# Alibaba Tongyi Open-Sources Qwen-Scope, an Interpretability Toolkit for Large Models

- Category: Flash (快讯)
- Published: 2026-05-03T11:52:00.000Z
- Source date: 2026-05-03
- HTML: https://www.88z.ai/en/news/flash-20260503-04bb98bdc93e8424
- Markdown: https://www.88z.ai/en/news/flash-20260503-04bb98bdc93e8424.md
- Other language: https://www.88z.ai/news/flash-20260503-04bb98bdc93e8424.md

Alibaba Tongyi has open-sourced Qwen-Scope, an interpretability toolkit for large models based on sparse autoencoder technology. It covers seven models in the Qwen3 and Qwen3.5 series, enabling targeted output control during inference, data classification, long-tail sample synthesis, and optimization of language mixing and repetitive generation issues.

## At a glance

- Covers seven Qwen3 and Qwen3.5 models using SAE technology
- Supports inference control, long-tail sample synthesis, and anomaly localization

## The update

Alibaba Tongyi has officially open-sourced Qwen-Scope, an interpretability toolkit for large models. Built on sparse autoencoder (SAE) technology, it comprehensively covers a total of seven models in the Qwen3 and Qwen3.5 series. Qwen-Scope enables targeted control over output results during the inference phase. It can efficiently perform data classification tasks and synthesize long-tail samples using a small amount of seed data. Furthermore, the toolkit supports locating anomalous features, helping to optimize issues such as language mixing and repetitive content generation during model operation, thereby enhancing the controllability and quality of model outputs.

## Sources

- [千问大模型](https://mp.weixin.qq.com/s/3y3cjMJG070RmqZReE6j2A) (2026-05-03)

This article is an AI-assisted synthesis by 88Z AI based on the public sources below. Product claims and availability are attributed to their publishers. It does not represent independent testing or first-hand reporting.