Projects

Project Overview

LuwuLLM is a lightweight large language model project focused on providing high-quality Chinese language understanding capabilities in resource-constrained environments.

Core Features

• Lightweight Design: Optimized model parameters suitable for edge device deployment
• Chinese Optimization: Deep training on Chinese corpora for more accurate understanding
• Fast Inference: Optimized inference engine with quick response times
• Easy Integration: Simple API interface

Tech Stack

• PyTorch
• Transformers
• ONNX Runtime
• FastAPI

Use Cases

• Intelligent customer service
• Text summarization
• Question answering systems
• Content generation

Project Status

🚧 In Development - Beta version expected Q1 2026

Project Overview

VisionPlus is an advanced multimodal visual understanding system capable of simultaneously processing images, videos, and text information to provide comprehensive visual intelligence solutions.

Core Functions

Image Understanding

• Object detection and recognition
• Scene understanding
• Image caption generation

Video Analysis

• Action recognition
• Event detection
• Video summarization

Multimodal Fusion

• Image-text matching
• Visual question answering
• Cross-modal retrieval

Technical Highlights

✨ Unified Architecture - Single model handles multiple vision tasks
⚡ Efficient Inference - Optimized model structure for real-time processing
🎯 High Accuracy - Achieves SOTA performance on multiple benchmark datasets
🔧 Flexible Deployment - Supports both cloud and edge devices

Project Overview

RLAgent is a universal reinforcement learning research framework providing a complete toolchain from algorithm research to practical deployment.

Framework Features

🧩 Modular Design

• Standardized environment interface
• Pluggable algorithm components
• Configurable training pipeline

🎮 Rich Algorithm Library

• DQN / DDPG / SAC
• PPO / A3C
• Multi-agent algorithms
• Model-based RL

📊 Visualization Tools

• Real-time training monitoring
• Policy visualization
• Performance analysis

🚀 Distributed Training

• Multi-GPU parallel processing
• Distributed sampling
• Experience replay management

Supported Environments

• OpenAI Gym
• MuJoCo
• Unity ML-Agents
• Custom environments

Typical Applications

1. Robot Control - Robotic arm manipulation, drone flight
2. Game AI - Strategy games, real-time combat
3. Resource Optimization - Task scheduling, path planning
4. Automated Trading - Quantitative strategy optimization

Quick Start

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from rlagent import Agent, Environment

env = Environment('CartPole-v1')
agent = Agent('PPO', env)
agent.train(episodes=1000)

Project Status

✅ Stable Release - v1.0 Available