# Ant Ling — Why We Open-Sourced Ling-3.0 tiny & flash Base Models

- Company: Ant Ling (ant-ling.com)
- Announced: 2026-08-21
- Category: open-weight-release
- Coverage: not counted
- Announcement: yes
- Group: models
- Source: https://developer.ant-ling.com/en/blogs/ling-3.0-base-models
- Record: https://forck.live/items/24487-why-we-open-sourced-ling-3-0-tiny-flash-base-models
- Subject: Ling / Ring / Ming
- Open weights: yes
- Models affected: Ling-3.0-tiny, Ling-3.0-flash, Ling-3.0-tiny-base, Ling-3.0-flash-base

Ant Ling has open-sourced the completed Base Models of Ling-3.0-tiny and Ling-3.0-flash, along with pre-training and mid-training checkpoints, after a community member asked for a Base Model. The company says the Base Models are the point, and the earlier weights make them more useful by opening a training path rather than only its endpoint. Ling-3.0-tiny-base has 7.9B total parameters and 1.3B activated parameters, while Ling-3.0-flash-base has 124B total parameters and 5.1B activated parameters. For both models Ant Ling released a pre-training checkpoint, a mid-training checkpoint, and the completed Base Model, and after mid-training Ling-3.0 uses WSM, a checkpoint-merging approach that supports continued pre-training, dynamic data expansion, and offline exploration of different training trajectories. Ant Ling says the models are not instruction-aligned chat products and should not be deployed directly to end users without task-specific post-training, evaluation, and validation.

## Evidence

Verbatim from https://developer.ant-ling.com/en/blogs/ling-3.0-base-models:

> For both Ling-3.0-tiny and Ling-3.0-flash, we released a pre-training checkpoint, a mid-training checkpoint, and the completed Base Model. After mid-training, Ling-3.0 uses WSM—Warmup-Stable and Merge—a checkpoint-merging approach that supports continued pre-training, dynamic data expansion, and offline exploration of different training trajectories.

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