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MonsterMMORPG 
posted an update 2 days ago
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3490
https://youtu.be/R6h02YY6gUs

Qwen Image is literally unchallenged at understanding complex prompts and writing amazing text on generated images. This model feels almost as if it’s illegal to be open source and free. It is my new tool for generating thumbnail images. Even with low-effort prompting, the results are excellent.
This tutorial literally shows how these images were generated with Gemini 2.5 Pro made prompts :
Qwen Image Dominates Text-to-Image: 700+ Tests Reveal Why It’s Better Than FLUX — Presets Published
https://youtu.be/R6h02YY6gUs

Gemini 2.5 Pro is freely available on Google Studio AI
All images generated in easy to use SwarmUI and they are unmodified raw generations
SwarmUI and ComfyUI install tutorial :
Master Local AI Art & Video Generation with SwarmUI (ComfyUI Backend): The Ultimate 2025 Tutorial
https://www.youtube.com/watch?v=fTzlQ0tjxj0
ovi054 
posted an update 1 day ago
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1981
WAN 2.2 Text to Image ⚡

ovi054/wan2-2-text-to-image

We all know that WAN 2.2 A14B is a video model. But It turns out this video model can also produce great image results with incredible prompt adherence! The image output is sharp, detailed, and sticks to the prompt better than most.

👉 Try it now: ovi054/wan2-2-text-to-image
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fdaudens 
posted an update 1 day ago
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1889
OpenAI’s GPT-OSS has sparked ~400 new models on Hugging Face and racked up 5M downloads in less than a week, already outpacing DeepSeek R1’s first-week numbers.

For comparison: when R1 launched, I tracked 550 derivatives (across 8 base models) in a week, with ~3M downloads. GPT-OSS is ahead on adoption and engagement.

It’s also the most-liked release of any major LLM this summer. The 20B and 120B versions quickly shot past Kimi K2, GLM 4.5, and others in likes.

Most-downloaded GPT-OSS models include LM Studio and Unsloth AI versions:
1️⃣ openai/gpt-oss-20b - 2.0M
2️⃣ lmstudio-community/gpt-oss-20b-MLX-8bit - 750K
3️⃣ openai/gpt-oss-120b - 430K
4️⃣ unsloth/gpt-oss-20b-GGUF - 380K
5️⃣ lmstudio-community/gpt-oss-20b-GGUF - 330K

The 20B version is clearly finding its audience, showing the power of smaller, faster, more memory- and energy-efficient models. (These numbers don’t include calls to the models via inference providers, so the real usage is likely even bigger, especially for the 120B version)

Open-weight models let anyone build on top. Empower the builders, and innovation takes off. 🚀
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FlameF0X 
posted an update 2 days ago
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3592
I am very sad to say that the budget in creating of SnowflakeCore-G1 1b and 7b MoE models ran out and I can't pre-train them anymore.
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Kseniase 
posted an update 2 days ago
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3001
6 Must-read books about AI and Machine Learning:

Sharing some free, useful resources for you. In this collection, we’ve gathered the most recent books to give you up-to-date information on key fundamental topics. Hope this helps you master AI and machine learning:

1. Machine Learning Systems by Vijay Janapa Reddi → https://www.mlsysbook.ai/
Provides a framework for building effective ML solutions, covering data engineering, optimization, hardware-aware training, inference acceleration, architecture choice, and other key principles

2. Generative Diffusion Modeling: A Practical Handbook by Zihan Ding, Chi Jin → https://arxiv.org/abs/2412.17162
Offers a unified view of diffusion models: probabilistic, score-based, consistency, rectified flow, pre/post-training. It aligns notations with code to close the “paper-to-code” gap.

3. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges → https://arxiv.org/abs/2104.13478
Explores unified geometric principles to analyze neural networks' architectures: CNNs, RNNs, GNNs, Transformers, and guide the design of the future ones

4. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein → https://arxiv.org/abs/2508.02723
Dives into the the key math concepts behind geometric Deep Learning: geometric and analytical structures, vector calculus, differential geometry, etc.

5. Interpretable Machine Learning by Christoph Molnar → https://github.com/christophM/interpretable-ml-book
Practical guide to simple, transparent models (e.g., decision trees) and model-agnostic methods like LIME, Shapley values, permutation importance, and accumulated local effects.

