forked from Archives/langchain
143 lines
3.8 KiB
Plaintext
143 lines
3.8 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Llama-cpp\n",
|
|
"\n",
|
|
"[llama-cpp](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp). \n",
|
|
"It supports [several LLMs](https://github.com/ggerganov/llama.cpp).\n",
|
|
"\n",
|
|
"This notebook goes over how to run `llama-cpp` within LangChain."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"!pip install llama-cpp-python"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Make sure you are following all instructions to [install all necessary model files](https://github.com/ggerganov/llama.cpp).\n",
|
|
"\n",
|
|
"You don't need an `API_TOKEN`!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.llms import LlamaCpp\n",
|
|
"from langchain import PromptTemplate, LLMChain\n",
|
|
"from langchain.callbacks.base import CallbackManager\n",
|
|
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"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": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Callbacks support token-wise streaming\n",
|
|
"callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
|
|
"# Verbose is required to pass to the callback manager\n",
|
|
"\n",
|
|
"# Make sure the model path is correct for your system!\n",
|
|
"llm = LlamaCpp(\n",
|
|
" model_path=\"./ggml-model-q4_0.bin\", callback_manager=callback_manager, verbose=True\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" First we need to identify what year Justin Beiber was born in. A quick google search reveals that he was born on March 1st, 1994. Now we know when the Super Bowl was played in, so we can look up which NFL team won it. The NFL Superbowl of the year 1994 was won by the San Francisco 49ers against the San Diego Chargers."
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"' First we need to identify what year Justin Beiber was born in. A quick google search reveals that he was born on March 1st, 1994. Now we know when the Super Bowl was played in, so we can look up which NFL team won it. The NFL Superbowl of the year 1994 was won by the San Francisco 49ers against the San Diego Chargers.'"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
|
|
"\n",
|
|
"llm_chain.run(question)"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.10.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|