I built an open-source plugin in OptiLLM that implements Google's "Deep Think" reasoning approach for local models like DeepSeek R1 and Qwen3.

Google's recent Gemini 2.5 report introduced Deep Think - a technique where models generate multiple hypotheses in parallel and critique them before arriving at final answers. It achieves SOTA results on math olympiads and competitive coding benchmarks.

The plugin works by modifying the inference pipeline to explore multiple solution paths simultaneously, then synthesizing the best approach. Instead of single-pass generation, the model essentially runs an internal debate before responding.

Technical details:

- Works with any model that supports structured reasoning patterns

- Implements parallel thinking during response generation - Particularly effective for complex reasoning tasks, math, and coding problems

- Increases inference time but significantly improves answer quality

Link: https://github.com/codelion/optillm/tree/main/optillm/plugin...

Demo: https://www.youtube.com/watch?v=b06kD1oWBA4

The implementation won the Cerebras & OpenRouter Qwen 3 Hackathon, but more importantly, it's now available for anyone running local models.

Questions for HN:

- Has anyone tried similar parallel reasoning approaches with local models?

- What other proprietary techniques do you think would be valuable to open-source?

- Any suggestions for optimizing the performance trade-offs?

The goal is to democratize advanced reasoning capabilities that were previously locked behind APIs. Would love feedback on the approach and ideas for improvements.