prithivMLmods (Prithiv Sakthi)

posted an update about 17 hours 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:

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

⚛️ 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/

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

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

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

reacted to nicolay-r's post with 🚀 about 17 hours 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 lysandre's post with 🔥❤️❤️ 1 day ago

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SmolVLM-2 and SigLIP-2 are now part of transformers in dedicated releases!

They're added on top of the v4.49.0 release, and can be installed from the following tags: v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.

This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).

Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.

Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.

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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 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 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 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 davanstrien's post with 🧠 3 days ago

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Hacked together a way to log trl GRPO training completions to a 🤗 dataset repo. This allows you to:

- Track rewards from multiple reward functions
- Treat the completion and rewards from training as a "proper" dataset and do EDA
- Share results for open science

The implementation is super hacky, but I'm curious if people would find this useful.

To push completions to the Hub, you just need two extra parameters:

log_completions=True
log_completions_hub_repo='your-username/repo-name'

Example dataset: davanstrien/test-logs
Colab: https://colab.research.google.com/drive/1wzBFPVthRYYTp-mEYlznLg_e_0Za1M3g

reacted to merve's post with 🧠🧠 3 days ago

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Google just released PaliGemma 2 Mix: new versatile instruction vision language models 🔥

> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯

Read more https://huggingface.co/blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4

reacted to burtenshaw's post with 🚀 4 days ago

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AGENTS + FINETUNING! This week Hugging Face learn has a whole pathway on finetuning for agentic applications. You can follow these two courses to get knowledge on levelling up your agent game beyond prompts:

1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction

Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.

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reacted to AdinaY's post with ❤️ 4 days ago

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🚀 StepFun阶跃星辰 is making BIG open moves!

Last year, their GOT-OCR 2.0 took the community by storm 🔥but many didn’t know they were also building some amazing models. Now, they’ve just dropped something huge on the hub!

📺 Step-Video-T2V: a 30B bilingual open video model that generates 204 frames (8-10s) at 540P resolution with high information density & consistency.
stepfun-ai/stepvideo-t2v

🔊 Step-Audio-TTS-3B : a TTS trained with the LLM-Chat paradigm on a large synthetic dataset, capable of generating RAP & Humming
stepfun-ai/step-audio-67b33accf45735bb21131b0b

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posted an update 5 days ago

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Dino: The Minimalist Multipurpose Chat System 🌠
Agent-Dino : prithivMLmods/Agent-Dino
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino

By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
@image: Generates an image using Stable Diffusion xL.
@3d: Generates a 3D mesh.
@web: Web search agents.
@rAgent: Initiates a reasoning chain using Llama mode for coding explanations.
@tts1-♀, @tts2-♂: Voice generation (Female and Male voices).
@yolo : Object Detection

reacted to ZennyKenny's post with 🤗 5 days ago

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Really excited to start contributing to the SWE Arena project: https://swe-arena.com/

Led by IBM PhD fellow @terryyz , our goal is to advance research in code generation and app development by frontier LLMs.

reacted to sayakpaul's post with 🔥 5 days ago

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Inference-time scaling meets Flux.1-Dev (and others) 🔥

Presenting a simple re-implementation of "Inference-time scaling diffusion models beyond denoising steps" by Ma et al.

I did the simplest random search strategy, but results can potentially be improved with better-guided search methods.

Supports Gemini 2 Flash & Qwen2.5 as verifiers for "LLMGrading" 🤗

The steps are simple:

For each round:

1> Starting by sampling 2 starting noises with different seeds.
2> Score the generations w.r.t a metric.
3> Obtain the best generation from the current round.

If you have more compute budget, go to the next search round. Scale the noise pool (2 ** search_round) and repeat 1 - 3.

This constitutes the random search method as done in the paper by Google DeepMind.

Code, more results, and a bunch of other stuff are in the repository. Check it out here: https://github.com/sayakpaul/tt-scale-flux/ 🤗

posted an update 6 days ago

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The last week of Impression Craft Arts and sketches from strangerzonehf🎨🧑🏻‍🎨

- Collection : strangerzonehf/Flux-Ultimate-LoRA-Collection

Adapters:
+ Ld-Art : strangerzonehf/Ld-Art
+ Animeopix-Flux : strangerzonehf/Animeopix-Flux
+ Flux-Super-Paint-LoRA : strangerzonehf/Flux-Super-Paint-LoRA
+ CinematicShot-Pics-Flux : strangerzonehf/cinematicShot-Pics-Flux
+ Oil-Wall-Art-Flux : strangerzonehf/Oil-Wall-Art-Flux
+ Pixelo-Flux : strangerzonehf/Pixelo-Flux
+ Abstract-Shattered : strangerzonehf/Abstract-Shattered
+ Neon-Impressionism-Flux : strangerzonehf/Neon-Impressionism-Flux
+ NewG-Art : strangerzonehf/NewG-Art

🪧Demo : prithivMLmods/FLUX-LoRA-DLC
🤗Page : https://huggingface.co/strangerzonehf

reacted to louisbrulenaudet's post with 🤗 7 days ago

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I am pleased to introduce my first project built upon Hugging Face’s smolagents framework, integrated with Alpaca for financial market analysis automation 🦙🤗

The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.

AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆

Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool

reacted to davanstrien's post with 🔥 7 days ago

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Dataset descriptions for trending Hugging Face datasets? Powered by a Smol model davanstrien/Smol-Hub-tldr

Prithiv Sakthi

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