Search Results for "environment-modules"
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Showing 22 open source projects for "environment-modules"
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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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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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Kaleidoscope-SDK
User toolkit for analyzing and interfacing with Large Language Models
kaleidoscope-sdk is a Python module used to interact with large language models hosted via the Kaleidoscope service available at: https://github.com/VectorInstitute/kaleidoscope. It provides a simple interface to launch LLMs on an HPC cluster, asking them to perform basic features like text generation, but also retrieve intermediate information from inside the model, such as log probabilities and activations. Users must authenticate using their Vector Institute cluster credentials. This can... -
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NVIDIA NeMo
Toolkit for conversational AI
...NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. ... -
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Generative AI
Sample code and notebooks for Generative AI on Google Cloud
Generative AI is a comprehensive collection of code samples, notebooks, and demo applications designed to help developers build generative-AI workflows on the Vertex AI platform. It spans multiple modalities—text, image, audio, search (RAG/grounding) and more—showing how to integrate foundation models like the Gemini family into cloud projects. The README emphasises getting started with prompts, datasets, environments and sample apps, making it ideal for both experimentation and... -
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InvokeAI
InvokeAI is a leading creative engine for Stable Diffusion models
InvokeAI is an implementation of Stable Diffusion, the open source text-to-image and image-to-image generator. It provides a streamlined process with various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies.... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Diffusers
State-of-the-art diffusion models for image and audio generation
...Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. We recommend installing Diffusers in a virtual environment from PyPi or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation. -
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Haystack
Haystack is an open source NLP framework to interact with your data
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning,... -
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OpenAI DALL·E AsyncImage SwiftUI
OpenAI swift async text to image for SwiftUI app using OpenAI
SwiftUI views that asynchronously loads and displays an OpenAI image from open API. You just type in your idea and AI will give you an art solution. DALL-E and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions, called "prompts". You need to have Xcode 13 installed in order to have access to Documentation Compiler (DocC) OpenAI's text-to-image model DALL-E 2 is a recent example of diffusion models. It uses diffusion models for... -
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SDGym
Benchmarking synthetic data generation methods
...In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch. They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion. The pseudo-3d convolutions isn't a new concept. It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning... -
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Matrix ChatGPT Bot
Talk to ChatGPT via any Matrix client
...If you use this bot, your OpenAI account will be charged! You might want to limit your budget in your account using the OpenAI website. You need to remove the CHATGPT_MODEL variable from your environment, if you changed the value. Create a room, add the bot, andtart chatting. You need to have an account at openai. Please note that the usage of the ChatGPT-API is not free. -
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GPT-Code UI
An open source implementation of OpenAI's ChatGPT Code interpreter
...Simply ask the OpenAI model to do something and it will generate & execute the code for you. You can put a .env in the working directory to load the OPENAI_API_KEY environment variable. For Azure OpenAI Services, there are also other configurable variables like deployment name. See .env.azure-example for more information. Note that model selection on the UI is currently not supported for Azure OpenAI Services. -
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MMGeneration
MMGeneration is a powerful toolkit for generative models
...It's time to play with your GANs! For the highly dynamic training in generative models, we adopt a new way to train dynamic models with MMDDP. A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combinations among different modules. Conditional GANs have been supported in our toolkit. More methods and pre-trained weights will come soon. -
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VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
...Experiment results show that VALL-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find VALL-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. -
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TextBox
A text generation library with pre-trained language models github.com
...From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets. From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). From a training perspective, we support 4 pre-training objectives and 4 efficient and robust training strategies, such as distributed data parallel and efficient generation. Compared with the previous version of TextBox, this extension mainly focuses on building a unified, flexible, and standardized framework for better supporting PLM-based text generation models. -
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PerlPP
Perl preprocessor - embed Perl source in any file
Translates Text+Perl to Text. It can be used for any kind of text templating, e.g. code generation. No external modules are required, just a single file. Requires Perl 5.10.1+. PerlPP runs in two passes: it generates a Perl script from your input, and then it runs the generated script. If you see error at (eval ##) (for some number ##), it means there was an error in the generated script. The -D switch defines elements of %D. If you do not specify a value, the value true (a constant in the generated script) will be used. ... -
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abstract2paper
Auto-generate an entire paper from a prompt or abstract using NLP
...Note: to compile a PDF of your auto-generated paper (when you run the demo locally), you'll need to have a working LaTeX installation on your machine (e.g., so that pdflatex is a recognized system command). The notebook will also automatically install the transformers library if it's not already available in your local environment. In its unmodified state, the demo notebooks use the abstract from the GPT-3 paper as the "seed" for a new paper. Each time you run the notebook you'll get a new result. -
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StudioGAN
StudioGAN is a Pytorch library providing implementations of networks
...The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT, RQ-Transformer), and Diffusion models (LSGM++, CLD-SGM, ADM-G-U). StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. Among these configurations, we formulate 30 GANs as representatives. Each modularized option is managed through a configuration system that works through a YAML file. -
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Texar-PyTorch
Integrating the Best of TF into PyTorch, for Machine Learning
...Texar-PyTorch integrates many of the best features of TensorFlow into PyTorch, delivering highly usable and customizable modules superior to PyTorch native ones. Texar-PyTorch (this repo) and Texar-TF have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Data processing, model architectures, loss functions, training and inference algorithms, evaluation, etc. -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
...However, for regular usage across multiple sessions, I prefer a local setup that can be started up rapidly. Thus, this simple Streamlit app for generating VQGAN-CLIP images on a local environment. Be advised that you need a beefy GPU with lots of VRAM to generate images large enough to be interesting. (Hello Quadro owners!). -
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GPT-2 FR
GPT-2 French demo | Démo française de GPT-2
...Books in French, French film scripts, reports of parliamentary debates, Tweet by Emmanuel Macron, allowing to generate text. Tensorflow and gpt-2-simple are required in order to fine-tune GPT-2. Create an environment then install the two packages pip install tensorflow==1.14 gpt-2-simple. A script and a notebook are available in the src folder to fine-tune GPT-2 on your own datasets. The output of each workout, i.e. the folder checkpoint/run1, is to be put ingpt2-model/model1 model2 model3 etc. You can run the script deploy_cloudrun.shto deploy all your different models (into gpt2-model) at once. ... -
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Market Reporter
Automatic Generation of Brief Summaries of Time-Series Data
...Ask the manager to give you AmazonS3FullAccess and issue a credential file. For details, please read AWS Identity and Access Management. Install Docker and Docker Compose. Edit envs/docker-compose.yaml according to your environment. Then, launch containers by docker-compose. We recommend to use pipenv to make a Python environment for this project. Suppose you have a database named master on your local machine. Prediction submodule generates a single comment of a financial instrument at specified time by loading a trained model. -
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Bottery
A conversational agent prototyping platform
Bottery is a prototyping environment and mini-language for designing conversational agents as explicit state machines rather than opaque bundles of code. It encourages authors to model intents, dialog turns, and transitions in a compact, declarative form that’s easy to visualize and simulate. A built-in simulator lets you “play” the conversation, test edge cases, and tweak flows without wiring up back-end services first.
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