diff --git "a/eval.ipynb" "b/eval.ipynb"
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+++ "b/eval.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1b1ffa69",
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:28:13.015522Z",
+     "iopub.status.busy": "2023-10-22T00:28:13.014853Z",
+     "iopub.status.idle": "2023-10-22T00:28:13.028171Z",
+     "shell.execute_reply": "2023-10-22T00:28:13.027305Z"
+    },
+    "papermill": {
+     "duration": 1.018587,
+     "end_time": "2023-10-22T00:28:13.029998",
+     "exception": false,
+     "start_time": "2023-10-22T00:28:12.011411",
+     "status": "completed"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'hf_dataset': 'weaviate/WithoutRetrieval-SchemaSplit-Test-40',\n",
+       " 'prompt_template': '## Instruction\\nYour task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\\n\\nOnly use the API reference to understand the syntax of the request.\\n\\n## Natural Language Query\\n{nlcommand}\\n\\n## Schema\\n{schema}\\n\\n## API reference\\n{apiRef}\\n\\n## Answer\\n```graphql\\n',\n",
+       " 'push_to_hub': 'substratusai/wgql-WithRetrieval-SchemaSplit-Train-40'}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "params = {}\n",
+    "params_path = Path(\"/content/params.json\")\n",
+    "if params_path.is_file():\n",
+    "    with params_path.open(\"r\", encoding=\"UTF-8\") as params_file:\n",
+    "        params = json.load(params_file)\n",
+    "\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "03e6edac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:28:15.921347Z",
+     "iopub.status.busy": "2023-10-22T00:28:15.920731Z",
+     "iopub.status.idle": "2023-10-22T00:28:21.687841Z",
+     "shell.execute_reply": "2023-10-22T00:28:21.686683Z"
+    },
+    "papermill": {
+     "duration": 7.727398,
+     "end_time": "2023-10-22T00:28:21.690089",
+     "exception": false,
+     "start_time": "2023-10-22T00:28:13.962691",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e30a6a57f2e240ad871ceaa6a9106881",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "864c0d3b6e4b45c8a6ddea96d227626a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17932f64d394491a82507516a1536074",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/11.6M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "105795bc322d400f926f674750ff9be8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bcd908ef38c4200a2eee90fc56234f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['input', 'output', 'nlcommand', 'apiRef', 'apiRefPath', 'schema', 'schemaPath'],\n",
+       "        num_rows: 2435\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "hf_dataset = params.get(\"hf_dataset\")\n",
+    "if hf_dataset:\n",
+    "    dataset = load_dataset(hf_dataset)\n",
+    "else:\n",
+    "    dataset = load_dataset(\"json\", data_files=\"/content/data/*.json*\")\n",
+    "\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a5af52cc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:28:23.581372Z",
+     "iopub.status.busy": "2023-10-22T00:28:23.580262Z",
+     "iopub.status.idle": "2023-10-22T00:28:23.588353Z",
+     "shell.execute_reply": "2023-10-22T00:28:23.587317Z"
+    },
+    "papermill": {
+     "duration": 0.979354,
+     "end_time": "2023-10-22T00:28:23.590432",
+     "exception": false,
+     "start_time": "2023-10-22T00:28:22.611078",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## Instruction\n",
+      "Your task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\n",
+      "\n",
+      "Only use the API reference to understand the syntax of the request.\n",
+      "\n",
+      "## Natural Language Query\n",
+      "```text\n",
+      "Show me the event name, description, year, significant impact, and the countries involved with their population for the top 10 historical events.\n",
+      "```\n",
+      "\n",
+      "## Schema\n",
+      "{\n",
+      "\"classes\": [\n",
+      "{\n",
+      "\"class\": \"HistoricalEvent\",\n",
+      "\"description\": \"Information about historical events\",\n",
+      "\"vectorIndexType\": \"hnsw\",\n",
+      "\"vectorizer\": \"text2vec-transformers\",\n",
+      "\"properties\": [\n",
+      "{\n",
+      "\"name\": \"eventName\",\n",
+      "\"dataType\": [\"text\"],\n",
+      "\"description\": \"Name of the historical event\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"description\",\n",
+      "\"dataType\": [\"text\"],\n",
+      "\"description\": \"Detailed description of the event\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"year\",\n",
+      "\"dataType\": [\"int\"],\n",
+      "\"description\": \"Year the event occurred\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"hadSignificantImpact\",\n",
+      "\"dataType\": [\"boolean\"],\n",
+      "\"description\": \"Whether the event had a significant impact\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"involvedCountries\",\n",
+      "\"dataType\": [\"Country\"],\n",
+      "\"description\": \"Countries involved in the event\"\n",
+      "}{\n",
+      "\"class\": \"Country\",\n",
+      "\"description\": \"Information about countries\",\n",
+      "\"vectorIndexType\": \"hnsw\",\n",
+      "\"vectorizer\": \"text2vec-transformers\",\n",
+      "\"properties\": [\n",
+      "{\n",
+      "\"name\": \"countryName\",\n",
+      "\"dataType\": [\"text\"],\n",
+      "\"description\": \"Name of the country\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"population\",\n",
+      "\"dataType\": [\"int\"],\n",
+      "\"description\": \"Population of the country\"\n",
+      "}}}\n",
+      "\n",
+      "## API reference\n",
+      "`limit` returned objects\n",
+      "\n",
+      "Often, you will only want the top `n` results from the query. This can be achieved by setting a `limit` as shown below.\n",
+      "\n",
+      "```graphql\n",
+      "{\n",
+      "  Get {\n",
+      "    JeopardyQuestion (\n",
+      "      limit: 1\n",
+      "    ) {\n",
+      "      question\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "## Answer\n",
+      "```graphql\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "default_prompt = \"\"\"\n",
+    "## Instruction\n",
+    "Your task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\n",
+    "\n",
+    "Only use the API reference to understand the syntax of the request.\n",
+    "\n",
+    "## Natural Language Query\n",
+    "{nlcommand}\n",
+    "\n",
+    "## Schema\n",
+    "{schema}\n",
+    "\n",
+    "## API reference\n",
+    "{apiRef}\n",
+    "\n",
+    "## Answer\n",
+    "```graphql\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt = params.get(\"prompt_template\", default_prompt)\n",
+    "print(prompt.format_map(dataset[\"train\"][0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "28077589",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:28:25.940339Z",
+     "iopub.status.busy": "2023-10-22T00:28:25.939556Z",
+     "iopub.status.idle": "2023-10-22T00:36:08.128576Z",
+     "shell.execute_reply": "2023-10-22T00:36:08.127872Z"
+    },
+    "papermill": {
