langchain/docs/extras/integrations/llms/gpt4all.ipynb

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"# GPT4All\n",
"\n",
"[GitHub:nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.\n",
"\n",
"This example goes over how to use LangChain to interact with `GPT4All` models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install gpt4all > /dev/null"
]
},
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"attachments": {},
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"source": [
"### Import GPT4All"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain\n",
"from langchain.llms import GPT4All\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
]
},
{
"attachments": {},
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"metadata": {},
"source": [
"### Set Up Question to pass to LLM"
]
},
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"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"attachments": {},
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"source": [
"### Specify Model\n",
"\n",
"To run locally, download a compatible ggml-formatted model. \n",
" \n",
"The [gpt4all page](https://gpt4all.io/index.html) has a useful `Model Explorer` section:\n",
"\n",
"* Select a model of interest\n",
"* Download using the UI and move the `.bin` to the `local_path` (noted below)\n",
"\n",
"For more info, visit https://github.com/nomic-ai/gpt4all.\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_path = (\n",
" \"./models/ggml-gpt4all-l13b-snoozy.bin\" # replace with your desired local file path\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Callbacks support token-wise streaming\n",
"callbacks = [StreamingStdOutCallbackHandler()]\n",
"\n",
"# Verbose is required to pass to the callback manager\n",
"llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)\n",
"\n",
"# If you want to use a custom model add the backend parameter\n",
"# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends\n",
"llm = GPT4All(model=local_path, backend=\"gptj\", callbacks=callbacks, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
},
{
"attachments": {},
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"source": [
"Justin Bieber was born on March 1, 1994. In 1994, The Cowboys won Super Bowl XXVIII."
]
}
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