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Fine-tuning Open Source LLMs with Mistral

Andrea, a Computing Engineer at CERN, and Josep, a Data Scientist at the Catalan Tourist Board, will walk you through the steps needed to customize the open-source Mistral LLM.
Josep Ferrer
Andrea Valenzuela
Josep Ferrer, Andrea Valenzuela

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Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.

While cutting-edge large language models can write almost any text you like, they are expensive to run. You can get the same performance for less money by using a smaller model and fine-tuning it to your needs.

In this session, Andrea, a Computing Engineer at CERN, and Josep, a Data Scientist at the Catalan Tourist Board, will walk you through the steps needed to customize the open-source Mistral LLM. You'll learn about choosing a suitable LLM, getting training data, tokenization, evaluating model performance, and best practices for fine-tuning.

Key Takeaways:

  • Learn how to fine-tune a large language model using the Hugging Face Python ecosystem.
  • Learn about the steps to prepare for fine-tuning and how to evaluate your success.
  • Learn about best practices for fine-tuning models.

Resources (including links to notebooks)

주제

Machine LearningArtificial IntelligenceData Scientist

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