Fine-Tuning DeepSeek R1 on Reasoning Task with Unsloth [Part 1]

Author(s): Youssef Hosni

Originally published on Towards AI.

Hands-On Fine-Tuning DeepSeek on Medical Reasoning Dataset

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DeepSeek company recently released DeepSeek-R1, the next step in their work on reasoning models. It’s an upgrade from their earlier DeepSeek-R1-Lite-Preview and shows they’re serious about competing with OpenAI’s o1.

In this two-part hands-on tutorial, we will fine-tune the DeepSeek-R1-Distill-Llama-8B model on the Medical Chain-of-Thought Dataset from Hugging Face using Unsloth.

In the first part, we will be introduced to the DeepSeek R1 model and set the working environment, then download the model and the tokenizer and finally test the model with zero shot inference and observe the result without fine-tuning.

Introduction to DeepSeek R1 Model [Part 1]Setting Up Working Environment [Part 1]Loading the Model & Tokenizer with Unsloth.ai [Part 1]Test the Model with Zero Shot Inference [Part 1]Loading and Processing the Dataset [Part 2]Fine — Tune the LLM [Part 2]Model Inference After Fine-Tuning [Part 2]Saving the model locally & Hugging Face Hub [Part 2]

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Published via Towards AI