This repo supports the paper "QLoRA: Efficient Finetuning of Quantized LLMs", an effort to democratize access to LLM research. QLoRA uses bitsandbytes for quantization and is integrated with Hugging Face's PEFT and transformers libraries.

GeoCalib is an algorithm for single-image calibration: it estimates the camera intrinsics and gravity direction from a single image only. By combining geometric optimization with deep learning, GeoCalib provides a more flexible and accurate calibration compared to previous approaches.

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.

⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡ [[Tech Report]](assets/llmzoo.pdf) technology gifted by the creator. For example, many pioneers have made great efforts to spread the use of light bulbs and vaccines to developing countries.

2025.12.09 Support Z-Image Turbo Standalone training is now supported. For details, please refer to the Standalone Environment Setup Repository.

This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu LongLLMLingua mitigates the 'lost in the middle' issue in LLMs, enhancing long-context information processing.

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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.

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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.

This repository is the reading list on Deep Learning for Mathematical Reasoning (DL4MATH). Contributors: Pan Lu @UCLA, Liang Qiu @UCLA, Wenhao Yu @Notre Dame, Sean Welleck @UW, Kai-Wei Chang @UCLA For more details, please refer to the paper: A Survey of Deep Learning for Mathematical Reasoning.

PowerInfer is a CPU/GPU LLM inference engine leveraging activation locality for your device. Project Kanban https://github.com/SJTU-IPADS/PowerInfer/assets/34213478/fe441a42-5fce-448b-a3e5-ea4abb43ba23 PowerInfer v.s.

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?

A Swift client library for interacting with the Ollama API. Pass "json" to get back a JSON string, or specify a full JSON Schema: The format parameter works with both chat and generate methods.

🔥 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.

𝕏 Follow me on X • 🤗 Hugging Face • 💻 Blog • 📙 LLM Engineer's Handbook The LLM course is divided into three parts: 1. 🧩 LLM Fundamentals is optional and covers fundamental knowledge about mathematics, Python, and neural networks.

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.