17 changed files with 3204 additions and 24 deletions
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"id": "56297249-4a8c-4e67-b8c3-a0d8652c104e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import modal" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"id": "0d240622-8422-4c99-8464-c04d063e4cb6", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# !modal setup" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"id": "3b133701-f550-44a1-a67f-eb7ccc4769a9", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from hello import app, hello" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"id": "0f3f73ae-1295-49f3-9099-b8b41fc3429b", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"'Hello from Seaport, New York, US!!'" |
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] |
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}, |
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"execution_count": 4, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"with app.run(show_progress=False):\n", |
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" reply=hello.local()\n", |
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"reply" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"id": "c1d8c6f9-edc7-4e52-9b3a-c07d7cff1ac7", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"'Hello from Frankfurt am Main, Hesse, DE!!'" |
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] |
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}, |
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"execution_count": 5, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"with app.run(show_progress=False):\n", |
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" reply=hello.remote()\n", |
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"reply" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "cb8b6c41-8259-4329-b1c4-a1f67d26d1be", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import modal\n", |
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"from llama import app, generate" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "db4a718a-d95d-4f61-9688-c9df21d88fe6", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"with modal.enable_output():\n", |
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" with app.run():\n", |
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" result=generate.remote(\"Life is a mystery, everyone must stand alone, I hear\")\n", |
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"result" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "9a9a6844-29ec-4264-8e72-362d976b3968", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import modal\n", |
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"from pricer_ephemeral import app, price" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "50e6cf99-8959-4ae3-ba02-e325cb7fff94", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"with modal.enable_output():\n", |
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" with app.run():\n", |
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" result=price.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")\n", |
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"result" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "7f90d857-2f12-4521-bb90-28efd917f7d1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"!modal deploy pricer_service" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "1dec70ff-1986-4405-8624-9bbbe0ce1f4a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"pricer = modal.Function.lookup(\"pricer-service\", \"price\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "17776139-0d9e-4ad0-bcd0-82d3a92ca61f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"pricer.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "58f5d19f-8ffc-496c-832b-04e0d5892f54", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import modal" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"id": "f56d1e55-2a03-4ce2-bb47-2ab6b9175a02", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"\u001b[2K\u001b[34m⠸\u001b[0m Creating objects.....\n", |
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"\u001b[38;5;244m└── \u001b[0m\u001b[34m⠋\u001b[0m Creating mount /Users/ed/dev/llm_engineering/week8/pricer_service2.py: \n", |
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"\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[34m⠦\u001b[0m Creating objects...\n", |
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"\u001b[38;5;244m└── \u001b[0m\u001b[34m⠸\u001b[0m Creating mount /Users/ed/dev/llm_engineering/week8/pricer_service2.py: \n", |
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"\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[34m⠏\u001b[0m Creating objects...\n", |
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"\u001b[38;5;244m└── \u001b[0m\u001b[34m⠦\u001b[0m Creating mount /Users/ed/dev/llm_engineering/week8/pricer_service2.py: \n", |
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"\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[34m⠹\u001b[0m Creating objects...\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created mount /Users/ed/dev/llm_engineering/week8/pricer_service2.py\n", |
