Mistral-7B SFT Tutorial
Exciting news for AI enthusiasts! A new YouTube video is now available, showcasing the supervised fine-tuning (SFT) process of Mistral-7B using Hugging Face tooling and cloud-based GPU acceleration. In a previous video, an overview of the latest developments in open-source large language models (LLMs) was provided, highlighting releases such as Mistral AI's Mixtral-8x7B, which rival the capabilities of OpenAI's GPT-3.5. Now, with the availability of these models, fine-tuning them for specific tasks has become more accessible than ever. In this tutorial, the focus shifts to a deeper exploration of supervised fine-tuning. By renting an RTX 4090 GPU on RunPod, the video demonstrates how to fine-tune Mistral-7B to generate useful completions based on human instructions. This process, also known as instruction tuning, transforms the base model into a versatile chatbot or assistant capable of understanding and responding to a wide range of queries. Despite using the same cross-entropy loss function as during pre-training, the objective of supervised fine-tuning is to train the model to produce meaningful completions in response to human prompts. By following this approach, users can harness the full potential of large language models for practical applications. The tutorial concludes with a preview of future content, hinting at upcoming notebooks and videos focused on fine-tuning language models based on human preferences using the DPO (direct preference optimization) method. Don't miss out on this insightful tutorial, and stay tuned for more updates as the journey to create powerful AI assistants continues!