generate-data and some eval
This commit is contained in:
Kyle Corbitt
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examples/classify-recipes/.env.example
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examples/classify-recipes/.env.example
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OPENAI_API_KEY="[your OpenAI API key]"
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OPENPIPE_API_KEY="[your OpenPipe API key from https://app.openpipe.ai/project/settings]"
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WANDB_API_KEY="[Optionally, you can set a Weights & Biases API key to track your training run. Create it at https://wandb.ai/settings]"
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0
examples/classify-recipes/__init__.py
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0
examples/classify-recipes/__init__.py
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167
examples/classify-recipes/evaluate.ipynb
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examples/classify-recipes/evaluate.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"I have a model in `./models/run1/merged` that was trained on GPT-4's outputs to classify recipes. I need to figure out whether it does a good job at classifying recipes. I'll install dependencies first."
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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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"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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"Requirement already satisfied: vllm==0.1.3 in /usr/local/lib/python3.10/dist-packages (0.1.3)\n",
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"Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->vllm==0.1.3) (0.13.3)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.10 -m pip install --upgrade pip\u001b[0m\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install vllm==0.1.3 pandas==2.0.3"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Remember I got a \"test.jsonl\" file from OpenPipe back in [./prepare.ipynb](./prepare.ipynb)? Since that is data formatted the same way as our training data but that we didn't use for training, we can use it to check our model's performance."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"test_data = pd.read_json(\"./data/test.jsonl\", lines=True)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"During the training process Axolotl transformed our data into an instruction/response format known as the \"Alpaca format\" based on [the project that introduced it](https://github.com/tatsu-lab/stanford_alpaca). I need to transform my test data into the same format for best results."
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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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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'axolotl.prompters'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39maxolotl\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mprompters\u001b[39;00m \u001b[39mimport\u001b[39;00m UnpromptedPrompter\n\u001b[1;32m 2\u001b[0m prompter \u001b[39m=\u001b[39m UnpromptedPrompter()\n\u001b[1;32m 4\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mformat_prompt\u001b[39m(\u001b[39minput\u001b[39m: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mstr\u001b[39m:\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'axolotl.prompters'"
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]
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}
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],
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"source": [
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"from axolotl.prompters import UnpromptedPrompter\n",
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"\n",
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"prompter = UnpromptedPrompter()\n",
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"\n",
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"\n",
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"def format_prompt(input: str) -> str:\n",
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" return next(prompter.build_prompt(input))\n",
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"\n",
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"\n",
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"prompts = test_data[\"input\"].apply(format_prompt)\n",
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"\n",
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"print(f\"Sample prompt:\\n-----------\\n{prompts[0]}\")\n"
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]
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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",
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"language": "python",
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"name": "python3"
