DeepSeek-R1
DeepSeek-R1 is a family of open AI reasoning models trained with reinforcement learning to think step-by-step through maths and code, matching OpenAI-o1 performance, with smaller distilled versions for less powerful hardware.
DeepSeek-R1 is the public release of a family of large language models built by DeepSeek AI that are designed to be good at step-by-step reasoning — solving maths problems, writing code, and working through long chains of thought before producing an answer. The repository contains documentation, evaluation results, an accompanying paper, and links to download the actual model weights from Hugging Face. It does not contain the model itself as code; the heavy machine-learning weights live separately and are loaded by other software.
The README describes the family in two parts. The first is the post-training method: rather than the usual approach of teaching the model with curated example answers (supervised fine-tuning) before reinforcement learning, the team applied reinforcement learning directly to a base model. The result, called DeepSeek-R1-Zero, learned to produce long chains of thought and self-check its answers, but suffered from issues like repetition and language mixing. DeepSeek-R1 adds "cold-start" data and additional stages to fix those issues. According to the README, DeepSeek-R1 reaches performance comparable to OpenAI-o1 on maths, code, and reasoning benchmarks. The second part is distillation: the team used data produced by DeepSeek-R1 to fine-tune smaller open-source models in sizes ranging from 1.5B up to 70B parameters, so users with less computing power can still benefit.
Someone might use this repository to download the weights, run the models locally, study the paper, or fine-tune the smaller distilled checkpoints for specific tasks. The project is released under the MIT licence.
Where it fits
- Download and run a smaller distilled DeepSeek-R1 model locally to answer maths problems with step-by-step reasoning.
- Fine-tune a distilled checkpoint on your own dataset to build a specialised reasoning assistant.
- Study the paper and training methodology to understand how reinforcement learning can replace supervised fine-tuning for reasoning.
- Benchmark the 70B model against other open models for code generation tasks.