Search Results for "aras-common" - Page 5
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Showing 173 open source projects for "aras-common"
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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mlforecast
Scalable machine learning for time series forecasting
...Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates those automatically based on a simple configuration. It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. The library is built to scale: behind the scenes, it can leverage distributed computing frameworks (Spark, Dask, Ray) when datasets or the number of series grow large. -
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can... -
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Featuretools
An open source python library for automated feature engineering
...You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling. Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. You can specify prediction times row-by-row. Featuretools come with a library of low-level functions that can be stacked to create features. You can build and share your own custom primitives to be reused on any dataset. Featuretools works alongside tools you already use to build machine learning pipelines. ... -
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
...The course leans heavily on realistic failure modes (ambiguity, hallucination, brittle instructions) and shows how to iteratively debug prompts the way you would debug code. Lessons include building prompts from scratch for common tasks like extraction, classification, transformation, and step-by-step reasoning, with checkpoints that let you compare your outputs against solid baselines. You’ll also practice advanced patterns such as tool use, constrained generation, and response validation so outputs are trustworthy and machine-consumable. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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snntorch
Deep and online learning with spiking neural networks in Python
...This allows researchers to train spiking neural models using familiar deep learning workflows while taking advantage of GPU acceleration and automatic differentiation. snnTorch provides implementations of common spiking neuron models, surrogate gradient training methods, and utilities for handling temporal neural dynamics. Because spiking neural networks operate over time and encode information through spike timing, the library includes tools for simulating temporal behavior. -
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Hiera
A fast, powerful, and simple hierarchical vision transformer
Hiera is a hierarchical vision transformer designed to be fast, simple, and strong across image and video recognition tasks. The core idea is to use straightforward hierarchical attention with a minimal set of architectural “bells and whistles,” achieving competitive or superior accuracy while being markedly faster at inference and often faster to train. The repository provides installation options (from source or Torch Hub), a model zoo with pre-trained checkpoints, and code for evaluation... -
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BIG-bench
Beyond the Imitation Game collaborative benchmark for measuring
...Tasks are intentionally heterogeneous: some are multiple-choice with exact scoring, others are free-form generation judged by model-based or human evaluation. The suite provides a common JSON task format and an evaluation harness so research groups can contribute new tasks and reproduce results consistently. It emphasizes robustness analysis—looking at scale trends, calibration, and areas where models systematically fail—to guide model development beyond raw accuracy. BIG-bench is as much a community process as a dataset, encouraging open sharing of tasks and findings to keep evaluations fresh and comprehensive. -
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Style Aligned
Official code for Style Aligned Image Generation via Shared Attention
StyleAligned is a diffusion-model editing technique and codebase that preserves the visual “style” of an original image while applying new semantic edits driven by text. Instead of fully re-generating an image—and risking changes to lighting, texture, or rendering choices—the method aligns internal features across denoising steps so the target edit inherits the source style. This alignment acts like a constraint on the model’s evolution, steering composition, palette, and brushwork even as... -
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AutoPR
Run AI-powered workflows over your codebase
AutoPR is an AI-driven tool for automating pull request (PR) generation and review processes. It streamlines code contributions by suggesting fixes, generating pull requests, and reviewing code using AI models, reducing manual overhead for developers. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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ConsistencyDecoder
Consistency Distilled Diff VAE
...The project provides a simple API for encoding with a Stable Diffusion VAE and decoding using the new consistency model, allowing for side-by-side comparisons with traditional decoders. It demonstrates how consistency models can enhance visual fidelity while maintaining efficiency, reducing artifacts common in GAN-decoded outputs. The repository includes installation instructions, usage examples, and visual comparisons to highlight improvements. Though compact, it provides an accessible entry point for experimenting with advanced decoding strategies in diffusion-based generative models. -
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Asteroid
The PyTorch-based audio source separation toolkit for researchers
The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined in a flexible way to create new systems. ... -
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Detectron
FAIR's research platform for object detection research
...The framework emphasized a clean configuration system, strong baselines, and a “model zoo” so researchers could compare results under consistent settings. It includes training and evaluation pipelines that handle multi-GPU setups, standard datasets, and common augmentations, which helped standardize experimental practice in detection research. Visualization utilities and diagnostic scripts make it straightforward to inspect predictions, proposals, and losses while training. Although the project has since been superseded by Detectron2, the original Detectron remains a historically important, reproducible reference that still informs many productions. -