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"\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[34m⠴\u001b[0m Creating objects...load_model_to_folder.\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created mount /Users/ed/dev/llm_engineering/week8/pricer_service2.py\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created function Pricer.download_model_to_folder.\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created function Pricer.*.\n", |
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"\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[34m⠴\u001b[0m Creating objects...\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created mount /Users/ed/dev/llm_engineering/week8/pricer_service2.py\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created function Pricer.download_model_to_folder.\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created function Pricer.*.\n", |
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"\u001b[38;5;244m└── \u001b[0m🔨 Created function Pricer.price.\n", |
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"\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m✓\u001b[0m Created objects.\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created mount /Users/ed/dev/llm_engineering/week8/pricer_service2.py\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created function Pricer.download_model_to_folder.\n", |
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"\u001b[38;5;244m├── \u001b[0m🔨 Created function Pricer.*.\n", |
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"\u001b[38;5;244m└── \u001b[0m🔨 Created function Pricer.price.\n", |
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"\u001b[32m✓\u001b[0m App deployed in 1.570s! 🎉\n", |
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"\n", |
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"View Deployment: \u001b[35mhttps://modal.com/apps/ed-donner/main/deployed/pricer-service\u001b[0m\n" |
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] |
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} |
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], |
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"source": [ |
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"!modal deploy pricer_service2" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"id": "9e19daeb-1281-484b-9d2f-95cc6fed2622", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"133.0\n" |
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] |
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} |
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], |
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"source": [ |
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"\n", |
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"import modal\n", |
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"\n", |
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"Pricer = modal.Cls.lookup(\"pricer-service\", \"Pricer\")\n", |
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"pricer = Pricer()\n", |
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"reply = pricer.price.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")\n", |
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"print(reply)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 9, |
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"id": "ac331454-21e2-4b37-9602-4667006e34ee", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"reply = pricer.price.remote(\"iphone SE\")\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 10, |
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"id": "c3a71dcd-b71d-4c48-b0d9-3ac296d2046a", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"299.0" |
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] |
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}, |
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"execution_count": 10, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"reply" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ba9aedca-6a7b-4d30-9f64-59d76f76fb6d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.10" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
@ -0,0 +1,328 @@ |
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"id": "993a2a24-1a58-42be-8034-6d116fb8d786", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import re\n", |
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"import math\n", |
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"import json\n", |
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"from tqdm import tqdm\n", |
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"import random\n", |
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"from dotenv import load_dotenv\n", |
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"from huggingface_hub import login\n", |
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"import numpy as np\n", |
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"import pickle\n", |
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"from sentence_transformers import SentenceTransformer\n", |
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"from datasets import load_dataset\n", |
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"import chromadb\n", |
