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GPT4All Monitoring

GPT4All integrates with OpenLIT open telemetry instrumentation to perform real-time monitoring of your LLM application and hardware.

Monitoring can enhance your GPT4All deployment with auto-generated traces for

  • performance metrics

  • user interactions

  • GPU metrics like utilization, memory, temperature, power usage

Setup Monitoring

Setup Monitoring

With OpenLIT, you can automatically monitor metrics for your LLM deployment:

pip install openlit
from gpt4all import GPT4All
import openlit

openlit.init()  # start
# openlit.init(collect_gpu_stats=True)  # or, start with optional GPU monitoring

model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf')

# Start a chat session and send queries
with model.chat_session():
    response1 = model.generate(prompt='hello', temp=0)
    response2 = model.generate(prompt='write me a short poem', temp=0)
    response3 = model.generate(prompt='thank you', temp=0)

    print(model.current_chat_session)

OpenLIT UI

Connect to OpenLIT's UI to start exploring performance metrics. Visit the OpenLIT Quickstart Guide for step-by-step details.

Grafana, DataDog, & Other Integrations

If you use tools like , you can integrate the data collected by OpenLIT. For instructions on setting up these connections, check the OpenLIT Connections Guide.