File size: 3,188 Bytes
7b856a8 f5ec828 872b692 f5ec828 872b692 f5ec828 872b692 7b856a8 f5ec828 7b856a8 4e3dc76 f5ec828 4e3dc76 7b856a8 f5ec828 872b692 7b856a8 4e3dc76 7b856a8 f5ec828 872b692 7b856a8 4e3dc76 7b856a8 4e3dc76 f5ec828 872b692 4e3dc76 7b856a8 4e3dc76 f5ec828 7b856a8 f5ec828 872b692 f5ec828 4e3dc76 7b856a8 4e3dc76 7b856a8 f5ec828 872b692 f5ec828 4e3dc76 7b856a8 4e3dc76 7b856a8 f5ec828 872b692 f5ec828 4e3dc76 7b856a8 4e3dc76 f5ec828 4e3dc76 7b856a8 f5ec828 872b692 f5ec828 7b856a8 4e3dc76 7b856a8 f5ec828 7b856a8 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "c0075889",
"metadata": {},
"outputs": [],
"source": [
"!pip install -q git+https://github.com/srush/MiniChain\n",
"!git clone https://github.com/srush/MiniChain; cp -fr MiniChain/examples/* . "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "033f7bd9",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [],
"source": [
"desc = \"\"\"\n",
"### Prompt-aided Language Models\n",
"\n",
"Chain for answering complex problems by code generation and execution. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/pal.ipynb)\n",
"\n",
"(Adapted from Prompt-aided Language Models [PAL](https://arxiv.org/pdf/2211.10435.pdf)).\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ada25bcd",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [],
"source": [
"from minichain import prompt, show, OpenAI, Python"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3249f5ac",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [],
"source": [
"@prompt(OpenAI(), template_file=\"pal.pmpt.tpl\")\n",
"def pal_prompt(model, question):\n",
" return model(dict(question=question))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37f265c9",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [],
"source": [
"@prompt(Python())\n",
"def python(model, inp):\n",
" return float(model(inp + \"\\nprint(solution())\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e767c1eb",
"metadata": {},
"outputs": [],
"source": [
"def pal(question):\n",
" return python(pal_prompt(question))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2a4f241",
"metadata": {},
"outputs": [],
"source": [
"question = \"Melanie is a door-to-door saleswoman. She sold a third of her \" \\\n",
" \"vacuum cleaners at the green house, 2 more to the red house, and half of \" \\\n",
" \"what was left at the orange house. If Melanie has 5 vacuum cleaners left, \" \\\n",
" \"how many did she start with?\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c22e7837",
"metadata": {},
"outputs": [],
"source": [
"gradio = show(pal,\n",
" examples=[question],\n",
" subprompts=[pal_prompt, python],\n",
" description=desc,\n",
" out_type=\"json\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8790a65e",
"metadata": {},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" gradio.launch()"
]
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|