does anyone have an example of how they used metaflow in combination with langchain or an LLM? i'm specifically looking for what i need to do to check on a fine-tuning job to see when it completes and get the results.

Hey Emily. I'm not aware of any well-tested examples with Metaflow ready yet, although working on some examples to share soon. Generally I think the pattern will be similar to most model training/tuning with Metaflow where 1. To see when it completes / track progress, you can write a callback at the end of each epoch to notify you. you could also add a notification step after your code confirms the model trained successfully and send an alert to slack or an email. 2. to get the results, I assume you mean the model state. This is easier said than done with huge models, but I think the pattern is similar in that you likely want to write the state to disk somehow, and move it to cloud storage so the trained model is ready to get pulled by other flows/apps. Especially since training can be very expensive, it is useful to checkpoint model state often (e.g., another callback after each epoch). Curious to hear what your use cases are like and understand requirements you have for LLM workflows. Also, I'm not aware of any fine-tuning features in langchain, what aspects of langchain are you looking to use with Metaflow together?