#python#ai#ai_art#art#asset_generator#chatbot#deep_learning#desktop_app#image_generation#mistral#multimodal#privacy#pygame#pyside6#python#self_hosted#speech_to_text#stable_diffusion#text_to_image#text_to_speech#text_to_speech_app
AI Runner is a tool that lets you use AI on your own computer without needing the internet. It can do many things like **voice chatbots**, **text-to-image** generation, and **image editing**. You can also make AI personalities for more interesting conversations. It runs fast and securely, keeping your data private. To use AI Runner, you need a good computer with a strong GPU, like an NVIDIA RTX 3060 or better. This helps keep your data safe and makes AI tasks faster.
https://github.com/Capsize-Games/airunner
#DL
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Zeus New Pytorch Ecosystem Tool
Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads.
🖥Github
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Main channel: @repo_science
Coupons: @freecoupons_reposcience
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#dl
Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12.
https://www.nature.com/articles/s41467-025-63790-8
#dl
A few cool ideas in this model.
Introducing Gemma 3n: The developer guide - Google Developers Blog
https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
#dl
There is this new lib called scale. One could compile CUDA code to use it on AMD GPU.
https://docs.scale-lang.com/manual/how-to-use/
I don't know who is more pissed off, NVidia or AMD.
#dl
This repo is really nice.
yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models
https://github.com/yuanchenyang/smalldiffusion
#dl
Google & USC benchmarked a prompt based forecasting method, and the results are amazing.
Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948