Search Results for "tiny paw linux" - Page 2
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Showing 36 open source projects for "tiny paw linux"
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1
jieba
Stuttering Chinese word segmentation
"Jaba" Chinese word segmentation, do the best Python Chinese word segmentation component. Four word segmentation modes are supported. Precise mode, which tries to cut the sentence most precisely, suitable for text analysis. Full mode, scans all the words that can be formed into words in the sentence, the speed is very fast, but the ambiguity cannot be resolved. The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable... -
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NASH OS
Nash Operating System for Modern Ecommerce
The all-built-in-one, automatic, ready-to-go out-of-box, easy-to-use state-of-the-art, and really awesome NASH OS! Over 25,000+ flexible features and controls and all scalable!! The most powerful solution ever built to instantly deliver new heights of online ecommerce enterprise to you. -
3
AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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tiny-dnn
header only, dependency-free deep learning framework in C++14
tiny-dnn is a C++14 implementation of deep learning. It is suitable for deep learning on limited computational resources, embedded systems, and IoT devices. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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5
CSSgram
CSS library for Instagram filters
Simply put, CSSgram is a library for editing your images with Instagram-like filters directly using CSS. What we're doing is adding filters to the images, as well as applying color and/or gradient overlays via various blending techniques to mimic filter effects. This means less manual image processing and more fun filter effects on the web! We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a... -
6
smartblob
tiny code, html webcam game or plug brain in each blob, java server
You play a 2d blob that reshapes to grab bend bounce and swing on objects floating in midair (like a platformer game), except acrobaticly your view spins when your blob does. I'm planning a huge multiplayer world, some blobs played by people holding a bendable loop game controller (tape a 1 meter cut of thick extension cord into a loop) in front of webcam and bend it to bend your blob on screen, and other blobs controlled by AI. This is a game for general AI research in a fun way people can... -
7
Identification
A tiny "guess what I'm thinking about" game, or an identifier.
This is a tiny game : think about something and answer the questions the software asks. The goal is to have a "identifier" which is able, for instance, to find which mushroom you have in your hands. -
8
tiny-AI Library
small and fast C++ library dealing with artificial intelligence
A fast artificial intelligence library which currently supports: kNN (k-Nearest Neighbor algorithm) MLP (Multilayer-Perceptron) -
9
HebbBrain
FEED-FORWARD NETWORK
FEED-FORWARD network. Simple to use, hard to manage. Born to be fast and tiny. AI FEED-FORWARD neural network -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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10
granite-timeseries-ttm-r2
Tiny pre-trained IBM model for multivariate time series forecasting
granite-timeseries-ttm-r2 is part of IBM’s TinyTimeMixers (TTM) series—compact, pre-trained models for multivariate time series forecasting. Unlike massive foundation models, TTM models are designed to be lightweight yet powerful, with only ~805K parameters, enabling high performance even on CPU or single-GPU machines. The r2 version is pre-trained on ~700M samples (r2.1 expands to ~1B), delivering up to 15% better accuracy than the r1 version. TTM supports both zero-shot and fine-tuned... -
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Ministral 3 3B Base 2512
Small 3B-base multimodal model ideal for custom AI on edge hardware
Ministral 3 3B Base 2512 is the smallest model in the Ministral 3 family, offering a compact yet capable multimodal architecture suited for lightweight AI applications. It combines a 3.4B-parameter language model with a 0.4B vision encoder, enabling both text and image understanding in a tiny footprint. As the base pretrained model, it is not fine-tuned for instructions or reasoning, making it the ideal foundation for custom post-training, domain adaptation, or specialized downstream tasks....
