Search Results for "one-wire"
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Showing 47 open source projects for "one-wire"
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1
Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
...You can turn on automatic mixed precision with one flag --amp. You should expect it to be 33% faster and save up to 40% memory. Aim is an open-source experiment tracker that logs your training runs, and enables a beautiful UI to compare them. -
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Haystack
Haystack is an open source NLP framework to interact with your data
...Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Pick any Transformer model from Hugging Face's Model Hub, experiment, find the one that works. Use Haystack NLP components on top of Elasticsearch, OpenSearch, or plain SQL. Boost search performance with Pinecone, Milvus, FAISS, or Weaviate vector databases, and dense passage retrieval. -
3
ChatGPT API
Node.js client for the official ChatGPT API. 🔥
...🔥 Note: We strongly recommend using ChatGPTAPI since it uses the officially supported API from OpenAI. We may remove support for ChatGPTUnofficialProxyAPI in a future release. 1. ChatGPTAPI - Uses the gpt-3.5-turbo-0301 model with the official OpenAI chat completions API (official, robust approach, but it's not free) 2. ChatGPTUnofficialProxyAPI - Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited) -
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pytorch-cpp
C++ Implementation of PyTorch Tutorials for Everyone
C++ Implementation of PyTorch Tutorials for Everyone. This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch (i.e. Section 1 to 3) Interactive Tutorials are currently running on LibTorch Nightly Version. Libtorch only supports 64bit Windows and an x64 generator needs to be specified. Create all required script module files for pre-learned models/weights during the build. Requires installed python3 with PyTorch and torch-vision. You can choose to only build tutorials in one of the categories basics, intermediate, advanced or popular. ... -
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5
LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
6
GPTel
A no-frills ChatGPT client for Emacs
...Also, it’s async. Interact with ChatGPT from any buffer in Emacs. ChatGPT’s responses are in Markdown or Org markup (configurable). Supports conversations (not just one-off queries) and multiple independent sessions. You can go back and edit your previous prompts, or even ChatGPT’s previous responses when continuing a conversation. These will be fed back to ChatGPT. Run M-x gptel to start or switch to the ChatGPT buffer. It will ask you for the key if you skipped the previous step. Run it with a prefix-arg to start a new session. ... -
7
Deep Lake
Data Lake for Deep Learning. Build, manage, and query datasets
...Our open-source dataset format is optimized for rapid streaming and querying of data while training models at scale, and it includes a simple API for creating, storing, and collaborating on AI datasets of any size. It can be deployed locally or in the cloud, and it enables you to store all of your data in one place, ranging from simple annotations to large videos. Deep Lake is used by Google, Waymo, Red Cross, Omdena, Yale, & Oxford. Use one API to upload, download, and stream datasets to/from AWS S3/S3-compatible storage, GCP, Activeloop cloud, or local storage. Store images, audios and videos in their native compression. Deeplake automatically decompresses them to raw data only when needed, e.g., when training a model. ... -
8
hfapigo
Unofficial (Golang) Go bindings for the Hugging Face Inference API
(Golang) Go bindings for the Hugging Face Inference API. Directly call any model available in the Model Hub. An API key is required for authorized access. To get one, create a Hugging Face profile. -
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AudioLM - Pytorch
Implementation of AudioLM audio generation model in Pytorch
Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch It also extends the work for conditioning with classifier free guidance with T5. This allows for one to do text-to-audio or TTS, not offered in the paper. Yes, this means VALL-E can be trained from this repository. It is essentially the same. This repository now also contains a MIT licensed version of SoundStream. It is also compatible with EnCodec, however, be aware that it has a more restrictive non-commercial license, if you choose to use it. -
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10
terminalGPT
Get GPT like ChatGPT on your terminal
Get GPT like ChatGPT on your terminal Note: This doesn't use OpenAI ChatGPT, it uses text-davinci-003 model (by default) You'll need to have your own OpenAi apikey to operate this package. 1. Go to https://beta.openai.com 2. Select you profile menu and go to View API Keys 3. Select + Create new secret key 4. Copy generated key Get started: Using tgpt: npm -g install terminalgpt or yarn global add terminalgpt Run tgpt chat ps.: If it is your first time running it, it will ask for open AI key , paste generated key from pre-requisite steps -
11
BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
...BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention. -
12
Phenaki - Pytorch
Implementation of Phenaki Video, which uses Mask GIT
...It will also combine another technique involving a token critic for potentially even better generations. A new paper suggests that instead of relying on the predicted probabilities of each token as a measure of confidence, one can train an extra critic to decide what to iteratively mask during sampling. This repository will also endeavor to allow the researcher to train on text-to-image and then text-to-video. Similarly, for unconditional training, the researcher should be able to first train on images and then fine tune on video. -
13
Orion
A machine learning library for detecting anomalies in signals
...Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. Abstract away from the users the nitty-gritty about preprocessing, finding the best pipeline, and postprocessing. We want to provide a systematic way to evaluate the latest and greatest machine learning methods via our benchmarking effort. ... -