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"from items import Item\n", |
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"from sklearn.manifold import TSNE\n", |
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"import plotly.graph_objects as go" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"id": "0e31676f-6f31-465f-a80e-02d51ff8425a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# CONSTANTS\n", |
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"\n", |
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"HF_USER = \"ed-donner\" # your HF name here! Or use mine if you just want to reproduce my results.\n", |
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"DATASET_NAME = f\"{HF_USER}/pricer-data\"\n", |
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"QUESTION = \"How much does this cost to the nearest dollar?\\n\\n\"\n", |
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"DB = \"products_vectorstore\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"id": "2359ccc0-dbf2-4b1e-9473-e472b32f548b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# environment\n", |
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"\n", |
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"load_dotenv()\n", |
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", |
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"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"id": "a29fcc4e-e4d7-4c54-aa6b-e5d1111ea9c4", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Token is valid (permission: write).\n", |
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"Your token has been saved in your configured git credential helpers (osxkeychain).\n", |
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"Your token has been saved to /Users/ed/.cache/huggingface/token\n", |
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"Login successful\n" |
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] |
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} |
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], |
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"source": [ |
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"# Log in to HuggingFace\n", |
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"\n", |
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"hf_token = os.environ['HF_TOKEN']\n", |
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"login(hf_token, add_to_git_credential=True)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"id": "688bd995-ec3e-43cd-8179-7fe14b275877", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's avoid curating all our data again! Load in the pickle files:\n", |
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"\n", |
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"with open('train.pkl', 'rb') as file:\n", |
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" train = pickle.load(file)" |
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] |
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}, |
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|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 6, |
||||||
|
"id": "2817eaf5-4302-4a18-9148-d1062e3b3dbb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [ |
||||||
|
"400000" |
||||||
|
] |
||||||
|
}, |
||||||
|
"execution_count": 6, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"items = train\n", |
||||||
|
"len(items)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 11, |
||||||
|
"id": "f4aab95e-d719-4476-b6e7-e248120df25a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"client = chromadb.PersistentClient(path=DB)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "5f95dafd-ab80-464e-ba8a-dec7a2424780", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Deleted existing collection: products\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Check if the collection exists and delete it if it does\n", |
||||||
|
"collection_name = \"products\"\n", |
||||||
|
"existing_collection_names = [collection.name for collection in client.list_collections()]\n", |
||||||
|
"if collection_name in existing_collection_names:\n", |
||||||
|
" client.delete_collection(collection_name)\n", |
||||||
|
" print(f\"Deleted existing collection: {collection_name}\")\n", |
||||||
|
"\n", |
||||||
|
"collection = client.create_collection(collection_name)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "a87db200-d19d-44bf-acbd-15c45c70f5c9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 14, |
||||||
|
"id": "38de1bf8-c9b5-45b4-9f4b-86af93b3f80d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def description(item):\n", |
||||||
|
" text = item.prompt.replace(\"How much does this cost to the nearest dollar?\\n\\n\", \"\")\n", |
||||||
|
" return text.split(\"\\n\\nPrice is $\")[0]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 15, |
||||||
|
"id": "8c79e2fe-1f50-4ebf-9a93-34f3088f2996", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stderr", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [21:47<00:00, 3.27s/it]\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"for i in tqdm(range(0, len(items), 1000)):\n", |
||||||
|
" documents = [description(item) for item in items[i: i+1000]]\n", |
