# Jaejun Shim: Open-Weight Models and Datasets
Source: https://savrn.com/model-publishers/junshim
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Models

Model · Text generation

### [When2Think-1.5B](https://savrn.com/models/when2think-1-5b)

[Jaejun Shim](https://savrn.com/model-publishers/junshim)

When2Think-1.5B is a post-trained hybrid reasoning model that learns both whether to reason explicitly and how much reasoning to allocate to each problem. The model encourages direct answering on easier instances while preserving extended reasoning on harder ones. Unlike uniform length-compression methods, When2Think treats reasoning depth as an instance-adaptive resource. When2Think-1.5B is an RLVR-post-trained version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. The checkpoint learns two coupled decisions: 1. Whether to reason 2. How much to reason - Within THINK, adapt generated computation to the input rather than following a fixed or uniformly compressed length target. These…

Open weights mit 1.8B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/when2think-1-5b)

Model · Text generation

### [When2Think-ThinkOnly-1.5B](https://savrn.com/models/when2think-thinkonly-1-5b)

[Jaejun Shim](https://savrn.com/model-publishers/junshim)

When2Think-1.5B is a post-trained hybrid reasoning model that learns both whether to reason explicitly and how much reasoning to allocate to each problem. The model encourages direct answering on easier instances while preserving extended reasoning on harder ones. Unlike uniform length-compression methods, When2Think treats reasoning depth as an instance-adaptive resource. When2Think-1.5B is an RLVR-post-trained version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. The checkpoint learns two coupled decisions: 1. Whether to reason 2. How much to reason - Within THINK, adapt generated computation to the input rather than following a fixed or uniformly compressed length target. These…

Open weights mit 1.8B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/when2think-thinkonly-1-5b)

## Explore More

- [All model publishers](https://savrn.com/model-publishers)
- [The model directory](https://savrn.com/models)
- [The dataset directory](https://savrn.com/datasets)

## Source

- Listed from their public repositories, read 2026-10-07.
- [Hugging Face profile](https://huggingface.co/junshim)
- [How the hub is built](https://savrn.com/model-hub/methodology)