Sophia Yang discusses best practices for fine-tuning Mistral models. We will cover topics like: (1) The permissive Mistral ToS and how it's perfect for fine tuning smaller models from bigger ones (2) How should people collect data (3) Domain specific evals (4) Use cases & examples (5) Common mistakes
This is a talk from Mastering LLMs: A survey course on applied topics for Large Language Models.
For more info and resources related to this talk, see:: parlance-labs.com/talks/fine_...
My personal site: hamel.dev/
My twitter: x.com/HamelHusain
Parlance Labs: parlance-labs.com/
00:00 Introduction
Sophia Yang introduces herself and provides an overview of the talk, which will cover Mistral models, their fine-tuning API, and demos.
0:35 Mistral's History and Model Offerings
Sophia discusses Mistral's history, from their founding to the release of various models, including open-source and enterprise-grade models, as well as specialized models like CodeStraw.
02:52 Customization and Fine-Tuning
Mistral recently released a fine-tuning codebase and API, allowing users to customize their models using LoRa fine-tuning. Sophia compares the performance of LoRa fine-tuning to full fine-tuning.
04:22 Prompting vs. Fine-Tuning
Sophia discusses the advantages and use cases for prompting and fine-tuning, emphasizing the importance of considering prompting before fine-tuning for specific tasks.
05:35 Fine-Tuning Demos
Sophia demonstrates how to use fine-tuned models shared by colleagues, as well as models fine-tuned on specific datasets like research paper abstracts and medical chatbots.
10:57 Developer Examples and Real-World Use Cases
Sophia showcases real-world examples of startups and developers using Mistral's fine-tuning API for various applications, such as information retrieval, medical domain, and legal co-pilots.
12:09 Using Mistral's Fine-Tuning API
Sophia walks through an end-to-end example of using Mistral's Fine-Tuning API on a custom dataset, including data preparation, uploading, creating fine-tuning jobs, and using the fine-tuned model.
19:10 Open-Source Fine-Tuning with Mistral
Sophia demonstrates how to fine-tune Mistral models using their open-source codebase, including installing dependencies, preparing data, and running the training process locally.