Search Results for "cdm-core" - Page 12
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Showing 466 open source projects for "cdm-core"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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LLaMA
Inference code for Llama models
...Tokenizer utilities, download scripts, shell helpers to fetch model weights with correct licensing/permissions. Includes example scripts for chat completions and text completions to show how to call the models in code. This repo is a core piece of the Llama model infrastructure, used by researchers and developers to run LLaMA models locally or in their infrastructure. It is meant for inference (not training from scratch) and connects with aspects like model cards, responsible use, licensing, etc. -
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simpleaichat
Python package for easily interfacing with chat apps
simpleaichat is a Python library that streamlines building conversational apps with large language models by offering a minimal, developer-friendly interface. It aims to abstract the boilerplate of prompt management, message history, and streaming while leaving core Python control in your hands. The package emphasizes simplicity over heavy frameworks, making it ideal for scripts, notebooks, and small services that need LLMs without architectural lock-in. It supports structured responses and validation patterns so your app can reliably parse model outputs instead of wrestling with brittle free-text parsing. ... -
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Free-Auto-GPT
Free AutoGPT enables autonomous AI tasks without paid APIs
...It is suitable for developers, learners, and hobbyists who want to explore AI-driven automation without subscription requirements. It provides a lightweight implementation that highlights core AutoGPT concepts such as task decomposition, iteration, and independent execution in a simplified environment. -
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ParlAI
A framework for training and evaluating AI models
...It provides a unified interface—agents, teachers, and worlds—so the same model can be trained on multi-turn chit-chat, question answering, task-oriented dialogue, retrieval, or safety-focused datasets without changing core code. The library integrates tightly with PyTorch and supports both generative and retrieval-augmented models, along with utilities for multitask training and model selection. A large set of built-in tasks and dataset loaders (with consistent preprocessing and metrics) makes it easy to compare methods under shared conditions. Tools for distributed training, mixed precision, and model zoos help scale experiments from laptops to multi-GPU clusters. -
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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VAMS
Virtual Assistant Maintenance System
Virtual Assistant Maintenance System also knowns as VAMS is an AI software application, that helps users with some computer maintenance issues. Application Requirements: Operating System: Windows 8.1/10 /11 Processor: Intel Core i5 or equivalent RAM: 4GB or higher Free Disk Space: 500MB -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. ... -
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Chameleon LLM
Codes for "Chameleon: Plug-and-Play Compositional Reasoning
...By integrating various tools such as vision models, web search engines, Python functions, and rule-based modules, Chameleon delivers more accurate, up-to-date, and precise responses, making it a game-changer in the natural language processing landscape. With GPT-4 at its core, Chameleon has showcased exceptional improvements in accuracy on benchmark tasks, outperforming competitors and setting new industry standards. -
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OptiMate
Libraries for optimizing AI models, inference speed, and GPU usage
...Its modules help developers automatically apply optimization techniques that better align AI models with the capabilities of the underlying hardware such as GPUs and CPUs. One of the core components, Speedster, focuses on accelerating model inference by applying state of the art optimization techniques to increase performance while lowering operational costs. Another component, Nos, targets infrastructure optimization by improving GPU utilization in Kubernetes clusters through dynamic partitioning and elastic resource quotas. -
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textacy
NLP, before and after spaCy
textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals, tokenization, part-of-speech tagging, dependency parsing, etc., delegated to another library, textacy focuses primarily on the tasks that come before and follow after. -
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minGPT
A minimal PyTorch re-implementation of the OpenAI GPT
minGPT is a minimalist, educational re-implementation of the GPT (Generative Pretrained Transformer) architecture built in PyTorch, designed by Andrej Karpathy to expose the core structure of a transformer-based language model in as few lines of code as possible. It strips away extraneous bells and whistles, aiming to show how a sequence of token indices is fed into a stack of transformer blocks and then decoded into the next token probabilities, with both training and inference supported. Because the whole model is around 300 lines of code, users can follow each step—from embedding lookup, positional encodings, multi-head attention, feed-forward layers, to output heads—and thus demystify how GPT-style models work beneath the surface. ... -
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Riffusion
Real-time music generation using stable diffusion techniques AI
...It implements a diffusion pipeline that supports prompt interpolation, allowing smooth transitions between different musical styles or prompts over time. Riffusion (hobby) serves as the core implementation for audio and image processing, providing essential building blocks for generating music from text prompts. It includes both developer-oriented tools and user-facing components such as a command-line interface and an interactive Streamlit application for experimentation. Additionally, it can run as a Flask server to expose model inference through an API, enabling integration with other applications or services. -
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SQLBucket
Lightweight library to write, orchestrate and test your SQL ETL
...It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines. To start working, you need to instantiate your SQLBucket core object with the project_folder parameter. That folder will contain all your SQL ETL. The python file where you create your SQLBucket object is also a good place to instantiate your command line interface. -
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FairScale
PyTorch extensions for high performance and large scale training