||||||
|
" vectors = model.encode(documents).astype(float).tolist()\n", |
||||||
|
" metadatas = [{\"category\": item.category, \"price\": item.price} for item in items[i: i+1000]]\n", |
||||||
|
" ids = [f\"doc_{j}\" for j in range(i, i+1000)]\n", |
||||||
|
" collection.add(\n", |
||||||
|
" ids=ids,\n", |
||||||
|
" documents=documents,\n", |
||||||
|
" embeddings=vectors,\n", |
||||||
|
" metadatas=metadatas\n", |
||||||
|
" )" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "525fc313-8a16-4ac0-8c42-6a6d1ba1c9b8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"CATEGORIES = ['Appliances', 'Automotive', 'Cell_Phones_and_Accessories', 'Electronics','Musical_Instruments', 'Office_Products', 'Tools_and_Home_Improvement', 'Toys_and_Games']\n", |
||||||
|
"COLORS = ['red', 'blue', 'brown', 'orange', 'yellow', 'green' , 'purple', 'cyan']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a4cf1c9a-1ced-48d4-974c-3c850905034e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Prework\n", |
||||||
|
"\n", |
||||||
|
"vectors_np = np.array(vectors)\n", |
||||||
|
"colors = [COLORS[CATEGORIES.index(t)] for t in categories]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0c6718b3-e0fd-4319-a1b5-d9d34d6b1dd9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# We humans find it easier to visalize things in 2D!\n", |
||||||
|
"# Reduce the dimensionality of the vectors to 2D using t-SNE\n", |
||||||
|
"# (t-distributed stochastic neighbor embedding)\n", |
||||||
|
"\n", |
||||||
|
"tsne = TSNE(n_components=2, random_state=42)\n", |
||||||
|
"reduced_vectors = tsne.fit_transform(vectors_np)\n", |
||||||
|
"\n", |
||||||
|
"# Create the 2D scatter plot\n", |
||||||
|
"fig = go.Figure(data=[go.Scatter(\n", |
||||||
|
" x=reduced_vectors[:, 0],\n", |
||||||
|
" y=reduced_vectors[:, 1],\n", |
||||||
|
" mode='markers',\n", |
||||||
|
" marker=dict(size=3, color=colors, opacity=0.8),\n", |
||||||
|
" text=[f\"Category: {c}<br>Text: {d[:100]}...\" for c, d in zip(categories, descriptions)],\n", |
||||||
|
" hoverinfo='text'\n", |
||||||
|
")])\n", |
||||||
|
"\n", |
||||||
|
"fig.update_layout(\n", |
||||||
|
" title='2D Chroma Vector Store Visualization',\n", |
||||||
|
" scene=dict(xaxis_title='x',yaxis_title='y'),\n", |
||||||
|
" width=1200,\n", |
||||||
|
" height=800,\n", |
||||||
|
" margin=dict(r=20, b=10, l=10, t=40)\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"fig.show()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c54df150-c8d8-4bc3-8877-6759691eeb42", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's try 3D!\n", |
||||||
|
"\n", |
||||||
|
"tsne = TSNE(n_components=3, random_state=42)\n", |
||||||
|
"reduced_vectors = tsne.fit_transform(vectors_np)\n", |
||||||
|
"\n", |
||||||
|
"# Create the 3D scatter plot\n", |
||||||
|
"fig = go.Figure(data=[go.Scatter3d(\n", |
||||||
|
" x=reduced_vectors[:, 0],\n", |
||||||
|
" y=reduced_vectors[:, 1],\n", |
||||||
|
" z=reduced_vectors[:, 2],\n", |
||||||
|
" mode='markers',\n", |
||||||
|
" marker=dict(size=3, color=colors, opacity=0.7),\n", |
||||||
|
" text=[f\"Category: {c}<br>Text: {d[:100]}...\" for c, d in zip(categories, descriptions)],\n", |
||||||
|
" hoverinfo='text'\n", |
||||||
|
")])\n", |
||||||
|
"\n", |
||||||
|
"fig.update_layout(\n", |
||||||
|
" title='3D Chroma Vector Store Visualization',\n", |
||||||
|
" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", |
||||||
|
" width=1200,\n", |
||||||
|
" height=800,\n", |
||||||
|
" margin=dict(r=20, b=10, l=10, t=40)\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"fig.show()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e8fb2a63-24c5-4dce-9e63-aa208272f82d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def " |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"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": "3.11.10" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,150 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "993a2a24-1a58-42be-8034-6d116fb8d786", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import re\n", |
||||||
|
"import math\n", |
||||||
|
"import json\n", |
||||||
|
"from tqdm import tqdm\n", |
||||||
|
"import random\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from huggingface_hub import login\n", |
||||||
|
"import numpy as np\n", |
||||||
|
"import pickle\n", |
||||||
|
"from sentence_transformers import SentenceTransformer\n", |
||||||
|
"from datasets import load_dataset\n", |
||||||
|
"import chromadb\n", |
||||||
|
"from items import Item\n", |
||||||
|
"from sklearn.manifold import TSNE\n", |
||||||
|
"import plotly.graph_objects as go" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 11, |
||||||
|
"id": "f4aab95e-d719-4476-b6e7-e248120df25a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"DB = \"products_vectorstore\"\n", |
||||||
|
"client = chromadb.PersistentClient(path=DB)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "5f95dafd-ab80-464e-ba8a-dec7a2424780", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Deleted existing collection: products\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"collection = client.get_or_create_collection('products')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "525fc313-8a16-4ac0-8c42-6a6d1ba1c9b8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"CATEGORIES = ['Appliances', 'Automotive', 'Cell_Phones_and_Accessories', 'Electronics','Musical_Instruments', 'Office_Products', 'Tools_and_Home_Improvement', 'Toys_and_Games']\n", |
