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Using Opik with Predibase

Predibase is a platform for fine-tuning and serving open-source Large Language Models (LLMs). It's built on top of open-source LoRAX.

You can check out the Colab Notebook if you'd like to jump straight to the code: Open In Colab

Tracking your LLM calls​

Predibase can be used to serve open-source LLMs and is available as a model provider in LangChain. We will leverage the Opik integration with LangChain to track the LLM calls made using Predibase models.

Getting started​

To use the Opik integration with Predibase, you'll need to have both the opik, predibase and langchain packages installed. You can install them using pip:

pip install --upgrade --quiet opik predibase langchain

You can then configure Opik using the opik configure command which will prompt you for the correct local server address or if you are using the Cloud platfrom your API key:

opik configure

You will also need to set the PREDIBASE_API_TOKEN environment variable to your Predibase API token:

export PREDIBASE_API_TOKEN=<your-predibase-api-token>

Tracing your Predibase LLM calls​

In order to use Predibase through the LangChain interface, we will start by creating a Predibase model. We will then invoke the model with the Opik tracing callback:

import os
from langchain_community.llms import Predibase
from opik.integrations.langchain import OpikTracer

model = Predibase(
    model="mistral-7b",
    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
)

# Test the model with Opik tracing
response = model.invoke(
    "Can you recommend me a nice dry wine?",
    config={
        "temperature": 0.5,
        "max_new_tokens": 1024,
        "callbacks": [OpikTracer(tags=["predibase", "mistral-7b"])]
    }
)
print(response)
tip

You can learn more about the Opik integration with LangChain in our LangChain integration guide or in the Predibase cookbook.

The trace will now be available in the Opik UI for further analysis.

predibase

Tracking your fine-tuning training runs​

If you are using Predibase to fine-tune an LLM, we recommend using Predibase's integration with Comet's Experiment Management functionality. You can learn more about how to set this up in the Comet integration guide in the Predibase documentation. If you are already using an Experiment Tracking platform, worth checking if it has an integration with Predibase.