Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.localGPT This project was inspired by the original privateGPT (https://github.com/imartinez/privateGPT). Most of the description here is inspired by the original privateGPT. In this model, I have replaced the GPT4ALL model with Vicuna-7B model and we are using the InstructorEmbeddings instead of LlamaEmbeddings as used in the original privateGPT. Both Embeddings as well as LLM will run on…
Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022 Donut 🍩 : Document Understanding Transformer Official Implementation of Donut and SynthDoG | Paper | Slide | Poster Introduction Donut 🍩, Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art p…
百亿参数的中英文双语基座大模型 CPM-Bee 百亿参数的开源中英文双语基座大模型 模型 • OpenBMB体系 • 性能表现 • 开源协议 ✨ 模型介绍 CPM-Bee是一个完全开源、允许商用的百亿参数中英文基座模型，也是CPM-Live训练的第二个里程碑。它采用Transformer自回归架构（auto-regressive），在超万亿（trillion）高质量语料上进行预训练，拥有强大的基础能力。开发者和研究者可以在CPM-Bee基座模型的基础上在各类场景进行适配来以创建特定领域的应用模型。 👐 开源可商用：OpenBMB始终秉承“让大模型飞入千家万户”的开源精神，CPM-Bee基座模型将完全开源并且可商用，以推动大模型领域的发展。我们鼓励全球范围内的科研机构、企业和个人开发者在遵守开源许可协议的前提下，自由地在CPM-Bee基座模型上进行创新。 💫 中英双语性能优异： CPM-Bee基座模型在预训练语料上进行了严格的筛选和配比，同时在中英双语上具有亮眼表现，具体可参见评测任务和结果。 📖 超大规模高质量语料： CPM-Bee基座模型在超万亿语…
A unified framework for 3D content generation. threestudio is a unified framework for 3D content creation from text prompts, single images, and few-shot images, by lifting 2D text-to-image generation models. 👆 Results obtained from methods implemented by threestudio 👆 | DreamFusion | Magic3D | SJC | Latent-NeRF | Fantasia3D | Did not find what you want? Submit a feature request or upvote others' requests here! News 05/26/2023: An experimental implem…
大麦网自动购票, 支持docker一键部署。https://t.me/+2EELgNTYiMYxMTFldm-ticket 简介 大麦网自动购票, 支持docker一键部署。 友情提示: 请先将README看完, 会避免一些问题。 如有问题，大部分可以在issue中找到答案。 特别声明 本项目内所有资源文件，禁止任何公众号、自媒体进行任何形式的转载、发布。 编写本项目主要目的为学习和研究Rust，无法保证项目内容的合法性、准确性、完整性和有效性。 本项目涉及的数据由使用的个人或组织自行填写，本项目不对数据内容负责，包括但不限于数据的真实性、准确性、合法性。使用本项目所造成的一切后果，与本项目的所有贡献者无关，由使用的个人或组织完全承担。 本项目中涉及的第三方硬件、软件等，与本项目没有任何直接或间接的关系。本项目仅对部署和使用过程进行客观描述，不代表支持使用任何第三方硬件、软件。使用任何第三方硬件、软件，所造成的一切后果由使用的个人或组织承担，与本项目无关。 本项目中所有内容只供学习和研究使用，不得将本项目中任何内容用于违反国家/地区/组织等的法律法规或相关规定的其他用途。 所有…
LoRA training scripts use kohya-ss's trainer, for diffusion model.LoRA-scripts LoRA training scripts for kohya-ss/sd-scripts ✨NEW: Train GUI Follow the installation guide below to install the GUI, then run run_gui.ps1(windows) or run_gui.sh(linux) to start the GUI. Usage Clone repo with submodules git clone --recurse-submodules https://github.com/Akegarasu/lora-scripts Required Dependencies Python 3.10.8 and Git Windows Installation Run install.ps1 will automaticilly create a venv for you and …
A custom script for AUTOMATIC1111/stable-diffusion-webui to implement a tiny template language for random prompt generationStable Diffusion Dynamic Prompts extension A custom extension for AUTOMATIC1111/stable-diffusion-webui that implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures. Table of Contents Basic Usage Online resources Installation Configuration Troubleshooting Com…
Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch Soundstorm - Pytorch (wip) Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch. They basically applied MaskGiT to the residual vector quantized codes from Soundstream. The transformer architecture they chose to use is one that fits well with the audio domain, named Conformer Project Page Appreciation Stability and 🤗 Huggingface for their generous sp…
Handwriting Synthesis with RNNs ✏️ Handwriting Synthesis Implementation of the handwriting synthesis experiments in the paper Generating Sequences with Recurrent Neural Networks by Alex Graves. The implementation closely follows the original paper, with a few slight deviations, and the generated samples are of similar quality to those presented in the paper. Web demo is available here. Usage lines = [ "Now this is a story all about how", "My life got flipped turned upside d…