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Demucs
Code for the paper Hybrid Spectrogram and Waveform Source Separation
...It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. The repository includes pretrained models for common tasks such as isolating vocals, drums, bass, and accompaniment from stereo music, achieving state-of-the-art results in benchmarks like MUSDB18. Demucs supports GPU-accelerated inference and can process multi-channel audio with chunked streaming for real-time or batch operation. It also provides training scripts and utilities to fine-tune on custom datasets, along with remixing and enhancement tools. -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
...It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common strategies such as moving average crossovers, momentum trading, and custom indicators on historical stock data. By automating data retrieval, strategy evaluation, and result visualization, the library reduces the barrier to entry for individuals interested in quantitative finance. The project also supports optimization workflows that allow users to search for parameter combinations that improve trading strategy performance. -
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FastViT
This repository contains the official implementation of research
...The models use lightweight attention and carefully engineered blocks to minimize token mixing costs while preserving representation power. Training and inference recipes highlight straightforward integration into common vision tasks such as classification, detection, and segmentation. The codebase provides reference implementations and checkpoints that make it easy to evaluate or fine-tune on downstream datasets. In practice, FastViT offers drop-in backbones that reduce compute and memory pressure without exotic training tricks. -
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LLM Cookbook
LLM Introduction Tutorial for Developers, Chinese version
...It covers the essential topics required to start working with LLM APIs and frameworks, including prompt engineering, application architecture, retrieval-augmented generation, and system evaluation. The repository includes practical coding examples that demonstrate how to integrate language models with tools such as LangChain and other common AI development frameworks. It also provides curated learning modules that guide users from introductory concepts to more advanced topics like building conversational systems or knowledge-based assistants. -
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ChatGLM Efficient Tuning
Fine-tuning ChatGLM-6B with PEFT
ChatGLM-Efficient-Tuning is a hands-on toolkit for fine-tuning ChatGLM-family models with parameter-efficient methods on everyday hardware. It wraps techniques like LoRA and prompt-tuning into simple training scripts so you can adapt a large model to your domain without full retraining. The project exposes practical switches for quantization and mixed precision, allowing bigger models to fit into limited VRAM. It includes examples for instruction tuning and dialogue datasets, making it... -
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FastEdit
Editing large language models within 10 seconds
FastEdit focuses on rapid “model editing,” letting you surgically update facts or behaviors in an LLM without full fine-tuning. It implements practical editing algorithms that insert or revise knowledge with targeted parameter updates, aiming to preserve model quality outside the edited scope. This approach is valuable when you need urgent corrections—think product names, APIs, or fast-changing facts—without retraining on large corpora. The repository provides evaluation harnesses so you can... -
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DIG
A library for graph deep learning research
...If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms. -
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ManimML
ManimML is a project focused on providing animations
ManimML is a project focused on providing animations and visualizations of common machine-learning concepts with the Manim Community Library. Please check out our paper. We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine-learning concepts. Additionally, we want to provide a set of abstractions that allow users to focus on explanations instead of software engineering. -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
Mars is a distributed computing framework designed to scale scientific computing and data science workloads across large clusters while preserving the familiar programming interfaces of common Python libraries. The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large computational tasks into smaller chunks that can be executed across multiple nodes in a cluster, allowing complex analytics, machine learning workflows, and data transformations to run efficiently at scale. ... -
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ImageAI
A python library built to empower developers
...ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
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Riffusion
Real-time music generation using stable diffusion techniques AI
Riffusion (hobby) is a Python-based open source library designed for real-time music and audio generation using stable diffusion techniques. Riffusion (hobby) works by generating and manipulating spectrogram images, which are then converted into playable audio clips, effectively bridging image-based diffusion models with sound synthesis. It implements a diffusion pipeline that supports prompt interpolation, allowing smooth transitions between different musical styles or prompts over time.... -
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UnionML
Build and deploy machine learning microservices
...Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. ... -
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CPT
CPT: A Pre-Trained Unbalanced Transformer
A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation. We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of them are traditional Chinese characters); 2) remove redundant tokens (e.g. Chinese character tokens with ## prefix); 3) add some English tokens to reduce OOV. Position Embeddings We extend the max_position_embeddings from 512 to 1024. We...
