Stars
Bash script for installing V2Ray in operating systems such as Debian / CentOS / Fedora / openSUSE that support systemd
电报注册教程：2025年国内手机号注册Telegram收不到验证码怎么办？【已解决】本文还将会详细介绍如何下载、安装、注册和使用Telegram电报，并会为大家提供在中国使用Telegram电报的一些实用建议！
Clinical Histopathology Imaging Evaluation Foundation Model
AMEGA-LLM: Autonomous Medical Evaluation for Guideline Adherence of Large Language Models
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 500+ LLMs (Qwen3, Qwen3-MoE, Llama4, GLM4.5, InternLM3, DeepSeek-R1, ...) and 200+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, Llava, GLM4v, Ph…
Official implementation of MATPO: Multi-Agent Tool-Integrated Policy Optimization.
A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
Tumor Origin diffeRentiation from Cytologic Histology
[AAAI-2026] Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner
Understanding R1-Zero-Like Training: A Critical Perspective
PrePATH: A parallel toolkit for Preprocessing WSI
The Official Repo for Paper: Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.
MedResearcher-R1 is a deep research agent for medical scenarios, built on a knowledge-informed trajectory synthesis framework.
Tongyi Deep Research, the Leading Open-source Deep Research Agent
Ling-V2 is a MoE LLM provided and open-sourced by InclusionAI.
slime is an LLM post-training framework for RL Scaling.
Qwen-SAM is a reasoning-based segmentation model that integrates Qwen 2.5 VL 7B with the Segment Anything Model (SAM), enabling fine-grained visual segmentation from complex text prompts using LoRA…
VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo
ContextGem: Effortless LLM extraction from documents
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
Multilingual Document Layout Parsing in a Single Vision-Language Model