||||||
|
"COLORS = ['red', 'blue', 'brown', 'orange', 'yellow', 'green' , 'purple', 'cyan']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a4cf1c9a-1ced-48d4-974c-3c850905034e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Prework\n", |
||||||
|
"result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", |
||||||
|
"vectors = np.array(result['embeddings'])\n", |
||||||
|
"documents = result['documents']\n", |
||||||
|
"categories = [metadata['category'] for metadata in result['metadatas']]\n", |
||||||
|
"colors = [COLORS[CATEGORIES.index(c)] for c in categories]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c54df150-c8d8-4bc3-8877-6759691eeb42", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's try 3D!\n", |
||||||
|
"\n", |
||||||
|
"tsne = TSNE(n_components=3, random_state=42, max_iter=250, n_jobs=-1)\n", |
||||||
|
"reduced_vectors = tsne.fit_transform(vectors)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e8fb2a63-24c5-4dce-9e63-aa208272f82d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"# Create the 3D scatter plot\n", |
||||||
|
"fig = go.Figure(data=[go.Scatter3d(\n", |
||||||
|
" x=reduced_vectors[:, 0],\n", |
||||||
|
" y=reduced_vectors[:, 1],\n", |
||||||
|
" z=reduced_vectors[:, 2],\n", |
||||||
|
" mode='markers',\n", |
||||||
|
" marker=dict(size=3, color=colors, opacity=0.7),\n", |
||||||
|
" text=[f\"Category: {c}<br>Text: {d[:100]}...\" for c, d in zip(categories, documents)],\n", |
||||||
|
" hoverinfo='text'\n", |
||||||
|
")])\n", |
||||||
|
"\n", |
||||||
|
"fig.update_layout(\n", |
||||||
|
" title='3D Chroma Vector Store Visualization',\n", |
||||||
|
" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", |
||||||
|
" width=1200,\n", |
||||||
|
" height=800,\n", |
||||||
|
" margin=dict(r=20, b=10, l=10, t=40)\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"fig.show()" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"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": "3.11.10" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -0,0 +1,19 @@ |
|||||||
|
import modal |
||||||
|
from modal import App, Volume, Image |
||||||
|
|
||||||
|
# Setup |
||||||
|
|
||||||
|
app = modal.App("hello") |
||||||
|
image = Image.debian_slim().pip_install("requests") |
||||||
|
gpu = "T4" |
||||||
|
|
||||||
|
# Hello! |
||||||
|
|
||||||
|
@app.function(image=image) |
||||||
|
def hello() -> str: |
||||||
|
import requests |
||||||
|
|
||||||
|
response = requests.get('https://ipinfo.io/json') |
||||||
|
data = response.json() |
||||||
|
city, region, country = data['city'], data['region'], data['country'] |
||||||
|
return f"Hello from {city}, {region}, {country}!!" |
@ -0,0 +1,101 @@ |
|||||||
|
from typing import Optional |
||||||
|
from transformers import AutoTokenizer |
||||||
|
import re |
||||||
|
|
||||||
|
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B" |
||||||
|
MIN_TOKENS = 150 |
||||||
|
MAX_TOKENS = 160 |
||||||
|
MIN_CHARS = 300 |
||||||
|
CEILING_CHARS = MAX_TOKENS * 7 |
||||||
|
|
||||||
|
class Item: |
||||||
|
""" |
||||||
|
An Item is a cleaned, curated datapoint of a Product with a Price |
||||||
|
""" |
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True) |
||||||
|
PREFIX = "Price is $" |
||||||
|
QUESTION = "How much does this cost to the nearest dollar?" |
||||||
|
REMOVALS = ['"Batteries Included?": "No"', '"Batteries Included?": "Yes"', '"Batteries Required?": "No"', '"Batteries Required?": "Yes"', "By Manufacturer", "Item", "Date First", "Package", ":", "Number of", "Best Sellers", "Number", "Product "] |
||||||
|
|
||||||
|
title: str |
||||||
|
price: float |
||||||
|
category: str |
||||||
|
token_count: int = 0 |
||||||
|
details: Optional[str] |
||||||
|
prompt: Optional[str] = None |
||||||
|
include = False |
||||||
|
|
||||||
|
def __init__(self, data, price): |
||||||
|
self.title = data['title'] |
||||||
|
self.price = price |
||||||
|
self.parse(data) |
||||||
|
|
||||||
|
def scrub_details(self): |
||||||
|
""" |
||||||
|
Clean up the details string by removing common text that doesn't add value |
||||||
|
""" |
||||||
|
details = self.details |
||||||
|
for remove in self.REMOVALS: |
||||||
|
details = details.replace(remove, "") |
||||||
|
return details |
||||||
|
|
||||||
|
def scrub(self, stuff): |
||||||
|
""" |
||||||
|
Clean up the provided text by removing unnecessary characters and whitespace |
||||||
|
Also remove words that are 7+ chars and contain numbers, as these are likely irrelevant product numbers |
||||||
|
""" |
||||||
|
stuff = re.sub(r'[:\[\]"{}【】\s]+', ' ', stuff).strip() |
||||||
|
stuff = stuff.replace(" ,", ",").replace(",,,",",").replace(",,",",") |
||||||
|
words = stuff.split(' ') |
||||||
|
select = [word for word in words if len(word)<7 or not any(char.isdigit() for char in word)] |
||||||
|
return " ".join(select) |
||||||
|
|
||||||
|
def parse(self, data): |
||||||
|
""" |
||||||
|
Parse this datapoint and if it fits within the allowed Token range, |
||||||
|
then set include to True |
||||||
|
""" |
||||||
