Large language models (LLMs) on Databricks

Databricks makes it simple to access and build off of publicly available large language models.

Databricks Runtime for Machine Learning includes libraries like Hugging Face Transformers and LangChain that allow you to integrate existing pre-trained models or other open-source libraries into your workflow. From here, you can leverage Databricks platform capabilities to fine-tune LLMs using your own data for better domain performance.

Hugging Face Transformers

With Hugging Face Transformers on Databricks you can scale out your natural language processing (NLP) batch applications and fine-tune models for large-language model applications.

The Hugging Face transformers library comes preinstalled on Databricks Runtime 10.4 LTS ML and above. Many of the popular NLP models work best on GPU hardware, so you might get the best performance using recent GPU hardware unless you use a model specifically optimized for use on CPUs.

• What are Hugging Face Transformers?

• Fine-tune Hugging Face models for a single GPU

• Model inference using Hugging Face Transformers for NLP

DSPy

DSPy automates prompt tuning by translating user-defined natural language signatures into complete instructions and few-shot examples.

See Build genAI apps using DSPy on Databricks for examples on how to use DSPy.

LangChain

LangChain is available as an experimental MLflow flavor which allows LangChain customers to leverage the robust tools and experiment tracking capabilities of MLflow directly from the Databricks environment.

LangChain is a software framework designed to help create applications that utilize large language models (LLMs) and combine them with external data to bring more training context for your LLMs.

Databricks Runtime ML includes langchain in Databricks Runtime 13.1 ML and above.

Learn about Databricks specific LangChain integrations.