Preeti
Large language models (LLMs) in Artificial Intelligence are powerful but costly to run in cloud environments. Ollama is a tool that allows developers to run LLMs locally, reducing costs and simplifying development. By running LLMs locally, developers can save on cloud expenses, experiment faster, and maintain data privacy. Ollama can be set up by downloading and installing the tool and the desired LLM model, such as Llama3. With at least 16GB of RAM, developers can run LLMs locally and experiment with their ideas without incurring significant production costs.
Langtrace is an observability tool that complements Ollama, providing insights into LLM application performance and behavior. By integrating Langtrace with Ollama, developers can trace LLM calls, capture metadata, and optimize application performance. The process involves generating an API key from Langtrace, installing the Langtrace Python or Typescript SDK, and initializing the SDK to start tracing LLM calls. By combining Ollama’s local LLM capabilities with Langtrace’s observability features, developers can build and optimize LLM applications effectively.
In conclusion, Ollama and Langtrace together offer a powerful toolset for developing and optimizing LLM applications. By leveraging Ollama for local LLM development and integrating Langtrace for observability, developers can reduce costs, accelerate experimentation, and improve data privacy while gaining valuable insights into application performance. The combination of Ollama and Langtrace enables more efficient, effective, and innovative LLM application development.
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Source link: https://medium.com/langtrace/run-your-llm-apps-locally-using-ollama-and-debug-with-langtrace-af5f90ab424e?source=rss——openai-5
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