en/unit0/introduction
Agents' theory and practice to learn how to build them using top libraries and tools

5. "Artificial Intelligence: Foundations of Computational Agents", 3rd Edition, book by David L. Poole and Alan K. Mackworth -> https://artint.info/3e/html/ArtInt3e.html
Agents' architectures, how they learn, reason, plan and act with certainty and uncertainty

6. "Intelligent Agents: Theory and Practice" book by Michael Wooldridge -> https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/ker95/ker95-html.html
A fascinating option to dive into how agents were seen in 1995 and explore their theory, architectures and agent languages

7. The Turing Post articles "AI Agents and Agentic Workflows" on Hugging Face -> https://huggingface.co/Kseniase
We explore agentic workflows in detail and agents' building blocks, such as memory and knowledge

8. Our collection "8 Free Sources to Master Building AI Agents" -> https://www.turingpost.com/p/building-ai-agents-sources

reacted to vincentg64's post with 👀 about 16 hours ago

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Spectacular Connection Between LLMs, Quantum Systems, and Number Theory | https://mltblog.com/3DgambA

In my recent paper 51 on cracking the deepest mathematical mystery, available at https://mltblog.com/3zsnQ2g, I paved the way to solve a famous multi-century old math conjecture. The question is whether or not the digits of numbers such as π are evenly distributed. Currently, no one knows if the proportion of '1' even exists in these binary digit expansions. It could oscillate forever without ever converging. Of course, mathematicians believe that it is 50% in all cases. Trillions of digits have been computed for various constants, and they pass all randomness tests. In this article, I offer a new framework to solve this mystery once for all, for the number e.

Rather than a closure on this topic, it is a starting point opening new research directions in several fields. Applications include cryptography, dynamical systems, quantum dynamics, high performance computing, LLMs to answer difficult math questions, and more. The highly innovative approach involves iterated self-convolutions of strings and working with numbers as large as 2^n + 1 at power 2^n, with n larger than 100,000. No one before has ever analyzed the digits of such titanic numbers!

To read the full article, participate in the AI & LLM challenge, get the very fast Python code, read about ground-breaking research, and see all the applications, visit https://mltblog.com/3DgambA

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

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The past few years have been a blast for artificial intelligence, with large language models (LLMs) stunning everyone with their capabilities and powering everything from chatbots to code assistants. However, not all applications demand the massive size and complexity of LLMs, the computational power required makes them impractical for many use cases. This is why Small Language Models (SLMs) entered the scene to make powerful AI models more accessible by shrinking in size.

In this article we went through what SLMs are, how they are made small, their benefits and limitations, real-world use cases, and how they can be used on mobile and desktop devices.
https://huggingface.co/blog/jjokah/small-language-model

reacted to KnutJaegersberg's post with 👀 about 16 hours ago

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Designing Ecosystems of Intelligence from First Principles

(new major Friston paper)

https://arxiv.org/abs/2212.01354

reacted to stas's post with 👀 1 day ago

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Do you want ArcticTraining at @SnowflakeDB to add an ability to post-train DeepSeek V3/R1 models with DPO using just a few GPU nodes?

Please vote here and tell others about it: https://github.com/snowflakedb/ArcticTraining/discussions/58

ArcticTraining is an open-source, easy to use post-training framework for NVIDIA GPUs built on top of DeepSpeed.

reacted to mmhamdy's post with 🔥 1 day ago

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🎉 We're excited to introduce MemoryCode, a novel synthetic dataset designed to rigorously evaluate LLMs' ability to track and execute coding instructions across multiple sessions. MemoryCode simulates realistic workplace scenarios where a mentee (the LLM) receives coding instructions from a mentor amidst a stream of both relevant and irrelevant information.

💡 But what makes MemoryCode unique?! The combination of the following:

✅ Multi-Session Dialogue Histories: MemoryCode consists of chronological sequences of dialogues between a mentor and a mentee, mirroring real-world interactions between coworkers.