...FairScale puts emphasis on correctness and debuggability, offering hook points, logging, and reference examples for common trainer patterns. Although many ideas have since landed in core PyTorch, FairScale remains a valuable reference and a practical toolbox for squeezing more performance out of multi-GPU and multi-node jobs. -
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Name-That-Hash
Identify MD5, SHA256 and 300+ other hashes
...It is designed as a successor and improvement to older tools like HashID and Hash-Identifier, focusing on up-to-date hash databases and better usability. One of its core ideas is popularity-aware ranking: when you feed in a hash, it prioritizes likely real-world types such as NTLM over obscure ones like Skype hashes, instead of treating them equally. The tool provides concise “hash summaries” that explain where a given hash format is commonly used, helping users decide how to proceed with cracking or further analysis. ... -
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aiopg
aiopg is a library for accessing a PostgreSQL database
aiopg is an asynchronous PostgreSQL database driver for Python built on top of asyncio and psycopg2. It provides support for asynchronous query execution and connection pooling, enabling efficient, non-blocking database access in Python applications. aiopg is well-suited for web services and microservices that require concurrency without threads. -
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AllenNLP
An open-source NLP research library, built on PyTorch
AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional allennlp subcommands. There is ecosystem of open source plugins, some of which are maintained by the AllenNLP team here at AI2, and some of which are maintained by the broader community. ... -
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DiffSinger
Singing Voice Synthesis via Shallow Diffusion Mechanism
DiffSinger is an open-source PyTorch implementation of a diffusion-based acoustic model for singing-voice synthesis (SVS) and also text-to-speech (TTS) in a related variant. The core idea is to view generation of a sung voice (mel-spectrogram) as a diffusion process: starting from noise, the model iteratively “denoises” while being conditioned on a music score (lyrics, pitch, musical timing). This avoids some of the typical problems of prior SVS models — like over-smoothing or unstable GAN training — and produces more realistic, expressive, and natural-sounding singing. ... -
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CommandlineConfig
A library for users to write configurations in Python
...It lets you define configuration in familiar Python dictionaries or JSON files and then access nested parameters via dot notation in code, improving readability and reducing boilerplate. One of its core strengths is the ability to override configuration values directly from the command line, making it convenient to run many experimental variants without editing files repeatedly. The library supports arbitrarily deep nested structures, type handling, enumerated value constraints, and even tuple types, which are common in ML experiment setups. ... -
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AWS GameKit Plugin for Unreal
The AWS GameKit Plugin for Unreal
AWS GameKit is an open-source engine integration toolkit for game developers who want to create high-quality, cloud-connected game features without the potentially steep learning curve for cloud service architecture and AWS. With AWS GameKit, you can deliver core functionality to your players with minimal effort and time by using pre-built AWS solutions for your game backend and integrating the features into your game. You can use the AWS GameKit game features as designed or as a launch pad to expand and customize as needed. The plugin is developed using Unreal version 4.27. Install before proceeding. ... -
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Apple Neural Engine (ANE) Transformers
Reference implementation of the Transformer architecture optimized
...It demonstrates how to structure attention and related layers to achieve substantial speedups and lower peak memory compared to baseline implementations when deployed to ANE. The repository targets practitioners who want to keep familiar PyTorch modeling while preparing models for Core ML/ANE execution paths. Documentation highlights reported improvements in throughput and memory residency, while releases track incremental fixes and packaging updates. The project sits alongside related Apple ML repos that focus on deploying attention-based models efficiently to ANE-equipped hardware. In short, it’s a practical blueprint for adapting Transformers to Apple’s dedicated ML accelerator without rewriting entire model stacks. -
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. ... -
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pyntcloud
pyntcloud is a Python library for working with 3D point clouds
This page will introduce the general concept of point clouds and illustrate the capabilities of pyntcloud as a point cloud processing tool. Point clouds are one of the most relevant entities for representing three dimensional data these days, along with polygonal meshes (which are just a special case of point clouds with connectivity graph attached). In its simplest form, a point cloud is a set of points in a cartesian coordinate system. Accurate 3D point clouds can nowadays be (easily and... -
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tom_core
tom_core - a tool for automating events on a computer
tom_core is a software tool used for the automation of everything that happens on your computer. By using this application, you can easily record your activity on your computer, starting the recording at any moment that you choose. The application repeats all your clicks or drags, keystrokes, hotkeys, etc. All in exactly the timing and number of repetitions you need. The toolbox such as the optical recognition and voice control enables to branch out the recordings into complex forms, with... -
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Mask2Former
Code release for "Masked-attention Mask Transformer
Mask2Former is a unified segmentation architecture that handles semantic, instance, and panoptic segmentation with one model and one training recipe. Its core idea is to cast segmentation as mask classification: a transformer decoder predicts a set of mask queries, each with an associated class score, eliminating the need for task-specific heads. A pixel decoder fuses multi-scale features and feeds masked attention in the transformer so each query focuses computation on its current spatial support. ... -
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pyTorch Tutorials
Build your neural network easy and fast
...The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. Each example explains PyTorch’s dynamic computation graph, optimization techniques, and core abstractions in a way that is accessible and reproducible. Contributors and authors integrate visual and coded examples so readers can see both the theory and the implementation side-by-side.