|
contents = '\n'.join(data['description']) |
||||||
|
if contents: |
||||||
|
contents += '\n' |
||||||
|
features = '\n'.join(data['features']) |
||||||
|
if features: |
||||||
|
contents += features + '\n' |
||||||
|
self.details = data['details'] |
||||||
|
if self.details: |
||||||
|
contents += self.scrub_details() + '\n' |
||||||
|
if len(contents) > MIN_CHARS: |
||||||
|
contents = contents[:CEILING_CHARS] |
||||||
|
text = f"{self.scrub(self.title)}\n{self.scrub(contents)}" |
||||||
|
tokens = self.tokenizer.encode(text, add_special_tokens=False) |
||||||
|
if len(tokens) > MIN_TOKENS: |
||||||
|
tokens = tokens[:MAX_TOKENS] |
||||||
|
text = self.tokenizer.decode(tokens) |
||||||
|
self.make_prompt(text) |
||||||
|
self.include = True |
||||||
|
|
||||||
|
def make_prompt(self, text): |
||||||
|
""" |
||||||
|
Set the prompt instance variable to be a prompt appropriate for training |
||||||
|
""" |
||||||
|
self.prompt = f"{self.QUESTION}\n\n{text}\n\n" |
||||||
|
self.prompt += f"{self.PREFIX}{str(round(self.price))}.00" |
||||||
|
self.token_count = len(self.tokenizer.encode(self.prompt, add_special_tokens=False)) |
||||||
|
|
||||||
|
def test_prompt(self): |
||||||
|
""" |
||||||
|
Return a prompt suitable for testing, with the actual price removed |
||||||
|
""" |
||||||
|
return self.prompt.split(self.PREFIX)[0] + self.PREFIX |
||||||
|
|
||||||
|
def __repr__(self): |
||||||
|
""" |
||||||
|
Return a String version of this Item |
||||||
|
""" |
||||||
|
return f"<{self.title} = ${self.price}>" |
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,44 @@ |
|||||||
|
import modal |
||||||
|
from modal import App, Volume, Image |
||||||
|
|
||||||
|
# Setup |
||||||
|
|
||||||
|
app = modal.App("llama") |
||||||
|
image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate") |
||||||
|
secrets = [modal.Secret.from_name("hf-secret")] |
||||||
|
GPU = "T4" |
||||||
|
MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B" |
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
@app.function(image=image, secrets=secrets, gpu=GPU) |
||||||
|
def generate(prompt: str) -> str: |
||||||
|
import os |
||||||
|
import torch |
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed |
||||||
|
|
||||||
|
# Quant Config |
||||||
|
quant_config = BitsAndBytesConfig( |
||||||
|
load_in_4bit=True, |
||||||
|
bnb_4bit_use_double_quant=True, |
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16, |
||||||
|
bnb_4bit_quant_type="nf4" |
||||||
|
) |
||||||
|
|
||||||
|
# Load model and tokenizer |
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
||||||
|
tokenizer.pad_token = tokenizer.eos_token |
||||||
|
tokenizer.padding_side = "right" |
||||||
|
|
||||||
|
model = AutoModelForCausalLM.from_pretrained( |
||||||
|
MODEL_NAME, |
||||||
|
quantization_config=quant_config, |
||||||
|
device_map="auto" |
||||||
|
) |
||||||
|
|
||||||
|
set_seed(42) |
||||||
|
inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda") |
||||||
|
attention_mask = torch.ones(inputs.shape, device="cuda") |
||||||
|
outputs = model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1) |
||||||
|
return tokenizer.decode(outputs[0]) |
@ -0,0 +1,66 @@ |
|||||||
|
import modal |
||||||
|
from modal import App, Volume, Image |
||||||
|
|
||||||
|
# Setup |
||||||
|
|
||||||
|
app = modal.App("pricer") |
||||||
|
image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate", "peft") |
||||||
|
secrets = [modal.Secret.from_name("hf-secret")] |
||||||
|
|
||||||
|
# Constants |
||||||
|
|
||||||
|
GPU = "T4" |
||||||
|
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B" |
||||||
|
PROJECT_NAME = "pricer" |
||||||
|
HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results. |
||||||
|
RUN_NAME = "2024-09-13_13.04.39" |
||||||
|
PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}" |
||||||
|
REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36" |
||||||
|
FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}" |
||||||
|
|
||||||
|
|
||||||
|
@app.function(image=image, secrets=secrets, gpu=GPU) |
||||||
|
def price(description: str) -> float: |
||||||
|
import os |
||||||
|
import re |
||||||
|
import torch |
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed |
||||||
|
from peft import PeftModel |
||||||
|
|
||||||
|
QUESTION = "How much does this cost to the nearest dollar?" |
||||||
|
PREFIX = "Price is $" |
||||||
|
|
||||||
|
prompt = f"{QUESTION}\n{description}\n{PREFIX}" |
||||||
|
|
||||||
|
# Quant Config |
||||||
|
quant_config = BitsAndBytesConfig( |
||||||
|
load_in_4bit=True, |
||||||
|
bnb_4bit_use_double_quant=True, |
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16, |
||||||
|
bnb_4bit_quant_type="nf4" |
||||||
|
) |
||||||
|
|
||||||
|
# Load model and tokenizer |
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) |
||||||
|
tokenizer.pad_token = tokenizer.eos_token |
||||||
|
tokenizer.padding_side = "right" |
||||||
|
|
||||||
|
base_model = AutoModelForCausalLM.from_pretrained( |
||||||
|
BASE_MODEL, |
||||||
|
quantization_config=quant_config, |
||||||
|
device_map="auto" |
||||||
|
) |
||||||
|
|
||||||
|
fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION) |
||||||
|
|
||||||
|
set_seed(42) |
||||||
|
inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda") |