✅ Interspersed Irrelevant Information: Critical instructions are deliberately interspersed with unrelated content, replicating the information overload common in office environments.

✅ Instruction Updates: Coding rules and conventions can be updated multiple times throughout the dialogue history, requiring LLMs to track and apply the most recent information.

✅ Prospective Memory: Unlike previous datasets that cue information retrieval, MemoryCode requires LLMs to spontaneously recall and apply relevant instructions without explicit prompts.

✅ Practical Task Execution: LLMs are evaluated on their ability to use the retrieved information to perform practical coding tasks, bridging the gap between information recall and real-world application.

📌 Our Findings

1️⃣ While even small models can handle isolated coding instructions, the performance of top-tier models like GPT-4o dramatically deteriorates when instructions are spread across multiple sessions.

2️⃣ This performance drop isn't simply due to the length of the context. Our analysis indicates that LLMs struggle to reason compositionally over sequences of instructions and updates. They have difficulty keeping track of which instructions are current and how to apply them.

🔗 Paper: From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (2502.13791)
📦 Code: https://github.com/for-ai/MemoryCode

reacted to fdaudens's post with 👍 1 day ago

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Trying something new to keep you ahead of the curve: The 5 AI stories of the week - a weekly curation of the most important AI news you need to know. Do you like it?

For more AI stories and deeper analysis, check out my newsletter: https://open.substack.com/pub/fdaudens/p/ai-competition-heats-up-grok-3-iphone

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

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📢 If you're looking for translating massive dataset of JSON-lines / CSV data with various set of source fields, then the following update would be relevant. So far and experimenting with adapting language specific Sentiment Analysis model, got a change to reforge and relaese bulk-translate 0.25.2.
⭐️ https://github.com/nicolay-r/bulk-translate/releases/tag/0.25.2

The update has the following major features
- Supporting schemas: all the columns to be translated are now could be declared within the same prompt-style format. using json this automatically allows to map them onto output fields
- The related updates for shell execution mode: schema parameter is now available alongside with just a prompt usage before.

Benefit is that your output is invariant. You can extend and stack various translators with separated shell laucnhes.

Screenshot below is the application of the google-translate engine in manual batching mode.
🚀 Performance: 2.5 it / sec (in the case of a single field translation)

🌟 about bulk-translate: https://github.com/nicolay-r/bulk-translate
🌌 nlp-thirdgate: https://github.com/nicolay-r/nlp-thirdgate?tab=readme-ov-file

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reacted to prithivMLmods's post with 🚀 1 day ago

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It's really interesting about the deployment of a new state of matter in Majorana 1: the world’s first quantum processor powered by topological qubits. If you missed this news this week, here are some links for you:

🅱️Topological qubit arrays: https://arxiv.org/pdf/2502.12252

⚛️ Quantum Blog: https://azure.microsoft.com/en-us/blog/quantum/2025/02/19/microsoft-unveils-majorana-1-the-worlds-first-quantum-processor-powered-by-topological-qubits/

📖 Read the story: https://news.microsoft.com/source/features/innovation/microsofts-majorana-1-chip-carves-new-path-for-quantum-computing/

📝 Majorana 1 Intro: https://youtu.be/Q4xCR20Dh1E?si=Z51DbEYnZFp_88Xp

🌀The Path to a Million Qubits: https://youtu.be/wSHmygPQukQ?si=TS80EhI62oWiMSHK

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reacted to MonsterMMORPG's post with 🚀 2 days ago

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IDM VTON : Virtual Try On APP Automatic Installers for Windows, RunPod, Massed Compute and a free Kaggle Account notebook Published - Can transfer objects too

Installers & APP
1-Click installers for Windows, RunPod, Massed Compute and a free Kaggle account notebook in below link:

https://www.patreon.com/posts/122718239

Features

Seamlessly install on Windows, RunPod, Massed Compute and on Kaggle with just 1-click into a Python 3.10 VENV