+     "duration": 463.135464,
+     "end_time": "2023-10-22T00:36:08.130311",
+     "exception": false,
+     "start_time": "2023-10-22T00:28:24.994847",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6b9ffcbfb4545c5a28746b12de2703c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.04k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e43b46dc8053470e89221e9928804030",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22f8b79f9be349a387d862efbe67100f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c445d1d33744ec1b6954dbf3649e3cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)in/added_tokens.json:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dee1a66f95df4822b9021ea2ec2c9a57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/552 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f2b7ec84e55468b80ad864e64c5a33a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)lve/main/config.json:   0%|          | 0.00/648 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43f6b28dc6644bcda2955cf044d8212c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)model.bin.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0bed752d482455bb069655298541396",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ab344386b804fe997a46b4daae44aa8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)l-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5323a75769f49cfb6fc5d3675a13d72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)l-00002-of-00002.bin:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63a9b372a3c248f5ac56b5e85a0e5e4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fd987173a1c417095cd36b11b0ca63c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)neration_config.json:   0%|          | 0.00/183 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import transformers\n",
+    "import torch\n",
+    "import sys\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "\n",
+    "model_path = \"/content/model/\"\n",
+    "model_id = params[\"push_to_hub\"]\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "            model_id, device_map=\"auto\", trust_remote_code=True,\n",
+    "            torch_dtype=torch.bfloat16, \n",
+    "            use_flash_attention_2=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e3867be8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:09.968377Z",
+     "iopub.status.busy": "2023-10-22T00:36:09.967290Z",
+     "iopub.status.idle": "2023-10-22T00:36:10.314086Z",
+     "shell.execute_reply": "2023-10-22T00:36:10.313206Z"
+    },
+    "papermill": {
+     "duration": 1.286863,
+     "end_time": "2023-10-22T00:36:10.316084",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:09.029221",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sun Oct 22 00:36:10 2023       \r\n",
+      "+-----------------------------------------------------------------------------+\r\n",
+      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\r\n",
+      "|-------------------------------+----------------------+----------------------+\r\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
+      "|                               |                      |               MIG M. |\r\n",
+      "|===============================+======================+======================|\r\n",
+      "|   0  NVIDIA L4           Off  | 00000000:00:04.0 Off |                    0 |\r\n",
+      "| N/A   39C    P0    28W /  72W |  13610MiB / 23034MiB |      0%      Default |\r\n",
+      "|                               |                      |                  N/A |\r\n",
+      "+-------------------------------+----------------------+----------------------+\r\n",
+      "                                                                               \r\n",
+      "+-----------------------------------------------------------------------------+\r\n",
+      "| Processes:                                                                  |\r\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
+      "|        ID   ID                                                   Usage      |\r\n",
+      "|=============================================================================|\r\n",
+      "+-----------------------------------------------------------------------------+\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "! nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2f1160ad",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:12.243834Z",
+     "iopub.status.busy": "2023-10-22T00:36:12.243504Z",
+     "iopub.status.idle": "2023-10-22T00:36:12.249718Z",
+     "shell.execute_reply": "2023-10-22T00:36:12.248885Z"
+    },
+    "papermill": {
+     "duration": 0.949772,
+     "end_time": "2023-10-22T00:36:12.251865",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:11.302093",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GenerationConfig {\n",
+       "  \"bos_token_id\": 1,\n",
+       "  \"do_sample\": true,\n",
+       "  \"eos_token_id\": 2,\n",
+       "  \"max_length\": 4096,\n",
+       "  \"pad_token_id\": 0,\n",
+       "  \"temperature\": 0.6,\n",
+       "  \"top_p\": 0.9\n",
+       "}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device = \"cuda\"\n",
+    "model.generation_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c43a6541",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:15.454073Z",
+     "iopub.status.busy": "2023-10-22T00:36:15.453240Z",
+     "iopub.status.idle": "2023-10-22T00:36:15.460810Z",
+     "shell.execute_reply": "2023-10-22T00:36:15.460003Z"
+    },
+    "papermill": {
+     "duration": 2.323745,
+     "end_time": "2023-10-22T00:36:15.463103",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:13.139358",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([7521])\n",
+      "False\n",
+      "False\n",
+      "```\n"
+     ]
+    }
+   ],
+   "source": [
+    "stop_ids = torch.LongTensor(tokenizer.encode(\"```\", add_special_tokens=False))\n",
+    "## Note the stop_ids aren't correct, for some reason there are multiple possible token IDs for ```\n",
+    "## so instead we're using tensor([13940, 28832], device='cuda:0') as the stop_ids, because that's\n",
+    "## what the model normally generates\n",
+    "print(stop_ids)\n",
+    "print(tokenizer.decode([8789]) == \"```\")\n",
+    "print(tokenizer.decode([13940, 28832]) == \"```\")\n",
+    "print(tokenizer.decode(tokenizer.encode(\"```\", add_special_tokens=False)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3b8c79dd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:22.591088Z",
+     "iopub.status.busy": "2023-10-22T00:36:22.590774Z",
+     "iopub.status.idle": "2023-10-22T00:36:22.596788Z",
+     "shell.execute_reply": "2023-10-22T00:36:22.595964Z"
+    },
+    "papermill": {
+     "duration": 0.940364,
+     "end_time": "2023-10-22T00:36:22.598451",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:21.658087",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import StoppingCriteria, StoppingCriteriaList\n",
+    "\n",