14
website-to-gif
Turn your website into a GIF
This Github Action automatically creates an animated GIF or WebP from a given web page to display on your project README (or anywhere else). In your GitHub repo, create a workflow file or extend an existing one. You have to also include a step to checkout and commit to the repo. You can use the following example gif.yml. Make sure to modify the url value and add any other input you want to use. WebP rendering will take a lot of time to benefit from lossless quality and file size optimization. -
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Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
...The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning points would easily apply to Imagen), make a few minor modifications for attention across time and other ways to skimp on the compute cost, do frame interpolation correctly, get a great video model out. Passing in images (if one were to pretrain on images first), both temporal convolution and attention will be automatically skipped. In other words, you can use this straightforwardly in your 2d Unet and then port it over to a 3d Unet once that phase of the training is done. -
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ProjectLibre - Project Management
#1 alternative to Microsoft Project : Project Management & Gantt Chart
ProjectLibre project management software: #1 free alternative to Microsoft Project w/ 7.8M+ downloads in 193 countries. ProjectLibre is a replacement of MS Project & includes Gantt Chart, Network Diagram, WBS, Earned Value etc. This site downloads our FOSS desktop app. 🌐 Try the Cloud: http://www.projectlibre.com/register/trial We also offer ProjectLibre Cloud—a subscription, AI-powered SaaS for teams & enterprises. -
17
KoboldCpp
Run GGUF models easily with a UI or API. One File. Zero Install.
KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features. -
18
uweb browser: unlimited power
minimal suckless android web browser with unlimited power
... - Convenient: book/dictionary/txt/command line/app can be search engine. - Tiny: less than 200k - Fast: run fast, even with thousands of user provided css/scripts - Efficient: less touches, one click to reach any number of search engines without repeated input; automate online services. - URL bar command line support ("!" and .js files as commands). - user-defined site-specific JS/CSS/HTML/preprocessing. - Online play/preview/preprocess for downloadable resources. - Multiple type profiles: switch any data including logins/config orthogonally - web automation, crontab (alarm clock) -
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Conversations
App in java for chatting to a generative A.I. (involving tts and stt)
... * The AI responds and the server returns that response in real time, and the sentences converted to audio (textToSpeech), and the application broadcasts them through the speaker. The application is prepared so that only one user occupies the server's resources, so if the server is busy, in theory it will not let you connect. There is a demo video that shows how it works: https://frojasg1.com:8443/resource_counter/resourceCounter?operation=countAndForward&url=https%3A%2F%2Ffrojasg1.com%2Fdemos%2Faplicaciones%2Fchat%2F20240815.Demo.Chat.mp4%3Forigin%3Dsourceforge&origin=web More about it at this web site: https://www.frojasg1.com:8443/downloads_web/web/html/conversaciones.html?... -
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Stable Diffusion in Docker
Run the Stable Diffusion releases in a Docker container
...Run the Stable Diffusion releases on Huggingface in a GPU-accelerated Docker container. By default, the pipeline uses the full model and weights which requires a CUDA capable GPU with 8GB+ of VRAM. It should take a few seconds to create one image. On less powerful GPUs you may need to modify some of the options; see the Examples section for more details. If you lack a suitable GPU you can set the options --device cpu and --onnx instead. Since it uses the model, you will need to create a user access token in your Huggingface account. Save the user access token in a file called token.txt and make sure it is available when building the container. ... -
21
Petals
Run 100B+ language models at home, BitTorrent-style
...Run large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. Single-batch inference runs at ≈ 1 sec per step (token) — up to 10x faster than offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec. Beyond classic language model APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch. ... -
22
DALL-E in Pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image
Implementation / replication of DALL-E (paper), OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the generations. Kobiso, a research engineer from Naver, has trained on the CUB200 dataset here, using full and deepspeed sparse attention. You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! The wrapper class should take care of downloading and caching the model for you auto-magically. You can also use the... -
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texturize
Generate photo-realistic textures based on source images
Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture. A command-line tool and Python library to automatically generate new textures similar to a source image or photograph. It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination... -
24
flat
All-in-one image generation AI
All-in-one image generation AI. Launch StableDiffusionWebUI with just a few clicks. No Python installation or repository cloning is required. Displays generated images in a list with information such as prompts. The image folder can be set freely. -
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Diffusion WebUI Colab
Choose your diffusion models and spin up a WebUI on Colab in one click
The most simplistic Colab with most models included by default. Custom models can be added easily. Stable Diffusion 2.0 in testing phase. Choose your diffusion models and spin up a WebUI on Colab in one click. Share your generations in our mastodon server - (This is hosted by a third party. I am not associated with the instance in any way.) The instructions are on the Colab.