QwenPaw-Flash is a lightweight model deeply optimized for the QwenPaw autonomous agent scenario. Since its training phase, the model has been specifically refined for QwenPaw tasks, delivering enhanced agentic performance in tool invocation, command execution, memory management, and multi-step planning.
Capability
The core strength of QwenPaw-Flash stems from its native integration with the QwenPaw ecosystem. We have constructed extensive, high-quality agent trajectory data sampled from real QwenPaw environments, systematically enhancing the model's proficiency in high-frequency daily scenarios. Key features include:
Active Memory Management: Autonomously identifies, stores, and retrieves persistent user preferences and task states, ensuring high logical consistency across multi-turn interactions.
Native File Parsing: Optimized for terminal operations and file system orchestration. Excels at generating precise CLI commands and executing complex, multi-step file I/O tasks.
Efficient Information Search: Enhanced for web-search tool invocation. Features precise search intent recognition and multi-step web navigation to effectively identify and query online information.
Intelligent Guidance: Built-in awareness of the QwenPaw feature map. Proactively suggests functional paths and troubleshooting based on real-time operational context.
Model Overview
QwenPaw-Flash-2B/4B/9B is fine-tuned from Qwen3.5-2B/4B/9B, sharing the same architectural parameters.
The complexity of QwenPaw's context engineering and tool usage poses heightened challenges for model evaluation. To address this, we have developed a dedicated benchmark tailored to the QwenPaw environment. This benchmark systematically evaluates model performance across five high-frequency usage scenarios, covering key operational dimensions.
Results indicate that QwenPaw-Flash delivers substantial improvements across multiple task categories, achieving performance comparable to leading flagship models—all while maintaining significantly lower resource requirements.
Figure 1: QwenPaw-Flash-9B compared with other models.
Figure 2: QwenPaw-Flash-2B/4B/9B compared with their respective baseline models.
Quickstart
Serving QwenPaw-Flash
QwenPaw-Flash can be served via APIs using popular inference frameworks. Below are example commands to launch OpenAI-compatible API servers for QwenPaw-Flash.
vLLM
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Since QwenPaw-Flash leverages the Qwen3.5 architecture, the latest vLLM version is required for optimal compatibility. You can install it in a fresh environment using:
The following command creates API endpoints at http://localhost:8000/v1. This example demonstrates launching the standard version with a maximum context length of 262,144 tokens using tensor parallelism across 8 GPUs.
