CoPaw is a personal AI assistant designed for easy installation and deployment, whether on your local machine or in the cloud. It supports multiple chat applications and offers easily extensible capabilities. Core features include broad channel support (DingTalk, Feishu, QQ, Discord, iMessage, and more), user control over memory and personalization, and built-in skills with the ability to create custom ones.
CoPaw enables various use cases, from social digests and productivity tools to creative writing and research assistance. It's a versatile teammate for your digital life, aiming to be a helpful "co-paw" by your side.
CoPaw enables various use cases, from social digests and productivity tools to creative writing and research assistance. It's a versatile teammate for your digital life, aiming to be a helpful "co-paw" by your side.
Examples for common OpenSandbox use cases. Each subdirectory contains runnable code and documentation. Integrations and sandboxes are available for various tools and services like AI models, desktop environments, and web scraping.
OpenSandbox provides a secure and isolated runtime environment for running commands, filesystems, code interpreters, browsers, and developer tools. It offers multi-language SDKs, unified APIs, and supports various AI workloads like coding agents, browser automation, remote development, AI code execution, and RL training.
OpenSandbox is a general-purpose sandbox platform for AI applications, offering multi-language SDKs, unified sandbox APIs, and Docker/Kubernetes runtimes for scenarios like Coding Agents, GUI Agents, Agent Evaluation, AI Code Execution, and RL Training.
Qwen3.5-27B is a powerful, multimodal language model designed for versatility and efficiency. It excels in tasks requiring reasoning, coding, and visual understanding thanks to its unified vision-language foundation and efficient architecture utilizing Gated Delta Networks and sparse Mixture-of-Experts. The model supports 201 languages and boasts a native 262,144 token context window, expandable to 1,010,000.
**Key Specs:**
* **Model Type:** Causal Language Model with Vision Encoder, 27 Billion Parameters
* **Architecture:** 64 Layers, 5120 Hidden Dimension
* **Training:** Scalable Reinforcement Learning for real-world adaptability.
**Performance Highlights:** Qwen3.5-27B demonstrates strong performance across a broad spectrum of benchmarks, including: **Knowledge & Reasoning** (MMLU, C-Eval, HLE, GPQA), **Instruction Following & General Agent Capabilities** (IFEval, IFBench, BFCL-V4, TAU2-Bench), **Coding** (SWE-bench, CodeForces), **Long Context Handling** (AA-LCR, LongBench v2), **Vision-Language Understanding** (MMMU, RealWorldQA), and **Multilingual Abilities** (MMMLU, WMT24++).
**Usage & Deployment:**
The model can be served and utilized through several frameworks: **SGLang & vLLM** (for fast, high-throughput inference with features like Multi-Token Prediction), **KTransformers & Hugging Face Transformers** (offering flexibility and lightweight testing options), and a **Chat Completions API** (with OpenAI SDK examples for various input types).
**Key Considerations:**
* Operates in "thinking mode" by default (intermediate thought processes), which can be disabled.
* Well-suited for agent applications, particularly with the Qwen-Agent framework.
* Documentation provides details on API configuration and recommended sampling parameters.
**Key Specs:**
* **Model Type:** Causal Language Model with Vision Encoder, 27 Billion Parameters
* **Architecture:** 64 Layers, 5120 Hidden Dimension
* **Training:** Scalable Reinforcement Learning for real-world adaptability.
**Performance Highlights:** Qwen3.5-27B demonstrates strong performance across a broad spectrum of benchmarks, including: **Knowledge & Reasoning** (MMLU, C-Eval, HLE, GPQA), **Instruction Following & General Agent Capabilities** (IFEval, IFBench, BFCL-V4, TAU2-Bench), **Coding** (SWE-bench, CodeForces), **Long Context Handling** (AA-LCR, LongBench v2), **Vision-Language Understanding** (MMMU, RealWorldQA), and **Multilingual Abilities** (MMMLU, WMT24++).
**Usage & Deployment:**
The model can be served and utilized through several frameworks: **SGLang & vLLM** (for fast, high-throughput inference with features like Multi-Token Prediction), **KTransformers & Hugging Face Transformers** (offering flexibility and lightweight testing options), and a **Chat Completions API** (with OpenAI SDK examples for various input types).
**Key Considerations:**
* Operates in "thinking mode" by default (intermediate thought processes), which can be disabled.
* Well-suited for agent applications, particularly with the Qwen-Agent framework.
* Documentation provides details on API configuration and recommended sampling parameters.
Alibaba has released CoPaw, an open-source framework designed to provide a standardized workstation for deploying and managing personal AI agents. It addresses the shift from LLM inference to autonomous agentic systems, focusing on the environment in which models operate. CoPaw utilizes AgentScope, AgentScope Runtime, and ReMe to handle agent logic, execution, and persistent memory, enabling long-term experience and multi-channel connectivity.
Zvec is an open-source, in-process vector database — lightweight, lightning-fast, and designed to embed directly into applications. Built on Proxima (Alibaba's battle-tested vector search engine), it delivers production-grade, low-latency, scalable similarity search with minimal setup.
The article details the release of Qwen3-Coder-Next, a new 80-billion-parameter open-source large language model (LLM) from Alibaba’s Qwen team. This model is designed for coding tasks and utilizes an ultra-sparse Mixture-of-Experts (MoE) architecture, activating only 3 billion parameters at a time for increased efficiency. It boasts a massive 262,144 token context window and innovative techniques like Gated DeltaNet and Best-Fit Packing to overcome traditional LLM limitations. Qwen3-Coder-Next was trained using an "agentic training" pipeline, learning from real-world coding scenarios and feedback. It supports 370 programming languages and demonstrates competitive performance against leading models like OpenAI’s Codex and Anthropic’s Claude, while also exhibiting strong security features. The release is positioned as a significant advancement in open-weight AI and a challenge to proprietary coding models.
Alibaba’s Qwen team released the Qwen 3 model family, offering a range of sizes and capabilities. The article discusses the model's features, performance, and the well-coordinated release across the LLM ecosystem, highlighting the trend of better models running on the same hardware.
This document details how to run Qwen models locally using the Text Generation Web UI (oobabooga), covering installation, setup, and launching the web interface.
