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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from llmlingua import PromptCompressor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"llm_lingua = PromptCompressor(\n",
" model_name=\"microsoft/llmlingua-2-xlm-roberta-large-meetingbank\",\n",
" use_llmlingua2=True,\n",
" device_map=\"mps\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"compressed_prompt The fn argument is very flexible -- you can pass any Python function that you want to wrap with a UI. In the example above, we saw a relatively simple function, but the function could be anything from a music generator to a tax calculator to the prediction function of a pretrained machine learning model. The inputs and outputs arguments take one or more Gradio components. As we'll see, Gradio includes more than 30 built-in components (such as the gr.Textbox(), gr.Image(), and gr.HTML() components) that are designed for machine learning applications.\n",
"compressed_prompt_list [\"The fn argument is very flexible -- you can pass any Python function that you want to wrap with a UI. In the example above, we saw a relatively simple function, but the function could be anything from a music generator to a tax calculator to the prediction function of a pretrained machine learning model. The inputs and outputs arguments take one or more Gradio components. As we'll see, Gradio includes more than 30 built-in components (such as the gr.Textbox(), gr.Image(), and gr.HTML() components) that are designed for machine learning applications.\"]\n",
"origin_tokens 113\n",
"compressed_tokens 112\n",
"ratio 1.0x\n",
"rate 99.1%\n",
"saving , Saving $0.0 in GPT-4.\n"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
]
}
],
"source": [
"with open(\"prompt.txt\", \"r\") as file:\n",
" prompt = file.read()\n",
" compressed_prompt = llm_lingua.compress_prompt(prompt, rate=1)\n",
" [print(\": \".join(entry)) for entry in compressed_prompt.items()]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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