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},
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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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": "3.10.6"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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329
examples/classify-recipes/generate-data.ipynb
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329
examples/classify-recipes/generate-data.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this notebook I'm using the OpenPipe client to capture a set of calls to the OpenAI API.\n",
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"\n",
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"For this example I'll blithely throw engineering best practices to the wind and use the notebook itself to manage dependencies. 😁"
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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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"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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"Requirement already satisfied: openpipe==3.0.3 in /usr/local/lib/python3.10/dist-packages (3.0.3)\n",
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"Requirement already satisfied: attrs<24.0.0,>=23.1.0 in /usr/local/lib/python3.10/dist-packages (from openpipe==3.0.3) (23.1.0)\n",
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"Requirement already satisfied: httpx<0.25.0,>=0.24.1 in /usr/local/lib/python3.10/dist-packages (from openpipe==3.0.3) (0.24.1)\n",
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"Requirement already satisfied: openai<0.28.0,>=0.27.8 in /usr/local/lib/python3.10/dist-packages (from openpipe==3.0.3) (0.27.9)\n",
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"Requirement already satisfied: python-dateutil<3.0.0,>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from openpipe==3.0.3) (2.8.2)\n",
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||||||
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"Requirement already satisfied: toml<0.11.0,>=0.10.2 in /usr/local/lib/python3.10/dist-packages (from openpipe==3.0.3) (0.10.2)\n",
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||||||
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"Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (2022.12.7)\n",
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"Requirement already satisfied: httpcore<0.18.0,>=0.15.0 in /usr/local/lib/python3.10/dist-packages (from httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (0.17.3)\n",
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"Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (3.4)\n",
|
||||||
|
"Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (1.3.0)\n",
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||||||
|
"Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.8->openpipe==3.0.3) (2.28.1)\n",
|
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|
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.8->openpipe==3.0.3) (4.66.1)\n",
|
||||||
|
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<0.28.0,>=0.27.8->openpipe==3.0.3) (3.8.5)\n",
|
||||||
|
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil<3.0.0,>=2.8.2->openpipe==3.0.3) (1.16.0)\n",
|
||||||
|
"Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (0.14.0)\n",
|
||||||
|
"Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (3.7.1)\n",
|
||||||
|
"Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (2.1.1)\n",
|
||||||
|
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.26.13)\n",
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||||||
|
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (6.0.4)\n",
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||||||
|
"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (4.0.3)\n",
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"Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.9.2)\n",
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|
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.4.0)\n",
|
||||||
|
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.3.1)\n",
|
||||||
|
"Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (1.1.2)\n",
|
||||||
|
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
|
||||||
|
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.10 -m pip install --upgrade pip\u001b[0m\n",
|
||||||
|
"Note: you may need to restart the kernel to use updated packages.\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"%pip install openpipe==3.0.3 python-dotenv==1.0.0 joblib==1.3.2"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"When working with remote datasets (or any data, really), it's a good idea to visually inspect some samples to make sure it looks like you expect. I'll print a recipe."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Recipe dataset:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"Dataset({\n",