||||||
|
attention_mask = torch.ones(inputs.shape, device="cuda") |
||||||
|
outputs = fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1) |
||||||
|
result = tokenizer.decode(outputs[0]) |
||||||
|
|
||||||
|
contents = result.split("Price is $")[1] |
||||||
|
contents = contents.replace(',','') |
||||||
|
match = re.search(r"[-+]?\d*\.\d+|\d+", contents) |
||||||
|
return float(match.group()) if match else 0 |
@ -0,0 +1,66 @@ |
|||||||
|
import modal |
||||||
|
from modal import App, Volume, Image |
||||||
|
|
||||||
|
# Setup - define our infrastructure with code! |
||||||
|
|
||||||
|
app = modal.App("pricer-service") |
||||||
|
image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate", "peft") |
||||||
|
secrets = [modal.Secret.from_name("hf-secret")] |
||||||
|
|
||||||
|
# Constants |
||||||
|
|
||||||
|
GPU = "T4" |
||||||
|
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B" |
||||||
|
PROJECT_NAME = "pricer" |
||||||
|
HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results. |
||||||
|
RUN_NAME = "2024-09-13_13.04.39" |
||||||
|
PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}" |
||||||
|
REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36" |
||||||
|
FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}" |
||||||
|
|
||||||
|
|
||||||
|
@app.function(image=image, secrets=secrets, gpu=GPU) |
||||||
|
def price(description: str) -> float: |
||||||
|
import os |
||||||
|
import re |
||||||
|
import torch |
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed |
||||||
|
from peft import PeftModel |
||||||
|
|
||||||
|
QUESTION = "How much does this cost to the nearest dollar?" |
||||||
|
PREFIX = "Price is $" |
||||||
|
|
||||||
|
prompt = f"{QUESTION}\n{description}\n{PREFIX}" |
||||||
|
|
||||||
|
# Quant Config |
||||||
|
quant_config = BitsAndBytesConfig( |
||||||
|
load_in_4bit=True, |
||||||
|
bnb_4bit_use_double_quant=True, |
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16, |
||||||
|
bnb_4bit_quant_type="nf4" |
||||||
|
) |
||||||
|
|
||||||
|
# Load model and tokenizer |
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) |
||||||
|
tokenizer.pad_token = tokenizer.eos_token |
||||||
|
tokenizer.padding_side = "right" |
||||||
|
|
||||||
|
base_model = AutoModelForCausalLM.from_pretrained( |
||||||
|
BASE_MODEL, |
||||||
|
quantization_config=quant_config, |
||||||
|
device_map="auto" |
||||||
|
) |
||||||
|
|
||||||
|
fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION) |
||||||
|
|
||||||
|
set_seed(42) |
||||||
|
inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda") |
||||||
|
attention_mask = torch.ones(inputs.shape, device="cuda") |
||||||
|
outputs = fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1) |
||||||
|
result = tokenizer.decode(outputs[0]) |
||||||
|
|
||||||
|
contents = result.split("Price is $")[1] |
||||||
|
contents = contents.replace(',','') |
||||||
|
match = re.search(r"[-+]?\d*\.\d+|\d+", contents) |
||||||
|
return float(match.group()) if match else 0 |
@ -0,0 +1,84 @@ |
|||||||
|
import modal |
||||||
|
from modal import App, Volume, Image |
||||||
|
|
||||||
|
# Setup - define our infrastructure with code! |
||||||
|
|
||||||
|
app = modal.App("pricer-service") |
||||||
|
image = Image.debian_slim().pip_install("huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft") |
||||||
|
secrets = [modal.Secret.from_name("hf-secret")] |
||||||
|
|
||||||
|
# Constants |
||||||
|
|
||||||
|
GPU = "T4" |
||||||
|
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B" |
||||||
|
PROJECT_NAME = "pricer" |
||||||
|
HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results. |
||||||
|
RUN_NAME = "2024-09-13_13.04.39" |
||||||
|
PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}" |
||||||
|
REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36" |
||||||
|
FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}" |
||||||
|
|
||||||
|
QUESTION = "How much does this cost to the nearest dollar?" |
||||||
|
PREFIX = "Price is $" |
||||||
|
|
||||||
|
|
||||||
|
@app.cls(image=image, secrets=secrets, gpu=GPU) |
||||||
|
class Pricer: |
||||||
|
@modal.build() |
||||||
|
def download_model_to_folder(self): |
||||||
|
from huggingface_hub import snapshot_download |
||||||
|
import os |
||||||
|
MODEL_DIR = "~/.cache/huggingface/hub/" |
||||||
|
os.makedirs(MODEL_DIR, exist_ok=True) |
||||||
|
snapshot_download(BASE_MODEL, local_dir=MODEL_DIR) |
||||||
|
snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=MODEL_DIR) |
||||||
|
|
||||||
|
@modal.enter() |
||||||
|
def setup(self): |
||||||
|
import os |
||||||
|
import torch |
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed |
||||||
|
from peft import PeftModel |
||||||
|
|
||||||
|
# Quant Config |
||||||
|
quant_config = BitsAndBytesConfig( |
||||||
|
load_in_4bit=True, |
||||||
|
bnb_4bit_use_double_quant=True, |
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16, |
||||||
|
bnb_4bit_quant_type="nf4" |
||||||
|
) |
||||||
|
|
||||||
|
# Load model and tokenizer |
||||||
|
|
||||||
|
self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) |
||||||
|
self.tokenizer.pad_token = self.tokenizer.eos_token |
||||||
|
self.tokenizer.padding_side = "right" |
||||||
|
|
||||||
|
