Designed for research exploration and industrial prototyping, UltraRAG standardizes core RAG components (Retriever, Generation, etc.) as independent MCP Servers, combined with the powerful workflow orchestration capabilities of the MCP Client. Developers can achieve precise orchestration of complex control structures such as conditional branches and loops simply through YAML configuration.
MedSegDiff is a Diffusion Probabilistic Model (DPM) based framework for the Segmentation and Reconstruction of organs/tissues from the medical images. The algorithm is elaborated on our paper MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer.
A self-contained image bundles the tool + the Claude Code and Codex CLIs. You log in once and the sessions persist in named volumes — no re-login on later runs.
2025.12.09 Support Z-Image Turbo Standalone training is now supported. For details, please refer to the Standalone Environment Setup Repository.
With MLX-LM-LoRA you can, train Large Language Models locally on Apple Silicon using MLX. Training works with all models supported by MLX-LM, including: Training Types: Training Algorithms: Quantization Aware Training (QAT): Training Your Custom Preference Model: --- The main command is mlxlmlora.train.
figure:: img/mainpage/subscribe.gif :target: https://machinelearningmindset.com/subscription/ Slack Group .. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/astorfi/Deep-Learning-World/pulls ..
Copyright 2025-2026 Ardan Labs hello@ardanlabs.com https://kronkai.com This project lets you use Go for hardware accelerated local inference with llama.cpp and whisper.cpp directly integrated into your Go applications via the yzma and bucky modules. Kronk provides a high-level API that feels similar to using an OpenAI compatible API.
Augustus is a Go-based LLM vulnerability scanner for security professionals. It tests large language models against a wide range of adversarial attacks, integrates with 28 LLM providers, and produces actionable vulnerability reports.
image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/osforscience/deep-learning-all-you-need/pulls .. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103 :target: https://github.com/ellerbrock/open-source-badge/ ..
This project aims at building a universal toolkit for extracting events automatically from documents 📄 (long texts). The details can be found in our paper: Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Yuan, Min Zhang.
The WebUI extension for ControlNet and other injection-based SD controls. This extension is for AUTOMATIC1111's Stable Diffusion web UI, allows the Web UI to add ControlNet to the original Stable Diffusion model to generate images.
AI Audio Datasets (AI-ADS) 🎵, including Speech, Music, and Sound Effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications.
This repo contains the source code of the Python package loralib and several examples of how to integrate it with PyTorch models, such as those in Hugging Face. See our paper for a detailed description of LoRA.
A research and development fork of llama.cpp, providing unique KV cache codecs, inference techniques, and bleeding edge features. Why pay 3-bit or 4-bit quality for a context length you only sometimes reach?
🚅 LiteLLM LiteLLM AI Gateway Open Source AI Gateway for 100+ LLMs. LiteLLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier | Website --- LiteLLM is an open source AI Gateway that gives you a single, unified interface to call 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure, and more — using the OpenAI format.
🔥 Large Language Models(LLM) have taken the ~~NLP community~~ ~~AI community~~ the Whole World by storm. Here is a curated list of papers about large language models, especially relating to ChatGPT.
🤗 PEFT State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters.
Deep Lake: Database for AI Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Storing and searching data plus vectors while building LLM applications 2.
This newer video covers the an updated 2024 version of the state of MLOps. You can join the Machine Learning Engineer newsletter.
This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today.
Designed for research exploration and industrial prototyping, UltraRAG standardizes core RAG components (Retriever, Generation, etc.) as independent MCP Servers, combined with the powerful workflow orchestration capabilities of the MCP Client. Developers can achieve precise orchestration of complex control structures such as conditional branches and loops simply through YAML configuration.
MedSegDiff is a Diffusion Probabilistic Model (DPM) based framework for the Segmentation and Reconstruction of organs/tissues from the medical images. The algorithm is elaborated on our paper MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer.
A self-contained image bundles the tool + the Claude Code and Codex CLIs. You log in once and the sessions persist in named volumes — no re-login on later runs.
2025.12.09 Support Z-Image Turbo Standalone training is now supported. For details, please refer to the Standalone Environment Setup Repository.
With MLX-LM-LoRA you can, train Large Language Models locally on Apple Silicon using MLX. Training works with all models supported by MLX-LM, including: Training Types: Training Algorithms: Quantization Aware Training (QAT): Training Your Custom Preference Model: --- The main command is mlxlmlora.train.
figure:: img/mainpage/subscribe.gif :target: https://machinelearningmindset.com/subscription/ Slack Group .. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/astorfi/Deep-Learning-World/pulls ..
Copyright 2025-2026 Ardan Labs hello@ardanlabs.com https://kronkai.com This project lets you use Go for hardware accelerated local inference with llama.cpp and whisper.cpp directly integrated into your Go applications via the yzma and bucky modules. Kronk provides a high-level API that feels similar to using an OpenAI compatible API.
Augustus is a Go-based LLM vulnerability scanner for security professionals. It tests large language models against a wide range of adversarial attacks, integrates with 28 LLM providers, and produces actionable vulnerability reports.
image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/osforscience/deep-learning-all-you-need/pulls .. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103 :target: https://github.com/ellerbrock/open-source-badge/ ..
This project aims at building a universal toolkit for extracting events automatically from documents 📄 (long texts). The details can be found in our paper: Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Yuan, Min Zhang.
The WebUI extension for ControlNet and other injection-based SD controls. This extension is for AUTOMATIC1111's Stable Diffusion web UI, allows the Web UI to add ControlNet to the original Stable Diffusion model to generate images.
AI Audio Datasets (AI-ADS) 🎵, including Speech, Music, and Sound Effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications.
This repo contains the source code of the Python package loralib and several examples of how to integrate it with PyTorch models, such as those in Hugging Face. See our paper for a detailed description of LoRA.
A research and development fork of llama.cpp, providing unique KV cache codecs, inference techniques, and bleeding edge features. Why pay 3-bit or 4-bit quality for a context length you only sometimes reach?
🚅 LiteLLM LiteLLM AI Gateway Open Source AI Gateway for 100+ LLMs. LiteLLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier | Website --- LiteLLM is an open source AI Gateway that gives you a single, unified interface to call 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure, and more — using the OpenAI format.
🔥 Large Language Models(LLM) have taken the ~~NLP community~~ ~~AI community~~ the Whole World by storm. Here is a curated list of papers about large language models, especially relating to ChatGPT.
🤗 PEFT State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters.
Deep Lake: Database for AI Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Storing and searching data plus vectors while building LLM applications 2.
This newer video covers the an updated 2024 version of the state of MLOps. You can join the Machine Learning Engineer newsletter.
This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today.