|
||||||
|
" features: ['recipe'],\n",
|
||||||
|
" num_rows: 5000\n",
|
||||||
|
"})"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"First recipe:\n",
|
||||||
|
" Shrimp Creole\n",
|
||||||
|
"\n",
|
||||||
|
"Ingredients:\n",
|
||||||
|
"- 20 shrimp (8 oz.)\n",
|
||||||
|
"- 2 c. (16 oz. can) tomato sauce\n",
|
||||||
|
"- 1 small onion, chopped\n",
|
||||||
|
"- 1 celery stalk, chopped\n",
|
||||||
|
"- 1/4 green bell pepper, diced\n",
|
||||||
|
"- 1/4 c. sliced mushrooms\n",
|
||||||
|
"- 3 Tbsp. parsley\n",
|
||||||
|
"- 1/2 tsp. pepper\n",
|
||||||
|
"- 1 to 1-1/2 c. brown rice, prepared according to pkg. directions (not included in exchanges)\n",
|
||||||
|
"\n",
|
||||||
|
"Directions:\n",
|
||||||
|
"- Peel, devein and wash shrimp; set aside.\n",
|
||||||
|
"- (If shrimp are frozen, let thaw first in the refrigerator.)\n",
|
||||||
|
"- Simmer tomato sauce, onion, celery, green pepper, mushrooms, parsley and pepper in skillet for 30 minutes.\n",
|
||||||
|
"- Add shrimp and cook 10 to 15 minutes more, until shrimp are tender.\n",
|
||||||
|
"- Serve over brown rice.\n",
|
||||||
|
"- Serves 2.\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from datasets import load_dataset\n",
|
||||||
|
"\n",
|
||||||
|
"recipes = load_dataset(\"corbt/unlabeled-recipes\")[\"train\"]\n",
|
||||||
|
"print(\"Recipe dataset shape:\\n------------------\")\n",
|
||||||
|
"display(recipes)\n",
|
||||||
|
"print(\"First recipe:\\n------------------\", recipes[\"recipe\"][0])\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"Mm, delicious. Anyway, we need to generate a training dataset. We'll call GPT-4 on each of our examples.\n",
|
||||||
|
"\n",
|
||||||
|
"We'll use [OpenPipe](https://github.com/openpipe/openpipe) to track our calls and form a training dataset. Create an account and a project, then copy your API key from https://app.openpipe.ai/project/settings into a file called `.env`."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 9,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"{'has_non_fish_meat': False,\n",
|
||||||
|
" 'requires_oven': True,\n",
|
||||||
|
" 'requires_stove': True,\n",
|
||||||
|
" 'cook_time_over_30_mins': False,\n",
|
||||||
|
" 'main_dish': False}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 9,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from openpipe import openai, configure_openpipe\n",
|
||||||
|
"import json\n",
|
||||||
|
"import os\n",
|
||||||
|
"import dotenv\n",
|
||||||
|
"\n",
|
||||||
|
"dotenv.load_dotenv()\n",
|
||||||
|
"\n",
|
||||||
|
"configure_openpipe(api_key=os.environ[\"OPENPIPE_API_KEY\"])\n",
|
||||||
|
"\n",
|
||||||
|
"openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def classify_recipe(recipe: str):\n",
|
||||||
|
" completion = openai.ChatCompletion.create(\n",
|
||||||
|
" model=\"gpt-4\",\n",
|
||||||
|
" messages=[\n",
|
||||||
|
" {\n",
|
||||||
|
" \"role\": \"system\",\n",
|
||||||
|
" \"content\": \"Your goal is to classify a recipe along several dimensions.Pay attention to the instructions.\",\n",
|
||||||
|
" },\n",
|
||||||
|
" {\n",
|
||||||
|
" \"role\": \"user\",\n",
|
||||||
|
" \"content\": recipe,\n",
|
||||||
|
" },\n",
|
||||||
|
" ],\n",
|
||||||
|
" functions=[\n",
|
||||||
|
" {\n",
|
||||||
|
" \"name\": \"classify\",\n",
|
||||||
|
" \"parameters\": {\n",
|
||||||
|
" \"type\": \"object\",\n",
|
||||||
|
" \"properties\": {\n",
|
||||||
|
" \"has_non_fish_meat\": {\n",
|
||||||
|
" \"type\": \"boolean\",\n",
|
||||||
|
" \"description\": \"True if the recipe contains any meat or meat products (eg. chicken broth) besides fish\",\n",
|
||||||
|
" },\n",
|
||||||
|
" \"requires_oven\": {\n",
|
||||||
|
" \"type\": \"boolean\",\n",
|
||||||
|
" \"description\": \"True if the recipe requires an oven\",\n",
|
||||||
|
" },\n",
|
||||||
|
" \"requires_stove\": {\n",
|
||||||
|
" \"type\": \"boolean\",\n",
|
||||||
|
" \"description\": \"True if the recipe requires a stove\",\n",
|
||||||
|
" },\n",
|
||||||
|
" \"cook_time_over_30_mins\": {\n",
|
||||||
|
" \"type\": \"boolean\",\n",
|
||||||
|
" \"description\": \"True if the recipe takes over 30 minutes to prepare and cook, including waiting time\",\n",
|
||||||
|
" },\n",
|
||||||
|
" \"main_dish\": {\n",
|
||||||
|
" \"type\": \"boolean\",\n",
|
||||||
|
" \"description\": \"True if the recipe can be served as a main dish\",\n",
|
||||||
|
" },\n",
|
||||||
|
" },\n",
|
||||||
|
" \"required\": [\n",
|
||||||
|
" \"has_non_fish_meat\",\n",
|
||||||
|
" \"requires_oven\",\n",
|
||||||
|
" \"requires_stove\",\n",
|
||||||
|
" \"cook_time_over_30_mins\",\n",
|
||||||
|
" \"main_course\",\n",
|
||||||
|
" ],\n",
|
||||||
|
" },\n",
|
||||||
|
" }\n",
|
||||||
|
" ],\n",
|
||||||
|
" function_call={\n",
|
||||||
|
" \"name\": \"classify\",\n",
|
||||||
|
" },\n",
|
||||||
|
" openpipe={\"tags\": {\"prompt_id\": \"classify-recipe\"}, \"cache\": True},\n",
|
||||||
|
" )\n",
|
||||||
|