+    "class BacktickStoppingCriteria(StoppingCriteria):\n",
+    "    def __init__(self, tokenizer):\n",
+    "        self.tokenizer = tokenizer\n",
+    "\n",
+    "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:\n",
+    "        if self.tokenizer.decode(input_ids[0][-2:]) == \"```\" or self.tokenizer.decode(input_ids[0][-1]) == \"```\":\n",
+    "            return True\n",
+    "        return False\n",
+    "\n",
+    "\n",
+    "\n",
+    "stopping_criteria = StoppingCriteriaList([BacktickStoppingCriteria(tokenizer)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "61c83e3a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:24.638409Z",
+     "iopub.status.busy": "2023-10-22T00:36:24.638071Z",
+     "iopub.status.idle": "2023-10-22T00:36:24.642461Z",
+     "shell.execute_reply": "2023-10-22T00:36:24.641662Z"
+    },
+    "papermill": {
+     "duration": 1.118823,
+     "end_time": "2023-10-22T00:36:24.644081",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:23.525258",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model.config.bos_token_id = tokenizer.bos_token_id = 1\n",
+    "model.config.eos_token_id = tokenizer.eos_token_id = 2\n",
+    "model.config.pad_token_id = tokenizer.pad_token_id = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "640930f5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:27.130307Z",
+     "iopub.status.busy": "2023-10-22T00:36:27.129487Z",
+     "iopub.status.idle": "2023-10-22T00:36:34.776045Z",
+     "shell.execute_reply": "2023-10-22T00:36:34.775365Z"
+    },
+    "papermill": {
+     "duration": 9.230333,
+     "end_time": "2023-10-22T00:36:34.778741",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:25.548408",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## Instruction\n",
+      "Your task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\n",
+      "\n",
+      "Only use the API reference to understand the syntax of the request.\n",
+      "\n",
+      "## Natural Language Query\n",
+      "```text\n",
+      "Show me the event name, description, year, significant impact, and the countries involved with their population for the top 10 historical events.\n",
+      "```\n",
+      "\n",
+      "## Schema\n",
+      "{\n",
+      "\"classes\": [\n",
+      "{\n",
+      "\"class\": \"HistoricalEvent\",\n",
+      "\"description\": \"Information about historical events\",\n",
+      "\"vectorIndexType\": \"hnsw\",\n",
+      "\"vectorizer\": \"text2vec-transformers\",\n",
+      "\"properties\": [\n",
+      "{\n",
+      "\"name\": \"eventName\",\n",
+      "\"dataType\": [\"text\"],\n",
+      "\"description\": \"Name of the historical event\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"description\",\n",
+      "\"dataType\": [\"text\"],\n",
+      "\"description\": \"Detailed description of the event\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"year\",\n",
+      "\"dataType\": [\"int\"],\n",
+      "\"description\": \"Year the event occurred\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"hadSignificantImpact\",\n",
+      "\"dataType\": [\"boolean\"],\n",
+      "\"description\": \"Whether the event had a significant impact\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"involvedCountries\",\n",
+      "\"dataType\": [\"Country\"],\n",
+      "\"description\": \"Countries involved in the event\"\n",
+      "}{\n",
+      "\"class\": \"Country\",\n",
+      "\"description\": \"Information about countries\",\n",
+      "\"vectorIndexType\": \"hnsw\",\n",
+      "\"vectorizer\": \"text2vec-transformers\",\n",
+      "\"properties\": [\n",
+      "{\n",
+      "\"name\": \"countryName\",\n",
+      "\"dataType\": [\"text\"],\n",
+      "\"description\": \"Name of the country\"\n",
+      "},\n",
+      "{\n",
+      "\"name\": \"population\",\n",
+      "\"dataType\": [\"int\"],\n",
+      "\"description\": \"Population of the country\"\n",
+      "}}}\n",
+      "\n",
+      "## API reference\n",
+      "`limit` returned objects\n",
+      "\n",
+      "Often, you will only want the top `n` results from the query. This can be achieved by setting a `limit` as shown below.\n",
+      "\n",
+      "```graphql\n",
+      "{\n",
+      "  Get {\n",
+      "    JeopardyQuestion (\n",
+      "      limit: 1\n",
+      "    ) {\n",
+      "      question\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "## Answer\n",
+      "```graphql\n",
+      "{\n",
+      "  Get {\n",
+      "    HistoricalEvent (\n",
+      "      limit: 10\n",
+      "    ) {\n",
+      "      eventName\n",
+      "      description\n",
+      "      year\n",
+      "      hadSignificantImpact\n",
+      "      involvedCountries {\n",
+      "        ... on Country {\n",
+      "          countryName\n",
+      "          population\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "```\n",
+      "CPU times: user 6.51 s, sys: 720 ms, total: 7.23 s\n",
+      "Wall time: 7.64 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "device = \"cuda\"\n",
+    "model_inputs = tokenizer([prompt.format_map(dataset[\"train\"][0])],\n",
+    "                         return_tensors=\"pt\").to(device)\n",
+    "\n",
+    "generated_ids = model.generate(**model_inputs,\n",
+    "                               max_new_tokens=300,\n",
+    "                               stopping_criteria=stopping_criteria)\n",
+    "\n",
+    "print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ec554d98",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:36.694602Z",
+     "iopub.status.busy": "2023-10-22T00:36:36.693742Z",
+     "iopub.status.idle": "2023-10-22T00:36:36.699010Z",
+     "shell.execute_reply": "2023-10-22T00:36:36.698367Z"
+    },
+    "papermill": {
+     "duration": 0.944849,
+     "end_time": "2023-10-22T00:36:36.701454",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:35.756605",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 534])\n",
+      "{\n",
+      "  Get {\n",
+      "    HistoricalEvent (\n",
+      "      limit: 10\n",
+      "    ) {\n",
+      "      eventName\n",
+      "      description\n",
+      "      year\n",
+      "      hadSignificantImpact\n",
+      "      involvedCountries {\n",
+      "        ... on Country {\n",
+      "          countryName\n",
+      "          population\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model_inputs[\"input_ids\"].shape)\n",
+    "input_length = model_inputs[\"input_ids\"].shape[1]\n",
+    "print(tokenizer.decode(generated_ids[0][input_length:], skip_special_tokens=True).strip(\"```\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "82b19009",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-22T00:36:38.614265Z",
+     "iopub.status.busy": "2023-10-22T00:36:38.613245Z",
+     "iopub.status.idle": "2023-10-22T04:24:28.193454Z",
+     "shell.execute_reply": "2023-10-22T04:24:28.192602Z"
+    },
+    "papermill": {
+     "duration": 13670.572956,
+     "end_time": "2023-10-22T04:24:28.195700",
+     "exception": false,
+     "start_time": "2023-10-22T00:36:37.622744",
