B
Benj Edwards
On Monday, Chinese AI lab
released its new
under an open
, with its largest version containing 671 billion parameters. The company claims the model performs at levels comparable to
simulated reasoning (SR) model on several math and coding benchmarks.
Alongside the release of the main and models, DeepSeek published six smaller "DeepSeek-R1-Distill" versions ranging from 1.5 billion to 70 billion parameters. These distilled models are based on existing open source architectures like Qwen and Llama, trained using data generated from the full R1 model. The smallest version can run on a laptop, while the full model requires far more substantial computing resources.
The releases immediately caught the attention of the AI community because most existing open-weights models—which can often be run and fine-tuned on local hardware—have lagged behind proprietary models like OpenAI's o1 in so-called reasoning benchmarks. Having these capabilities available in an MIT-licensed model that anyone can study, modify, or use commercially potentially marks a shift in what's possible with publicly available AI models.
Alongside the release of the main and models, DeepSeek published six smaller "DeepSeek-R1-Distill" versions ranging from 1.5 billion to 70 billion parameters. These distilled models are based on existing open source architectures like Qwen and Llama, trained using data generated from the full R1 model. The smallest version can run on a laptop, while the full model requires far more substantial computing resources.
The releases immediately caught the attention of the AI community because most existing open-weights models—which can often be run and fine-tuned on local hardware—have lagged behind proprietary models like OpenAI's o1 in so-called reasoning benchmarks. Having these capabilities available in an MIT-licensed model that anyone can study, modify, or use commercially potentially marks a shift in what's possible with publicly available AI models.
