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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Petals LLM Example\n",
"This notebook goes over how to use Langchain with [Petals](https://github.com/bigscience-workshop/petals)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install petals\n",
"The `petals` package is required to use the Petals API. Install `petals` using `pip3 install petals`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"$ pip3 install petals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from langchain.llms import Petals\n",
"from langchain import PromptTemplate, LLMChain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set the Environment API Key\n",
"Make sure to get your API key from Huggingface."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"HUGGINGFACE_API_KEY\"] = \"YOUR_KEY_HERE\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the Petals instance\n",
"You can specify different parameters such as the model name, max new tokens, temperature, etc."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = Petals(model_name=\"bigscience/bloom-petals\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a Prompt Template\n",
"We will create a prompt template for Question and Answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initiate the LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the LLMChain\n",
"Provide a question and run the LLMChain."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
"version": "3.9.1"
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"vscode": {
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