+     "status": "completed"
+    },
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running inference for 2435 entries in dataset\n",
+      "entry 1 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 3 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 4 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 5 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 6 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 7 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 8 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 9 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 10 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 11 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 12 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 13 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 14 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 15 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 16 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 17 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 18 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 19 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 20 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 21 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 22 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 23 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 24 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 25 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 26 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 27 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 28 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 29 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 30 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 31 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 32 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 33 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 34 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 35 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 36 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 37 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 38 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 39 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 40 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 41 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 42 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 43 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 44 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 45 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 46 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 47 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 48 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 49 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 50 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 51 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 52 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 53 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 54 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 55 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 56 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 57 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 58 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 59 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 60 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 61 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 62 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 63 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 64 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 65 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 66 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 67 of 2435\n"
+     ]
+    },
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+      "entry 1878 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1879 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1880 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1881 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1882 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1883 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1884 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1885 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1886 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1887 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1888 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1889 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1890 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1891 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1892 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1893 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1894 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1895 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1896 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1897 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1898 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1899 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1900 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1901 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1902 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1903 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1904 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1905 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1906 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1907 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1908 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1909 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1910 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1911 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1912 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1913 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1914 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1915 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1916 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1917 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1918 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1919 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1920 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1921 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1922 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1923 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1924 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1925 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1926 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1927 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1928 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1929 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1930 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1931 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1932 