6. Understanding Deep Learning by Simon J.D. Prince → https://udlbook.github.io/udlbook/
Explores core deep learning concenpts: models, training, evaluation, RL, architectures for images, text, and graphs, addressing open theoretical questions

Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
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hba123 
posted an update 1 day ago
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2231
After so many requests, I want to update everyone on the status of Ark, i.e., doing robotics in Python. First, thanks a lot for the amazing and impressive interest we got in it. We are now @ 1.13 K downloads, which is beyond my wildest expectations (https://pepy.tech/projects/ark-robotics?timeRange=threeMonths&category=version&includeCIDownloads=true&granularity=daily&viewType=line&versions=0.1.1%2C0.1%2C0.0.1)!

0. First and foremost, we are pip installable (https://pypi.org/project/ark-robotics/)
1. We are currently working on supporting more robots: G1 and Drones are in the works with a cool set of amazing, fantastic colleagues. Those are coming.
2. We have support for multiple sensors and interfaces (https://github.com/Robotics-Ark/ark_interfaces)
3. We also now have support for machine learning via diffusion policies (https://github.com/Robotics-Ark/ark_diffusion_policies_on_franka)
4. We have a set of tutorials that detail each step (https://arkrobotics.notion.site/ARK-Home-22be053d9c6f8096bcdbefd6276aba61)

You can read the paper here: https://robotics-ark.github.io/ark_robotics.github.io/static/images/2506.21628v2.pdf

Have fun building robotics with Python people! Please star our repo (https://github.com/Robotics-Ark/ark_framework), so we can continue our open-sourcing endeavour!
prithivMLmods 
posted an update 3 days ago
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4158
On the verge of releasing Poseidon-Reasoning-5M, a dataset built to excel in general thought processes, mathematics, and science across a diverse mixture of domains, I’m also dropping the Gargantua-R1-Compact dataset, a collection of over six million high-quality reasoning QA pair traces. 🤗🚀

✦ Gargantua-R1-Compact : prithivMLmods/Gargantua-R1-Compact

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Compact", split="train")

Additionally, I’m adding the mini version of Gargantua — the Gargantua-R1-Wee : prithivMLmods/Gargantua-R1-Wee

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Wee", split="train")

The composition spans 73.93% core mathematical reasoning involving problems, proofs, and computational challenges, 12.11% across diverse scientific domains such as physics, chemistry, biology, and interdisciplinary topics, 11.35% in competitive coding covering algorithms and data structures, 1.37% in academic science focusing on research-level methodology, 0.95% in creative and analytical reasoning through logic puzzles and problem-solving tasks, 0.25% in specialized technical areas like MLOps, LLMs, diffusion models, and CUDA, and 0.06% involving data from graphs and charts converted into structured JSON formats. Designed with both rich contextual depth and formal structural clarity, Gargantua-R1-Compact is an optimal resource for advancing research in symbolic reasoning, interpretability, and high-precision question answering in mathematical domains.

✦ Collection : prithivMLmods/gargantua-r1-mod-6896bfd7834e82b89ad2b38b


To know more about it, visit the dataset card of the respective dataset. !!
albertvillanova 
posted an update about 9 hours ago
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573
Latest smolagents release supports GPT-5: build agents that think, plan, and act.
⚡ Upgrade now and put GPT-5 to work!
meg 
posted an update about 22 hours ago
Akhil-Theerthala 
posted an update 1 day ago
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648
I'm excited to announce that I've just released the newest versions of my Kuvera models and the expanded Personal Finance Reasoning dataset on Hugging Face!

What's new:
I've expanded the Personal Finance Reasoning Dataset, which now includes 18.9k samples of real-world financial questions paired with detailed, empathetic answers. The previous generation pipeline was also streamlined with better psychological context and response validations.

I've also released new Kuvera models trained on this improved dataset:
- Kuvera-4B & 8B: These are my upgraded non-reasoning models, fine-tuned to provide practical financial advice. I've specifically trained the 8B model to better understand the user's emotional context.
- Kuvera-12B: A first experimental reasoning model focused on the query resolution.

As the sole person working on this project, this release is a noticeable step forward from my previous work, offering more powerful and nuanced tools for financial AI.

I am actively looking to collaborate with others who are passionate about analyzing and improving the quality of personal finance advice generated by large language models. If this sounds like you, please reach out!

You can check these out on the following links:

Models:
- Akhil-Theerthala/Kuvera-8B-qwen3-v0.2.1
- Akhil-Theerthala/Kuvera-4B-unsloth-gemma3
- Akhil-Theerthala/kuvera-12B-v0.2.0-unsloth-gemma3

Dataset:
- Akhil-Theerthala/Kuvera-PersonalFinance-V2.1

P.S. The paper on the framework used to generate these models along with the detailed evaluation of the main 8B model's responses is going to be released soon!
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