My Python scripts to make high-quality figures for publications in top AI conferences and journals.

One dashboard. An entire research team.

ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works…

Code for journal paper "Learning Dual Semantic Relations with Graph Attention for Image-Text Matching", TCSVT, 2020.

开源的端到端产品级通用智能体

Official implementation of X-Master, a general-purpose tool-augmented reasoning agent.

《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程

A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io

The Paper List of Large Multi-Modality Model (Perception, Generation, Unification), Parameter-Efficient Finetuning, Vision-Language Pretraining, Conventional Image-Text Matching for Preliminary Ins…

设备指纹 获取真实的IMEI AndroidId 序列号 MAC地址等，Android模拟器检测，检测Android模拟器 ，作为可信DeviceID，应对防刷需求等 Android设备指纹 How to detect Android emulator

Universal cross-platform tokenizers binding to HF and sentencepiece

手机品牌型号汇总 | Mobile Models | This repository is licensed under CC BY-NC-SA 4.0

Android 17 local LLM prototype with Jetpack Compose and ONNX Runtime for offline AI inference experiments.

Chinese and English multimodal conversational language model | 多模态中英双语对话语言模型

Android App监听自身卸载，反馈统计

⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF, SFT etc.

llm deploy project based mnn. This project has merged into MNN.

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

Inference code for Llama models

Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research

基于：https://github.com/lamster2018/EasyProtector 的UniApp原生插件封装

基于Python的开源量化交易平台开发框架

qq browser multimodal video similarity contest

"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)