" return json.loads(completion.choices[0].message.function_call.arguments)\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"classify_recipe(recipes[\"recipe\"][-1])\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 10,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Classifying recipe 0/5000: Shrimp Creole\n",
|
||||||
|
"Classifying recipe 100/5000: Spoon Bread\n",
|
||||||
|
"Classifying recipe 200/5000: Quadrangle Grille'S Pumpkin-Walnut Cheesecake\n",
|
||||||
|
"Classifying recipe 300/5000: Broccoli Casserole\n",
|
||||||
|
"Error reporting to OpenPipe: 520 is not a valid HTTPStatus\n",
|
||||||
|
"520 is not a valid HTTPStatus\n",
|
||||||
|
"Classifying recipe 400/5000: Paal Payasam (3-Ingredient Rice Pudding)\n",
|
||||||
|
"Classifying recipe 500/5000: Dirt Dessert\n",
|
||||||
|
"Classifying recipe 600/5000: Dolma, Stuffed Dried Peppers And Eggplants\n",
|
||||||
|
"Classifying recipe 700/5000: Party Pecan Pies\n",
|
||||||
|
"Classifying recipe 800/5000: Pie Crust\n",
|
||||||
|
"Classifying recipe 900/5000: Russian Dressing(Salad Dressing) \n",
|
||||||
|
"Classifying recipe 1000/5000: O'Brien Potatoes\n",
|
||||||
|
"Classifying recipe 1100/5000: Monster Cookies\n",
|
||||||
|
"Classifying recipe 1200/5000: Striped Fruit Pops\n",
|
||||||
|
"Classifying recipe 1300/5000: Cute Heart-Shaped Fried Egg\n",
|
||||||
|
"Classifying recipe 1400/5000: Steak Marinade\n",
|
||||||
|
"Classifying recipe 1500/5000: Bbq Sauce For Fish Recipe\n",
|
||||||
|
"Classifying recipe 1600/5000: Barbecue Ranch Salad\n",
|
||||||
|
"Classifying recipe 1700/5000: White Fudge\n",
|
||||||
|
"Classifying recipe 1800/5000: Seaton Chocolate Chip Cookies\n",
|
||||||
|
"Classifying recipe 1900/5000: Beef Stroganoff\n",
|
||||||
|
"Classifying recipe 2000/5000: Lemon Delight\n",
|
||||||
|
"Classifying recipe 2100/5000: Cream Cheese Chicken Chili\n",
|
||||||
|
"Classifying recipe 2200/5000: Bean Salad\n",
|
||||||
|
"Classifying recipe 2300/5000: Green Beans Almondine\n",
|
||||||
|
"Classifying recipe 2400/5000: Radish-And-Avocado Salad\n",
|
||||||
|
"Classifying recipe 2500/5000: Salsa Rojo\n",
|
||||||
|
"Classifying recipe 2600/5000: Pepperoni Bread\n",
|
||||||
|
"Classifying recipe 2700/5000: Sabzi Polow\n",
|
||||||
|
"Classifying recipe 2800/5000: Italian Vegetable Pizzas\n",
|
||||||
|
"Error classifying recipe 2801: Bad gateway. {\"error\":{\"code\":502,\"message\":\"Bad gateway.\",\"param\":null,\"type\":\"cf_bad_gateway\"}} 502 {'error': {'code': 502, 'message': 'Bad gateway.', 'param': None, 'type': 'cf_bad_gateway'}} {'Date': 'Thu, 24 Aug 2023 15:44:45 GMT', 'Content-Type': 'application/json', 'Content-Length': '84', 'Connection': 'keep-alive', 'X-Frame-Options': 'SAMEORIGIN', 'Referrer-Policy': 'same-origin', 'Cache-Control': 'private, max-age=0, no-store, no-cache, must-revalidate, post-check=0, pre-check=0', 'Expires': 'Thu, 01 Jan 1970 00:00:01 GMT', 'Server': 'cloudflare', 'CF-RAY': '7fbca943df684de1-MCI', 'alt-svc': 'h3=\":443\"; ma=86400'}\n",
|
||||||
|
"Classifying recipe 2900/5000: Hot Fudge Sauce, Soda Shop Style\n",
|
||||||
|
"Classifying recipe 3000/5000: Meatball Soup With Vegetables And Brown Rice\n",
|
||||||
|
"Classifying recipe 3100/5000: Herbed Potatoes And Onions\n",
|
||||||
|
"Classifying recipe 3200/5000: Apple Crunch Pie (2 Extra Servings)\n",
|
||||||
|
"Classifying recipe 3300/5000: Pineapple-Orange Punch\n",
|
||||||
|
"Classifying recipe 3400/5000: Turkey Veggie Burgers With Avocado Mayo\n",
|
||||||
|
"Error reporting to OpenPipe: 520 is not a valid HTTPStatus\n",
|
||||||
|
"520 is not a valid HTTPStatus\n",
|
||||||
|
"Classifying recipe 3500/5000: Pear & Goat Cheese Salad\n",
|
||||||
|
"Classifying recipe 3600/5000: Triple Chocolate Cookies\n",
|
||||||
|
"Classifying recipe 3700/5000: Strawberry Banana Yogurt Pops\n",
|
||||||
|
"Error classifying recipe 3779: Request timed out: HTTPSConnectionPool(host='api.openai.com', port=443): Read timed out. (read timeout=600)\n",
|
||||||
|
"Classifying recipe 3800/5000: Chicken Croquettes\n",
|
||||||
|
"Classifying recipe 3900/5000: Mushroom Casserole\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"for i, recipe in enumerate(recipes[\"recipe\"]):\n",
|
||||||
|
" if i % 100 == 0:\n",
|
||||||
|
" recipe_title = recipe.split(\"\\n\")[0]\n",
|
||||||
|
" print(f\"Classifying recipe {i}/{len(recipes)}: {recipe_title}\")\n",
|
||||||
|
" try:\n",
|
||||||
|
" classify_recipe(recipe)\n",
|
||||||
|
" except Exception as e:\n",
|
||||||
|
" print(f\"Error classifying recipe {i}: {e}\")\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"Ok, we have our "
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"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"
|
||||||
|
},
|
||||||
|
"orig_nbformat": 4
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
@@ -4,7 +4,7 @@
|
|||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"source": [
|
||||||
"Now let's get to the fun part -- training a model. We'll start by installing our dependencies."
