forked from Archives/langchain
985496f4be
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>
169 lines
3.5 KiB
Plaintext
169 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "73f9bf40",
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"metadata": {},
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"source": [
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"# LLM Serialization\n",
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"\n",
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"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)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9c9fb6ff",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.llms.loading import load_llm"
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]
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},
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{
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"cell_type": "markdown",
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"id": "88ce018b",
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"metadata": {},
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"source": [
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"## Loading\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f12b28f3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\r\n",
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" \"model_name\": \"text-davinci-003\",\r\n",
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" \"temperature\": 0.7,\r\n",
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" \"max_tokens\": 256,\r\n",
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" \"top_p\": 1.0,\r\n",
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" \"frequency_penalty\": 0.0,\r\n",
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" \"presence_penalty\": 0.0,\r\n",
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" \"n\": 1,\r\n",
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" \"best_of\": 1,\r\n",
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" \"request_timeout\": null,\r\n",
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" \"_type\": \"openai\"\r\n",
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"}"
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]
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}
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],
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"source": [
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"!cat llm.json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9ab709fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = load_llm(\"llm.json\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "095b1d56",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"_type: openai\r\n",
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"best_of: 1\r\n",
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"frequency_penalty: 0.0\r\n",
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"max_tokens: 256\r\n",
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"model_name: text-davinci-003\r\n",
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"n: 1\r\n",
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"presence_penalty: 0.0\r\n",
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"request_timeout: null\r\n",
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"temperature: 0.7\r\n",
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"top_p: 1.0\r\n"
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]
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}
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],
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"source": [
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"!cat llm.yaml"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "8cafaafe",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = load_llm(\"llm.yaml\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ab3e4223",
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"metadata": {},
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"source": [
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"## Saving\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b38f685d",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm.save(\"llm.json\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "b7365503",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm.save(\"llm.yaml\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "68e45b1c",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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