0 Fork 0

mirror of https://github.com/langchain-ai/langchain.git synced 2026-10-08 10:55:18 +03:00

Files

T

Harrison ChaseandShreya Rajpal 985496f4be Docs refactor (#480 )

Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>

2023-01-02 08:24:09 -08:00

3.5 KiB

Raw Blame History

LLM Serialization

This notebook walks 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 (eg 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 a 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 a 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")

In [ ]:

3.5 KiB Raw Blame History

LLM Serialization

Loading

Saving

3.5 KiB

Raw Blame History