Spaces:
Running
Running
File size: 3,628 Bytes
cfd3735 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
{
"cells": [
{
"cell_type": "markdown",
"id": "73f9bf40",
"metadata": {},
"source": [
"# How to serialize LLM classes\n",
"\n",
"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)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9c9fb6ff",
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.llms.loading import load_llm"
]
},
{
"cell_type": "markdown",
"id": "88ce018b",
"metadata": {},
"source": [
"## Loading\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f12b28f3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\r\n",
" \"model_name\": \"text-davinci-003\",\r\n",
" \"temperature\": 0.7,\r\n",
" \"max_tokens\": 256,\r\n",
" \"top_p\": 1.0,\r\n",
" \"frequency_penalty\": 0.0,\r\n",
" \"presence_penalty\": 0.0,\r\n",
" \"n\": 1,\r\n",
" \"best_of\": 1,\r\n",
" \"request_timeout\": null,\r\n",
" \"_type\": \"openai\"\r\n",
"}"
]
}
],
"source": [
"!cat llm.json"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ab709fc",
"metadata": {},
"outputs": [],
"source": [
"llm = load_llm(\"llm.json\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "095b1d56",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_type: openai\r\n",
"best_of: 1\r\n",
"frequency_penalty: 0.0\r\n",
"max_tokens: 256\r\n",
"model_name: text-davinci-003\r\n",
"n: 1\r\n",
"presence_penalty: 0.0\r\n",
"request_timeout: null\r\n",
"temperature: 0.7\r\n",
"top_p: 1.0\r\n"
]
}
],
"source": [
"!cat llm.yaml"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8cafaafe",
"metadata": {},
"outputs": [],
"source": [
"llm = load_llm(\"llm.yaml\")"
]
},
{
"cell_type": "markdown",
"id": "ab3e4223",
"metadata": {},
"source": [
"## Saving\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b38f685d",
"metadata": {},
"outputs": [],
"source": [
"llm.save(\"llm.json\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b7365503",
"metadata": {},
"outputs": [],
"source": [
"llm.save(\"llm.yaml\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "68e45b1c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|