GoonIntroduction to Qwen's QwQ 32B Reasoning Model
The release of Qwen's QwQ 32B reasoning model marks a significant milestone in the local reasoning model space. This article will delve into the details of this model, its creation, and how it can be utilized locally on personal computers. We will also explore the benchmarks and comparisons made with other models, such as the Deep Seek R1.
Qwen QwQ 32B Model Overview
Qwen had previously released a preview version of the QwQ 32B model, and it is likely that they were still refining the best approaches to Reinforcement Learning (RL) and exploring different ideas around this. The release of the Deep Seek R1 model probably also influenced the development of Qwen's QwQ Max preview. The QwQ 32B model is essentially their large model, which may not be open-sourced.
Benchmarks and Comparisons
The QwQ 32B model is compared to the Deep Seek R1 model, a 671B model, in the benchmarks. However, it's essential to note that the Deep Seek R1 is a mixed experts model, with only 37 billion parameters active at any moment. The QwQ 32B model, on the other hand, is a dense model with 32 billion parameters. The benchmarks show that the QwQ 32B model performs remarkably well, often surpassing the distilled versions of the Deep Seek R1 model.
RL Process and Training
The RL process used to train the QwQ 32B model involves two stages. The first stage uses outcome-based rewards, focusing on math and coding tasks with clear right or wrong answers. The second stage utilizes a trained reward model and rule-based verifiers to teach the model more general capabilities. Although the details of the RL process are not fully disclosed, it is clear that the QwQ 32B model has achieved impressive results.
Running the QwQ 32B Model Locally
To try out the QwQ 32B model, it can be downloaded from Hugging Face and run locally with multi-GPUs in Transformers. Alternatively, it can be run on Hugging Face Spaces or via Ollama. The model can also be tested using LM Studio, which provides a nice UI and the ability to easily play around with settings.
Conclusion
The release of Qwen's QwQ 32B reasoning model is a significant development in the local reasoning model space. With its impressive performance and ability to run locally, this model is an exciting option for those interested in exploring reasoning models. While there is still more to learn about the model and its training process, the results so far are promising, and it is definitely worth checking out.
Note: The images at timestamps 16 seconds, 2484 seconds, 12296 seconds, 18084 seconds, 23536 seconds, and 31088 seconds are not available, so they are not included in this article.