|
"Now let's get to the fun part -- training a model. I'll start by installing the dependencies."
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -177,11 +177,11 @@
|
|||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"source": [
|
||||||
"We'll use the [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) library to manage our training run. It includes a lot of neat tricks that speed up training without sacrificing quality.\n",
|
"I'll use the [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) library to manage our training run. It includes a lot of neat tricks that speed up training without sacrificing quality.\n",
|
||||||
"\n",
|
"\n",
|
||||||
"In this case we'll use 8-bit training to use less GPU RAM, and sample packing to maximize GPU utilization. You can read more about the available options at https://github.com/OpenAccess-AI-Collective/axolotl.\n",
|
"In this case I'm using 8-bit training to use less GPU RAM, and sample packing to maximize GPU utilization. You can read more about the available options at https://github.com/OpenAccess-AI-Collective/axolotl.\n",
|
||||||
"\n",
|
"\n",
|
||||||
"The training run options we're using here are defined in [training-args.yaml](./training-args.yaml)."
|
"The training run options are defined in [training-config.yaml](./training-config.yaml)."
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -365,16 +365,16 @@
|
|||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"!accelerate launch ./axolotl/scripts/finetune.py training-args.yaml"
|
"!accelerate launch ./axolotl/scripts/finetune.py training-config.yaml"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"source": [
|
||||||
"Nice work! If you look on your filesystem you should see a new directory `./models/recipe-model`. This contains your trained model, which you can use to classify more recipes.\n",
|
"Sweet! If you look on your filesystem you should see a new directory `./models/run1`. This contains your trained model, which you can use to classify more recipes.\n",
|
||||||
"\n",
|
"\n",
|
||||||
"Before we using it though, we need to *merge* the model. We trained our model using [LoRA](https://huggingface.co/docs/peft/conceptual_guides/lora), which is a memory-efficient training method. But the inference library we'll use for testing doesn't support LoRA models yet, so we need to \"merge\" our LoRA model to transform it into a standard Llama2-style model. We've defined a helper to do that that we'll use below."
|
"There's one more step though. I trained our model using [LoRA](https://huggingface.co/docs/peft/conceptual_guides/lora), which is a memory-efficient training method. But the inference library we'll use for testing doesn't support LoRA models directly yet, so we need to \"merge\" our LoRA model to transform it into a standard Llama2-shaped model. I've defined a small helper to do that called `merge_lora_model` that I'll use below."
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -418,7 +418,7 @@
|
|||||||
"from utils import merge_lora_model\n",
|
"from utils import merge_lora_model\n",
|
||||||
"\n",
|
"\n",
|
||||||
"print(\"Merging model (this could take a while)\")\n",
|
"print(\"Merging model (this could take a while)\")\n",
|
||||||
"final_model_dir = merge_lora_model(\"training-args.yaml\")\n",
|
"final_model_dir = merge_lora_model(\"training-config.yaml\")\n",
|
||||||
"print(f\"Final model saved to '{final_model_dir}'\")\n"
|
"print(f\"Final model saved to '{final_model_dir}'\")\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
|
|||||||
73
examples/classify-recipes/training-config.yaml
Normal file
73
examples/classify-recipes/training-config.yaml
Normal file
@@ -0,0 +1,73 @@
|
|||||||
|
# This file is used by the training script in train.ipynb. You can read more about
|
||||||
|
# the format and see more examples at https://github.com/OpenAccess-AI-Collective/axolotl.
|
||||||
|
# One of the parameters you might want to play around with is `num_epochs`: if you have a
|
||||||
|
# smaller dataset size, making that large can have good results.
|
||||||
|
|
||||||
|
base_model: meta-llama/Llama-2-7b-hf
|
||||||
|
base_model_config: meta-llama/Llama-2-7b-hf
|
||||||
|
model_type: LlamaForCausalLM
|
||||||
|
tokenizer_type: LlamaTokenizer
|
||||||
|
is_llama_derived_model: true
|
||||||
|
|
||||||
|
load_in_8bit: true
|
||||||
|
load_in_4bit: false
|
||||||
|
strict: false
|
||||||
|
|
||||||
|
datasets:
|
||||||
|
- path: ./data/train.jsonl
|
||||||
|
type: alpaca_instruct.load_no_prompt
|
||||||
|
dataset_prepared_path: ./data/last_run_prepared
|
||||||
|
val_set_size: 0.05
|
||||||
|
output_dir: ./models/run1
|
||||||
|
|
||||||
|
sequence_len: 4096
|
||||||
|
sample_packing: true
|
||||||
|
|
||||||
|
adapter: lora
|
||||||
|
lora_model_dir:
|
||||||
|
lora_r: 32
|
||||||
|
lora_alpha: 16
|
||||||
|
lora_dropout: 0.05
|
||||||
|
lora_target_linear: true
|
||||||
|
lora_fan_in_fan_out:
|
||||||
|
|
||||||
|
# This will report stats from your training run to https://wandb.ai/. If you don't want to create a wandb account you can comment this section out.
|
||||||
|
wandb_project: classify-recipes
|
||||||
|
wandb_entity:
|
||||||
|
wandb_watch:
|
||||||
|
wandb_run_id: run1
|
||||||
|
wandb_log_model:
|
||||||
|
|
||||||
|
gradient_accumulation_steps: 4
|
||||||
|
micro_batch_size: 2
|
||||||
|
num_epochs: 5
|
||||||
|
optimizer: adamw_bnb_8bit
|
||||||
|
lr_scheduler: cosine
|
||||||
|
learning_rate: 0.0002
|
||||||
|
|
||||||
|
train_on_inputs: false
|
||||||
|
group_by_length: false
|
||||||
|
bf16: true
|
||||||
|
fp16: false
|
||||||
|
tf32: false
|
||||||
|
|
||||||
|
gradient_checkpointing: true
|
||||||
|
early_stopping_patience:
|
||||||
|
resume_from_checkpoint:
|
||||||
|
local_rank:
|
||||||
|
logging_steps: 1
|
||||||
|
xformers_attention:
|
||||||
|
flash_attention: true
|
||||||
|
|
||||||
|
warmup_steps: 10
|
||||||
|
eval_steps: 20
|
||||||
|
save_steps: 60
|
||||||
|
debug:
|
||||||
|
deepspeed:
|
||||||
|
weight_decay: 0.0
|
||||||
|
fsdp:
|
||||||
|
fsdp_config:
|
||||||
|
special_tokens:
|
||||||
|
bos_token: "<s>"
|
||||||
|
eos_token: "</s>"
|
||||||
|
unk_token: "<unk>"
|
||||||
37
examples/classify-recipes/utils.py
Normal file
37
examples/classify-recipes/utils.py
Normal file
@@ -0,0 +1,37 @@
|
|||||||
|
import yaml
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||||
|
import torch
|
||||||
|
from peft import PeftModel
|
||||||
|
import os
|
||||||
|
|
||||||
|
|
||||||
|
def merge(config_file: str):
|
||||||
|
config = yaml.load(open(config_file, "r"), Loader=yaml.FullLoader)
|
||||||
|
|
||||||
|
base_model = config["base_model"]
|
||||||
|
lora_model = config["output_dir"]
|
||||||
|
merged_model = f"{lora_model}/merged"
|
||||||
|
|
||||||
|
if os.path.exists(merged_model):
|
||||||
|
print(f"Model {merged_model} already exists, skipping")
|
||||||
|
return merged_model
|
||||||
|
|
||||||
|
print("Loading base model")
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
base_model,
|
||||||
|
return_dict=True,
|
||||||
|
torch_dtype=torch.float16,
|
||||||
|
)
|
||||||
|
|
||||||
|
print("Loading PEFT model")
|
||||||
|
model = PeftModel.from_pretrained(model, lora_model)
|
||||||
|
print(f"Running merge_and_unload")
|
||||||
|
model = model.merge_and_unload()
|
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(base_model)
|
||||||
|
|
||||||
|
model.save_pretrained(merged_model)
|
||||||
|
tokenizer.save_pretrained(merged_model)
|
||||||
|
print(f"Model saved to {merged_model}")
|
||||||
|
|
||||||
|
return merged_model
|
||||||
Reference in New Issue
Block a user