Repository History
81 repositories tagged with Machine Learning
Lance: Modern Columnar Data Format for ML and LLMs
Lance is a modern columnar data format, implemented in Rust, designed for machine learning and large language model workflows. It offers significant performance improvements over Parquet for random access, includes vector indexing, and supports data versioning. Compatible with popular tools like Pandas, DuckDB, and PyTorch, Lance streamlines data management for ML applications.
Gradio: Build and Share Machine Learning Apps in Python
Gradio is an open-source Python library that simplifies the creation and sharing of interactive web applications for machine learning models, APIs, or any Python function. It allows developers to quickly build user interfaces without needing JavaScript, CSS, or web hosting expertise, offering a straightforward way to demo AI projects. With Gradio, you can transform your Python functions into shareable web demos in just a few lines of code.
Step-Video-T2V: State-of-the-Art Text-to-Video Generation Model
Step-Video-T2V is a state-of-the-art text-to-video pre-trained model capable of generating videos up to 204 frames with 30 billion parameters. It achieves high efficiency through a deep compression Video-VAE and enhances visual quality using Direct Preference Optimization (DPO). The model's performance is validated on its novel benchmark, Step-Video-T2V-Eval, demonstrating superior text-to-video quality.
Plexe: Build Machine Learning Models from Natural Language Prompts
Plexe is an innovative Python library that empowers developers to build machine learning models using natural language descriptions. It automates the entire model creation process, from intent to deployment, through an intelligent multi-agent architecture. This allows for rapid development and experimentation, making ML accessible and efficient.
Fluxgym: Simple FLUX LoRA Training UI with Low VRAM Support
Fluxgym offers a user-friendly web interface for training FLUX LoRA models, specifically designed to support systems with low VRAM, such as 12GB, 16GB, and 20GB GPUs. It combines the simplicity of a Gradio UI, forked from AI-Toolkit, with the powerful and flexible training capabilities of Kohya sd-scripts. This tool allows users to easily train custom LoRAs, including advanced features like automatic sample image generation and direct publishing to Hugging Face.
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
LivePortrait is an official PyTorch implementation for efficient portrait animation, bringing still images and videos to life with advanced stitching and retargeting control. It supports both human and animal subjects, offering various features like image-driven mode, regional control, and precise editing. Widely adopted by major video platforms, LivePortrait provides a robust solution for generating dynamic animated portraits.
Ollama-OCR: Advanced OCR with Vision Language Models via Ollama
Ollama-OCR is a robust Python package and Streamlit application for Optical Character Recognition. It leverages state-of-the-art vision language models, accessible through Ollama, to accurately extract text from both images and PDF documents. The tool offers extensive features including support for multiple models, various output formats, and batch processing capabilities.
AI Samples for .NET: Integrating AI into Your .NET Applications
The AI Samples for .NET repository provides a comprehensive collection of samples demonstrating how to integrate artificial intelligence into .NET applications. It features examples using Microsoft.Extensions.AI for unified API access to AI services and Microsoft.Extensions.AI.Evaluation for assessing LLM response quality. This resource is ideal for .NET developers looking to leverage AI, including large language models, in their projects.
chatterbox-vllm: Accelerating Chatterbox TTS with vLLM for Enhanced Performance
chatterbox-vllm is a high-performance port of the Chatterbox Text-to-Speech (TTS) model to vLLM, designed to significantly improve generation speed and GPU memory efficiency. This personal project aims to provide a more efficient and easily integratable solution for speech synthesis, offering substantial speedups compared to the original implementation. While currently usable and demonstrating benchmark-topping throughput, it leverages internal vLLM APIs and hacky workarounds, with ongoing refactoring planned.