How to Get Responses From Local LLM Models With Python | HackerNoon
Briefly

from Hackernoon1 year ago
This article guides users in setting up a Python interface to interact with a local LLM instance established with LM Studio. It details necessary steps starting from confirming the local LLM system is running, identifying the local server address, and utilizing supported RESTful API endpoints to list available models and send prompts. The article illustrates how to retrieve model data and generate responses from the LLM, highlighting the difference between single prompts and conversational interactions, with practical Python code examples provided throughout.
To interact with your local LLM using Python, ensure the LLM system is running and utilize the provided RESTful API endpoints.
Hackernoonhttps://hackernoon.com/how-to-get-responses-from-local-llm-models-with-python
The /v1/models endpoint helps retrieve available models, allowing users to create an effective interface for their local LLM.
Hackernoonhttps://hackernoon.com/how-to-get-responses-from-local-llm-models-with-python
Endpoints for various interactions include /v1/completions for single prompts and /v1/chat/completions for conversational context.
Hackernoonhttps://hackernoon.com/how-to-get-responses-from-local-llm-models-with-python
Use Python's requests library to programmatically access your local LLM's RESTful API for generating completions.
Hackernoonhttps://hackernoon.com/how-to-get-responses-from-local-llm-models-with-python
Read at Hackernoon
#local-llm #python-interface #api #lm-studio #restful-api
[
|
]