How to serialize LLM classes#

This notebook walks through how to write and read an LLM Configuration to and from disk. This is useful if you want to save the configuration for a given LLM (e.g., the provider, the temperature, etc).

from langchain.llms import OpenAI
from langchain.llms.loading import load_llm


First, lets go over loading an LLM from disk. LLMs can be saved on disk in two formats: json or yaml. No matter the extension, they are loaded in the same way.

!cat llm.json
    "model_name": "text-davinci-003",
    "temperature": 0.7,
    "max_tokens": 256,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 0.0,
    "n": 1,
    "best_of": 1,
    "request_timeout": null,
    "_type": "openai"
llm = load_llm("llm.json")
!cat llm.yaml
_type: openai
best_of: 1
frequency_penalty: 0.0
max_tokens: 256
model_name: text-davinci-003
n: 1
presence_penalty: 0.0
request_timeout: null
temperature: 0.7
top_p: 1.0
llm = load_llm("llm.yaml")


If you want to go from an LLM in memory to a serialized version of it, you can do so easily by calling the .save method. Again, this supports both json and yaml."llm.json")"llm.yaml")