Fine-Tuning Your LLM to "Think" Like DeepSeek R1, on Your Computer
Experiments with SFT, Llama 3.2 3B, and Training Data Generated by DeepSeek R1
DeepSeek-R1 is a massive model that requires multiple high-end GPUs to run locally, making it impractical for most users. However, as we explored in a previous article, DeepSeek AI has also released distilled versions that are significantly smaller and based on more widely adopted models like Llama 3.1 and Qwen2.5.
The AI community is actively working on replicating DeepSeek-R1, and we are now seeing datasets generated by R1 that could be leveraged to train other models to "think" in a similar way.
With these datasets, fine-tuning existing LLMs to emulate R1’s reasoning becomes easier. In this article, we will explore how to fine-tune an adapter to enhance an LLM’s capabilities using community-generated R1 datasets. We will use supervised fine-tuning, a more accessible and cost-effective approach compared to the reinforcement learning method (GRPO) used by DeepSeek AI. This process is feasible on consumer-grade hardware, requiring only a 24GB GPU.
For demonstration purposes, we will fine-tune Llama 3.2 3B, though the same approach can be applied to other models like Qwen2.5 or Gemma 2. The fine-tuning follows a standard LoRA method but incorporates special considerations for embeddings and tokenization to better handle the “thinking” part.
The following notebook provides a step-by-step guide on fine-tuning Llama 3.2 3B to "think" like R1.