Our APP has so many extra features

Can perfectly handle any resolution and aspect ratio images

You can perfectly manually mask via latest version of Gradio and properly working image editor

Supports 4-bit, 8-bit quantization + CPU offloading for lower VRAM GPUs
All generated images are also automatically saved

You can also generate more than 1 image like 10 images as batch generation with order

Official repo : https://idm-vton.github.io/

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reacted to DmitryRyumin's post with 🔥 2 days ago

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🚀🎭🌟 New Research Alert - WACV 2025 (Avatars Collection)! 🌟🎭🚀
📄 Title: EmoVOCA: Speech-Driven Emotional 3D Talking Heads 🔝

📝 Description: EmoVOCA is a data-driven method for generating emotional 3D talking heads by combining speech-driven lip movements with expressive facial dynamics. This method has been developed to overcome the limitations of corpora and to achieve state-of-the-art animation quality.

👥 Authors: @FedeNoce , Claudio Ferrari, and Stefano Berretti

📅 Conference: WACV, 28 Feb – 4 Mar, 2025 | Arizona, USA 🇺🇸

📄 Paper: https://arxiv.org/abs/2403.12886

🌐 Github Page: https://fedenoce.github.io/emovoca/
📁 Repository: https://github.com/miccunifi/EmoVOCA

🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers

🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers

🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #EmoVOCA #3DAnimation #TalkingHeads #SpeechDriven #FacialExpressions #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #WACV2024

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reacted to caelancooper's post with 👀 2 days ago

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Hey Huggingface Community,

I'm just starting my journey. I'm here to learn and contribute as much as I can to the AI community. What happened with one of my models was I left the security permissions open for people to commit changes and contribute to the model in good faith and the opposite happened.

I'm open to all feedback you may have on my future projects. Let's keep it collegial and try to make something amazing. I always stride to make situations a win for all parties involved and would love to collaborate with anybody who's interested in innovation, optimization and new use cases for AI.

Thanks Everyone,
Caelan

reacted to jsulz's post with 🚀 2 days ago

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Time flies!

Six months after joining Hugging Face the Xet team is kicking off the first migrations from LFS to our storage for a number of repositories on the Hub.

More on the nitty gritty details behind the migration soon, but here are the big takeaways:

🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release

✅ No action on your part needed - you can work with a Xet-backed repo like any other repo on the Hub (for now - major improvements on their way!)

👀 Keep an eye out for the Xet logo to see if a repo you know is on our infra! See the screenshots below to spot the difference 👇

⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.

🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!

This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow

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reacted to onekq's post with 👀 2 days ago

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Still waiting for 👽Grok👽 3 API ⌛😞😫

reacted to tegridydev's post with 🔥🤗❤️ 2 days ago

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Open Source AI Agents | Github/Repo List | [2025]

https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory

Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)

reacted to JingzeShi's post with 🚀 2 days ago

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🤗Welcome to the Doge Edge Device Small language Model.

SmallDoge/Doge-160M-Instruct

reacted to THUdyh's post with 👀 2 days ago

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🔥🔥Introducing Ola! State-of-the-art omni-modal understanding model with advanced progressive modality alignment strategy!
Ola ranks #1 on OpenCompass Leaderboard (<10B)
.
📜Paper: https://arxiv.org/abs/2502.04328
🛠️Code: https://github.com/Ola-Omni/Ola

🛠️We have fully released our video&audio training data, intermediate image&video model at THUdyh/ola-67b8220eb93406ec87aeec37. Try to build your own powerful omni-modal model with our data and models!

reacted to ychen's post with 👍 2 days ago

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Here's some annoying keywords that 4o tends to use when responding to personal experiences with negative sentiments. Will be updated over time.

rough, tough, sound like, sounds like, frustrating, overwhelming

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