langchain/docs/modules/models/llms/examples/llm_serialization.ipynb

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).

In [1]:

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

Loading

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.

In [2]:

!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"
}

In [3]:

llm = load_llm("llm.json")

In [4]:

!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

In [5]:

llm = load_llm("llm.yaml")

Saving

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.

In [6]:

llm.save("llm.json")

In [7]:

llm.save("llm.yaml")

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