JJJYmmm/Multimodal-RoPEs
Official implement of paper "Revisiting Multimodal Positional Encoding in Vision–Language Models", ICLR 2026
Official implement of paper "Revisiting Multimodal Positional Encoding in Vision–Language Models", ICLR 2026
Docs Hub for Qwen MM Plugins
Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation.
robot model inference in C/C++
LLM inference in C/C++
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
Make any agent harness multimodal-native.
About myself.
π₀ inference in C/C++, built on top of llama.cpp.
Simple Implementation of Pix2seqV2(multi-task)
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
dev
🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.
Run LLMs with MLX
A high-throughput and memory-efficient inference and serving engine for LLMs
This repository provides the BeingBeyond D1 SDK, example Python scripts, and basic guidance for environment setup and common first-time troubleshooting.
Minecraft AI with LLMs+Mineflayer
Being-H is BeingBeyond's family of human-centric embodied foundation models.
Common recipes to run vLLM
Qwen3.5 is the large language model series developed by Qwen team, Alibaba Cloud.
SGLang is a high-performance serving framework for large language models and multimodal models.
基于Netfilter的Linux状态检测防火墙,支持NAT。华中科技大学2023学年网络安全课程设计项目,参考https://github.com/RicheyJang/RJFireWall
Self-aiming and perspective tools for Assault Cube ver1.3.0.2
Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
Supplementary Materials for paper: "Stealthy and Effective Physical Adversarial Attacks in Autonomous Driving"
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework