{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "id": "db8736a7-ed94-441c-9556-831fa57b5a10", "metadata": { "id": "db8736a7-ed94-441c-9556-831fa57b5a10" }, "source": [ "# The Product Pricer Challenge\n", "\n", "A baseline established by gpt4o and attempt to beat it\n" ] }, { "cell_type": "markdown", "source": [ "## Initialize and load configuration" ], "metadata": { "id": "FwYmkcF_Jw4m" }, "id": "FwYmkcF_Jw4m" }, { "cell_type": "code", "execution_count": null, "id": "681c717b-4c24-4ac3-a5f3-3c5881d6e70a", "metadata": { "id": "681c717b-4c24-4ac3-a5f3-3c5881d6e70a" }, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import re\n", "import math\n", "import json\n", "import random\n", "import pickle\n", "\n", "from collections import Counter\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from huggingface_hub import login\n", "from openai import OpenAI\n", "\n", "from items import Item\n", "from testing import Tester" ] }, { "cell_type": "code", "execution_count": null, "id": "36d05bdc-0155-4c72-a7ee-aa4e614ffd3c", "metadata": { "id": "36d05bdc-0155-4c72-a7ee-aa4e614ffd3c" }, "outputs": [], "source": [ "# environment\n", "\n", "try:\n", " from google.colab import userdata\n", " os.environ['OPENAI_API_KEY']=userdata.get('OPENAI_API_KEY')\n", " os.environ['HF_TOKEN']=userdata.get('HF_TOKEN')\n", " print(\"✅ Using Colab secrets\")\n", "except:\n", " from dotenv import load_dotenv\n", " load_dotenv(override=True)\n", " os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", " os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')\n", " print(\"✅ Using local .env file\")" ] }, { "cell_type": "code", "execution_count": null, "id": "4dd3aad2-6f99-433c-8792-e461d2f06622", "metadata": { "id": "4dd3aad2-6f99-433c-8792-e461d2f06622" }, "outputs": [], "source": [ "# Log in to HuggingFace\n", "\n", "hf_token = os.environ['HF_TOKEN']\n", "login(hf_token)" ] }, { "cell_type": "code", "execution_count": null, "id": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b", "metadata": { "id": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b" }, "outputs": [], "source": [ "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": null, "id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278", "metadata": { "id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278" }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "id": "5c9b05f4-c9eb-462c-8d86-de9140a2d985", "metadata": { "id": "5c9b05f4-c9eb-462c-8d86-de9140a2d985" }, "outputs": [], "source": [ "# Let's avoid curating all our data again! Load in the pickle files:\n", "\n", "with open('train2.pkl', 'rb') as file:\n", " train = pickle.load(file)\n", "\n", "with open('test2.pkl', 'rb') as file:\n", " test = pickle.load(file)\n", "\n", "with open('validation2.pkl','rb') as file:\n", " validation = pickle.load(file)" ] }, { "cell_type": "code", "execution_count": null, "id": "e8367135-f40e-43e1-8f3c-09e990ab1194", "metadata": { "id": "e8367135-f40e-43e1-8f3c-09e990ab1194" }, "outputs": [], "source": [ "# OpenAI recommends fine-tuning with populations of 50-100 examples\n", "# But as our examples are very small, I'm suggesting we go with 500 examples (and 1 epoch)\n", "\n", "fine_tune_train = train[:500]\n", "fine_tune_validation = train[500:550]" ] }, { "cell_type": "code", "source": [ "# Weight and Biases\n", "wandb_integration = {\"type\": \"wandb\", \"wandb\": {\"project\": \"gpt-pricer-ft\"}}" ], "metadata": { "id": "xvsvrdivOBCs" }, "id": "xvsvrdivOBCs", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Helpers" ], "metadata": { "id": "Fr5cFugENugL" }, "id": "Fr5cFugENugL" }, { "cell_type": "code", "source": [ "# A utility function to extract the price from a string\n", "\n", "def get_price(s):\n", " s = s.replace('$','').replace(',','')\n", " match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n", " return float(match.group()) if match else 0" ], "metadata": { "id": "rRF5PhHANsTN" }, "id": "rRF5PhHANsTN", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Prompt\n", "def messages_for(item):\n", " system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", " user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n", " return [\n", " {\"role\": \"system\", \"content\": system_message},\n", " {\"role\": \"user\", \"content\": user_prompt},\n", " {\"role\": \"assistant\", \"content\": \"Price is $\"}\n", " ]" ], "metadata": { "id": "-mDWirZLOTxf" }, "id": "-mDWirZLOTxf", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def messages_with_price(item):\n", " system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", " user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n", " return [\n", " {\"role\": \"system\", \"content\": system_message},\n", " {\"role\": \"user\", \"content\": user_prompt},\n", " {\"role\": \"assistant\", \"content\": f\"Price is ${item.price:.2f}\"}\n", " ]" ], "metadata": { "id": "ttaE6iO9SAZX" }, "id": "ttaE6iO9SAZX", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Baseline *gpt4o*\n", "\n", "**Error:** $78.02 RMSLE 0.81 Hits 55.6%" ], "metadata": { "id": "N9hXBrSBI2_q" }, "id": "N9hXBrSBI2_q" }, { "cell_type": "code", "execution_count": null, "id": "03ff4b48-3788-4370-9e34-6592f23d1bce", "metadata": { "id": "03ff4b48-3788-4370-9e34-6592f23d1bce" }, "outputs": [], "source": [ "def gpt_4o_frontier(item):\n", " response = openai.chat.completions.create(\n", " model=\"gpt-4o-2024-08-06\",\n", " messages=messages_for(item),\n", " seed=42,\n", " max_tokens=5\n", " )\n", " reply = response.choices[0].message.content\n", " return get_price(reply)" ] }, { "cell_type": "code", "source": [ "Tester.test(gpt_4o_frontier, test)" ], "metadata": { "id": "ymJRsQKRJAhS" }, "id": "ymJRsQKRJAhS", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Fine tuned baseline *gpt4o*\n", "\n", "**Error:** $105.37 RMSLE 0.84 Hits 41.2%" ], "metadata": { "id": "RZAsscjePPg4" }, "id": "RZAsscjePPg4" }, { "cell_type": "markdown", "id": "8be4a889-81c3-42b1-a2fc-034cdc7321a6", "metadata": { "id": "8be4a889-81c3-42b1-a2fc-034cdc7321a6" }, "source": [ "### Data Preprocessing\n", "\n", "Prepare our data for fine-tuning in JSONL (JSON Lines) format and upload to OpenAI" ] }, { "cell_type": "code", "execution_count": null, "id": "c0e5b56c-8a0b-4d8e-a112-ce87efb4e152", "metadata": { "id": "c0e5b56c-8a0b-4d8e-a112-ce87efb4e152" }, "outputs": [], "source": [ "# Convert the items into a list of json objects - a \"jsonl\" string\n", "# Each row represents a message in the form:\n", "# {\"messages\" : [{\"role\": \"system\", \"content\": \"You estimate prices...\n", "\n", "def make_jsonl(items):\n", " result = \"\"\n", " for item in items:\n", " messages = messages_with_price(item)\n", " messages_str = json.dumps(messages)\n", " result += '{\"messages\": ' + messages_str +'}\\n'\n", " return result.strip()" ] }, { "cell_type": "code", "execution_count": null, "id": "7734bff0-95c4-4e67-a87e-7e2254e2c67d", "metadata": { "id": "7734bff0-95c4-4e67-a87e-7e2254e2c67d" }, "outputs": [], "source": [ "# Convert the items into jsonl and write them to a file\n", "\n", "def write_jsonl(items, filename):\n", " with open(filename, \"w\") as f:\n", " jsonl = make_jsonl(items)\n", " f.write(jsonl)" ] }, { "cell_type": "code", "execution_count": null, "id": "393d3ad8-999a-4f99-8c04-339d9166d604", "metadata": { "id": "393d3ad8-999a-4f99-8c04-339d9166d604" }, "outputs": [], "source": [ "write_jsonl(fine_tune_train, \"fine_tune_train.jsonl\")\n", "write_jsonl(fine_tune_validation, \"fine_tune_validation.jsonl\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d59ad8d2-c61a-448e-b7ed-232f1606970f", "metadata": { "id": "d59ad8d2-c61a-448e-b7ed-232f1606970f" }, "outputs": [], "source": [ "with open(\"fine_tune_train.jsonl\", \"rb\") as f:\n", " train_file = openai.files.create(file=f, purpose=\"fine-tune\")" ] }, { "cell_type": "code", "execution_count": null, "id": "083fefba-fd54-47ce-9ff3-aabbc200846f", "metadata": { "id": "083fefba-fd54-47ce-9ff3-aabbc200846f" }, "outputs": [], "source": [ "train_file" ] }, { "cell_type": "code", "execution_count": null, "id": "97df3360-0760-4422-a556-5f26d23de6dc", "metadata": { "id": "97df3360-0760-4422-a556-5f26d23de6dc" }, "outputs": [], "source": [ "with open(\"fine_tune_validation.jsonl\", \"rb\") as f:\n", " validation_file = openai.files.create(file=f, purpose=\"fine-tune\")" ] }, { "cell_type": "code", "execution_count": null, "id": "a1abb8f3-9e52-4061-970c-fcf399d8ffa3", "metadata": { "id": "a1abb8f3-9e52-4061-970c-fcf399d8ffa3" }, "outputs": [], "source": [ "validation_file" ] }, { "cell_type": "markdown", "source": [ "### Fine Tune the model" ], "metadata": { "id": "MXXCe72aKdfR" }, "id": "MXXCe72aKdfR" }, { "cell_type": "code", "execution_count": null, "id": "45421b86-5531-4e42-ab19-d6abbb8f4c13", "metadata": { "id": "45421b86-5531-4e42-ab19-d6abbb8f4c13" }, "outputs": [], "source": [ "openai.fine_tuning.jobs.create(\n", " training_file=train_file.id,\n", " validation_file=validation_file.id,\n", " model=\"gpt-4o-mini-2024-07-18\",\n", " seed=42,\n", " hyperparameters={\"n_epochs\": 1},\n", " integrations = [wandb_integration],\n", " suffix=\"pricer\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "aeb9de2e-542c-4e83-81c7-b6745133e48b", "metadata": { "id": "aeb9de2e-542c-4e83-81c7-b6745133e48b" }, "outputs": [], "source": [ "openai.fine_tuning.jobs.list(limit=1)" ] }, { "cell_type": "code", "execution_count": null, "id": "40d24873-8ff5-413f-b0d4-8f77c28f18e1", "metadata": { "id": "40d24873-8ff5-413f-b0d4-8f77c28f18e1" }, "outputs": [], "source": [ "job_id = openai.fine_tuning.jobs.list(limit=1).data[0].id" ] }, { "cell_type": "code", "execution_count": null, "id": "a32aef35-4b38-436c-ad00-d082f758efa7", "metadata": { "id": "a32aef35-4b38-436c-ad00-d082f758efa7" }, "outputs": [], "source": [ "job_id" ] }, { "cell_type": "code", "execution_count": null, "id": "a7e01247-c133-48e1-93d3-c79c399e6178", "metadata": { "id": "a7e01247-c133-48e1-93d3-c79c399e6178" }, "outputs": [], "source": [ "openai.fine_tuning.jobs.retrieve(job_id)" ] }, { "cell_type": "code", "execution_count": null, "id": "0f5150e1-b8de-485f-8eba-cf1e5b00c117", "metadata": { "id": "0f5150e1-b8de-485f-8eba-cf1e5b00c117" }, "outputs": [], "source": [ "openai.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10).data" ] }, { "cell_type": "markdown", "id": "066fef03-8338-4526-9df3-89b649ad4f0a", "metadata": { "id": "066fef03-8338-4526-9df3-89b649ad4f0a" }, "source": [ "### Run inference on the fine tune model" ] }, { "cell_type": "code", "execution_count": null, "id": "fa4488cb-3c17-4eda-abd1-53c1c68a491b", "metadata": { "id": "fa4488cb-3c17-4eda-abd1-53c1c68a491b" }, "outputs": [], "source": [ "fine_tuned_model_name = openai.fine_tuning.jobs.retrieve(job_id).fine_tuned_model" ] }, { "cell_type": "code", "execution_count": null, "id": "4ff92d61-0d27-4b0d-8b32-c9891016509b", "metadata": { "id": "4ff92d61-0d27-4b0d-8b32-c9891016509b" }, "outputs": [], "source": [ "# Try this out\n", "\n", "messages_for(test[237])" ] }, { "cell_type": "code", "execution_count": null, "id": "501a2a7a-69c8-451b-bbc0-398bcb9e1612", "metadata": { "id": "501a2a7a-69c8-451b-bbc0-398bcb9e1612" }, "outputs": [], "source": [ "# The function for gpt-4o fine tuned\n", "\n", "def gpt_fine_tuned(item):\n", " response = openai.chat.completions.create(\n", " model=fine_tuned_model_name,\n", " messages=messages_for(item),\n", " seed=42,\n", " max_tokens=7\n", " )\n", " reply = response.choices[0].message.content\n", " return get_price(reply)" ] }, { "cell_type": "code", "execution_count": null, "id": "843d88b4-364a-431b-b48b-8a7c1f68b786", "metadata": { "id": "843d88b4-364a-431b-b48b-8a7c1f68b786" }, "outputs": [], "source": [ "print(test[237].price)\n", "print(gpt_fine_tuned(test[237]))" ] }, { "cell_type": "code", "execution_count": null, "id": "36bdd2c9-1859-4f99-a09f-3ec83b845b30", "metadata": { "id": "36bdd2c9-1859-4f99-a09f-3ec83b845b30" }, "outputs": [], "source": [ "Tester.test(gpt_fine_tuned, test)" ] }, { "cell_type": "markdown", "source": [ "## gpt 4.1 base\n", "**Error:** $70.36 RMSLE=0.522 Hits=64.4%" ], "metadata": { "id": "EF9S1_dBEVAc" }, "id": "EF9S1_dBEVAc" }, { "cell_type": "code", "source": [ "def gpt_4_1_frontier(item):\n", " response = openai.chat.completions.create(\n", " model=\"gpt-4.1-2025-04-14\",\n", " messages=messages_for(item),\n", " seed=42,\n", " max_completion_tokens=7\n", " )\n", " reply = response.choices[0].message.content\n", " return get_price(reply)" ], "metadata": { "id": "QRIelvwmEZgw" }, "id": "QRIelvwmEZgw", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "Tester.test(gpt_4_1_frontier, test)" ], "metadata": { "id": "2vwrkA_4Eon6" }, "id": "2vwrkA_4Eon6", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### gpt 4.1 hypertuned , extended dataset\n", "**Error**: $67.93, RMSLE=0.47, Hits 68.8" ], "metadata": { "id": "L0-cps4dLg0S" }, "id": "L0-cps4dLg0S" }, { "cell_type": "code", "source": [ "def gpt_4_1_hypertuned(item):\n", " response = openai.chat.completions.create(\n", " model=\"gpt-4.1-2025-04-14\",\n", " messages=messages_v2(item, with_price=False),\n", " seed=42,\n", " temperature=0.2,\n", " max_completion_tokens=7\n", " )\n", " reply = response.choices[0].message.content\n", " return get_price(reply)" ], "metadata": { "id": "ZptbHZN3LilR" }, "id": "ZptbHZN3LilR", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "Tester.test(gpt_4_1_hypertuned, test)" ], "metadata": { "id": "CtBfsCixLgSe" }, "id": "CtBfsCixLgSe", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## gpt-5 base\n", "**Error:** $77.97 RMLSE=0.53 Hits=61.6% (reasoning_effort=\"minimal\"\n", "\n", "**Error:** $70.63 RMLSE=0.51 Hits=61.6% (reasoning_effort=\"low\"" ], "metadata": { "id": "LQiDcxk3pNc4" }, "id": "LQiDcxk3pNc4" }, { "cell_type": "code", "source": [ "def gpt_5_frontier(item):\n", " response = openai.chat.completions.create(\n", " model=\"gpt-5-2025-08-07\",\n", " messages=messages_for(item),\n", " seed=42,\n", " reasoning_effort=\"low\",\n", " max_completion_tokens=800\n", " )\n", " reply = response.choices[0].message.content\n", " return get_price(reply)" ], "metadata": { "id": "nZk45Bujp4aS" }, "id": "nZk45Bujp4aS", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "Tester.test(gpt_5_frontier, test)" ], "metadata": { "id": "9wx-0BT_p-j_" }, "id": "9wx-0BT_p-j_", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## gpt 4.1 fine-tuned with extended dataset" ], "metadata": { "id": "mOXvulp11NRS" }, "id": "mOXvulp11NRS" }, { "cell_type": "markdown", "source": [ "### Data Preprocessing" ], "metadata": { "id": "hY0v7oBR1brc" }, "id": "hY0v7oBR1brc" }, { "cell_type": "code", "source": [ "\n", "def messages_v2(item, with_price=True):\n", " system_message = \\\n", " \"Role: You are a retail price estimator.\\n\" \\\n", " \"Market: United States; Currency: USD.\\n\" \\\n", " \"Scope: Predict the most likely new retail price. Ignore taxes, shipping, coupons, bundles, used/renewed.\\n\" \\\n", " \"Output: Only a number with two decimals (e.g., 129.99). No $ sign. No words.\\n\" \\\n", " \"Think silently; do not reveal reasoning.\"\n", "\n", " user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n", " return [\n", " {\"role\": \"system\", \"content\": system_message},\n", " {\"role\": \"user\", \"content\": str({\n", " \"query\":\"price_estimate\",\n", " \"locale\":\"en_US\",\n", " \"currency\":\"USD\",\n", " \"category\":item.category,\n", " \"description\":user_prompt,\n", " \"brand\":json.loads(item.details).get(\"Brand\",\"Unknown\")\n", " })\n", " },\n", " {\"role\": \"assistant\", \"content\": f\"Price is ${item.price:.2f}\" if with_price else \"Price is $\"}\n", " ]" ], "metadata": { "id": "dAqEb7GD2HJZ" }, "id": "dAqEb7GD2HJZ", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "messages_v2(test[237], with_price=False)\n" ], "metadata": { "id": "wRqFRHzE_LPm" }, "id": "wRqFRHzE_LPm", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_jsonl(items):\n", " result = \"\"\n", " for item in items:\n", " messages = messages_v2(item)\n", " messages_str = json.dumps(messages)\n", " result += '{\"messages\": ' + messages_str +'}\\n'\n", " return result.strip()" ], "metadata": { "id": "CIkBy83R1T_J" }, "id": "CIkBy83R1T_J", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Convert the items into jsonl and write them to a file\n", "\n", "def write_jsonl(items, filename):\n", " with open(filename, \"w\") as f:\n", " jsonl = make_jsonl(items)\n", " f.write(jsonl)" ], "metadata": { "id": "WBkFmTvb1hwI" }, "id": "WBkFmTvb1hwI", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "write_jsonl(fine_tune_train, \"fine_tune_train_v2.jsonl\")\n", "write_jsonl(fine_tune_validation, \"fine_tune_validation_v2.jsonl\")" ], "metadata": { "id": "7YbhOEZA1lhm" }, "id": "7YbhOEZA1lhm", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "with open(\"fine_tune_train_v2.jsonl\", \"rb\") as f:\n", " train_file_v2 = openai.files.create(file=f, purpose=\"fine-tune\")\n", "\n", "with open(\"fine_tune_validation_v2.jsonl\", \"rb\") as f:\n", " validation_file_v2 = openai.files.create(file=f, purpose=\"fine-tune\")" ], "metadata": { "id": "n62FQj701ntK" }, "id": "n62FQj701ntK", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Fine tune the model" ], "metadata": { "id": "CvqTsT3w547n" }, "id": "CvqTsT3w547n" }, { "cell_type": "code", "source": [ "openai.fine_tuning.jobs.create(\n", " training_file=train_file_v2.id,\n", " validation_file=validation_file_v2.id,\n", " model=\"gpt-4.1-2025-04-14\",\n", " seed=42,\n", " hyperparameters={\"n_epochs\": 1},\n", " integrations = [wandb_integration],\n", " suffix=\"pricer\"\n", ")" ], "metadata": { "id": "V4hVbBhi58_k" }, "id": "V4hVbBhi58_k", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "openai.fine_tuning.jobs.list(limit=1)" ], "metadata": { "id": "QdUK7rYd6X7J" }, "id": "QdUK7rYd6X7J", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "job_id = openai.fine_tuning.jobs.list(limit=1).data[0].id" ], "metadata": { "id": "lpOvwnI36Y7m" }, "id": "lpOvwnI36Y7m", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "openai.fine_tuning.jobs.retrieve(job_id)" ], "metadata": { "id": "YYL0Thpw6ZoU" }, "id": "YYL0Thpw6ZoU", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "openai.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10).data" ], "metadata": { "id": "ZjYZl4eo6jDL" }, "id": "ZjYZl4eo6jDL", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Inference" ], "metadata": { "id": "ZuGvgAwX6p5N" }, "id": "ZuGvgAwX6p5N" }, { "cell_type": "code", "source": [ "fine_tuned_model_name = openai.fine_tuning.jobs.retrieve(job_id).fine_tuned_model" ], "metadata": { "id": "YiPB6tOx6je6" }, "id": "YiPB6tOx6je6", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def gpt_41fine_tuned(item):\n", " response = openai.chat.completions.create(\n", " model=fine_tuned_model_name,\n", " messages=messages_v2(item, with_price=False),\n", " seed=42,\n", " temperature=1.0,\n", " max_tokens=7\n", " )\n", " reply = response.choices[0].message.content\n", " return get_price(reply)" ], "metadata": { "id": "NQy00Zx065yT" }, "id": "NQy00Zx065yT", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "Tester.test(gpt_41fine_tuned, test)" ], "metadata": { "id": "bUVakvwgUa0Y" }, "id": "bUVakvwgUa0Y", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "###" ], "metadata": { "id": "ZxQokpS95n-5" }, "id": "ZxQokpS95n-5" } ], "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": "3.11.11" }, "colab": { "provenance": [], "include_colab_link": true } }, "nbformat": 4, "nbformat_minor": 5 }