Search Results for "using class net.sourceforge.jtds.jdbc.driver"

...For conditioning on text, they derived text embeddings by first passing the tokenized text through BERT-large. You can also directly pass in the descriptions of the video as strings, if you plan on using BERT-base for text conditioning. This repository also contains a handy Trainer class for training on a folder of gifs. Each gif must be of the correct dimensions image_size and num_frames.

...It uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image. In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space.

Infinite Craft but in Pyside6 and Python with local LLM (llama2 & others) using Ollama that also lets you create your own crafting game based on any topic Customize the game any way you like in the settings.

Langfuse is building open-source observability and analytics for LLM apps. Observability: Explore and debug complex logs & traces in a visual UI. Analytics: Improve performance of LLM apps. In particular, get a view on costs, latency and response quality using intuitive dashboards.

Primordial Photon–Dark Photon Entanglement A research and analysis framework for testing the Photon–Dark Photon Entanglement Hypothesis, analyzing astronomical imaging data, and generating reproducible physics validation results using HST, JWST, and other observatory FITS products. 🚀 Overview This repository provides: Physics Validation Tests that compute predicted P–D entanglement observables. Expected Numerical Results for cross‑checking model predictions. Automated...

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 pretrained VAE offered by the authors of Taming Transformers! Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192. ...

Aida is a language-agnostic library for text generation. When using Aida, first you compose a tree of operations on your text that includes conditions via branches and other control flow. Later, you fill the tree with data and render the text. A building block is a variable class: Var. Use it to represent a value that you want to control later. A variable can hold numbers (e.g. float, int) or strings.