self.base_model = AutoModelForCausalLM.from_pretrained( |
||||||
|
BASE_MODEL, |
||||||
|
quantization_config=quant_config, |
||||||
|
device_map="auto" |
||||||
|
) |
||||||
|
|
||||||
|
self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_MODEL, revision=REVISION) |
||||||
|
|
||||||
|
@modal.method() |
||||||
|
def price(self, description: str) -> float: |
||||||
|
import os |
||||||
|
import re |
||||||
|
import torch |
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed |
||||||
|
from peft import PeftModel |
||||||
|
|
||||||
|
set_seed(42) |
||||||
|
prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}" |
||||||
|
inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda") |
||||||
|
attention_mask = torch.ones(inputs.shape, device="cuda") |
||||||
|
outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1) |
||||||
|
result = self.tokenizer.decode(outputs[0]) |
||||||
|
|
||||||
|
contents = result.split("Price is $")[1] |
||||||
|
contents = contents.replace(',','') |
||||||
|
match = re.search(r"[-+]?\d*\.\d+|\d+", contents) |
||||||
|
return float(match.group()) if match else 0 |
||||||
|
|
@ -0,0 +1,75 @@ |
|||||||
|
import math |
||||||
|
import matplotlib.pyplot as plt |
||||||
|
|
||||||
|
GREEN = "\033[92m" |
||||||
|
YELLOW = "\033[93m" |
||||||
|
RED = "\033[91m" |
||||||
|
RESET = "\033[0m" |
||||||
|
COLOR_MAP = {"red":RED, "orange": YELLOW, "green": GREEN} |
||||||
|
|
||||||
|
class Tester: |
||||||
|
|
||||||
|
def __init__(self, predictor, data, title=None, size=250): |
||||||
|
self.predictor = predictor |
||||||
|
self.data = data |
||||||
|
self.title = title or predictor.__name__.replace("_", " ").title() |
||||||
|
self.size = size |
||||||
|
self.guesses = [] |
||||||
|
self.truths = [] |
||||||
|
self.errors = [] |
||||||
|
self.sles = [] |
||||||
|
self.colors = [] |
||||||
|
|
||||||
|
def color_for(self, error, truth): |
||||||
|
if error<40 or error/truth < 0.2: |
||||||
|
return "green" |
||||||
|
elif error<80 or error/truth < 0.4: |
||||||
|
return "orange" |
||||||
|
else: |
||||||
|
return "red" |
||||||
|
|
||||||
|
def run_datapoint(self, i): |
||||||
|
datapoint = self.data[i] |
||||||
|
guess = self.predictor(datapoint) |
||||||
|
truth = datapoint.price |
||||||
|
error = abs(guess - truth) |
||||||
|
log_error = math.log(truth+1) - math.log(guess+1) |
||||||
|
sle = log_error ** 2 |
||||||
|
color = self.color_for(error, truth) |
||||||
|
title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40]+"..." |
||||||
|
self.guesses.append(guess) |
||||||
|
self.truths.append(truth) |
||||||
|
self.errors.append(error) |
||||||
|
self.sles.append(sle) |
||||||
|
self.colors.append(color) |
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print(f"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}") |
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|
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def chart(self, title): |
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max_error = max(self.errors) |
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plt.figure(figsize=(12, 8)) |
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max_val = max(max(self.truths), max(self.guesses)) |
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plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6) |
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plt.scatter(self.truths, self.guesses, s=3, c=self.colors) |
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plt.xlabel('Ground Truth') |
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plt.ylabel('Model Estimate') |
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plt.xlim(0, max_val) |
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plt.ylim(0, max_val) |
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plt.title(title) |
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plt.show() |
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|
|
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def report(self): |
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average_error = sum(self.errors) / self.size |
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rmsle = math.sqrt(sum(self.sles) / self.size) |
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hits = sum(1 for color in self.colors if color=="green") |
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|
title = f"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%" |
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self.chart(title) |
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|
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def run(self): |
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|
self.error = 0 |
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|
for i in range(self.size): |
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self.run_datapoint(i) |
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self.report() |
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|
|
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|
@classmethod |
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|
def test(cls, function, data): |
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|
cls(function, data).run() |
Loading…
Reference in new issue