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1933 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1934 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1935 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1936 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1937 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1938 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1939 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1940 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1941 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1942 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1943 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1944 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1945 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1946 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1947 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1948 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1949 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1950 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1951 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1952 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1953 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1954 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1955 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1956 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1957 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1958 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1959 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1960 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1961 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1962 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1963 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1964 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1965 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1966 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1967 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1968 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1969 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1970 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1971 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1972 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1973 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1974 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1975 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1976 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1977 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1978 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1979 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1980 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1981 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1982 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1983 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1984 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1985 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1986 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1987 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1988 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1989 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1990 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1991 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1992 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1993 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1994 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1995 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1996 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1997 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1998 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 1999 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2000 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2001 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2002 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2003 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2004 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2005 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2006 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2007 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2008 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2009 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2010 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2011 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2012 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2013 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2014 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2015 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2016 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2017 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2018 of 2435\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2435 of 2435\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "dataset_size = len(dataset[\"train\"])\n",
+    "output_path = \"/content/artifacts/test-output.json\"\n",
+    "entries = []\n",
+    "print(f\"Running inference for {dataset_size} entries in dataset\")\n",
+    "for i in range(dataset_size):\n",
+    "    print(f\"entry {i+1} of {dataset_size}\")\n",
+    "    entry = dataset[\"train\"][i]\n",
+    "    model_inputs = tokenizer([prompt.format_map(entry)],\n",
+    "                         return_tensors=\"pt\").to(device)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    generated_ids = model.generate(**model_inputs,\n",
+    "                               max_new_tokens=300,\n",
+    "                               stopping_criteria=stopping_criteria)\n",
+    "    input_length = model_inputs[\"input_ids\"].shape[1]\n",
+    "    output = tokenizer.decode(generated_ids[0][input_length:], skip_special_tokens=True)\n",
+    "    entry[\"modelOutput\"] = output.strip(\"```\")\n",
+    "    entries.append(entry)\n",
+    "\n",
+    "    with open(output_path, 'a') as file:\n",
+    "        json.dump(entry, file)\n",
+    "        file.write(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fcc115",
+   "metadata": {
+    "papermill": {
+     "duration": 1.343179,
+     "end_time": "2023-10-22T04:24:31.126816",
+     "exception": false,
+     "start_time": "2023-10-22T04:24:29.783637",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Store the test dataset with model output in the original HuggingFace Model repo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53ade791",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": false,
+     "start_time": "2023-10-22T04:24:32.713441",
+     "status": "running"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "repo_id = params.get(\"push_to_hub\")\n",
+    "if repo_id:\n",
+    "    hf_api = HfApi()\n",
+    "    hf_api.upload_file(\n",
+    "            path_or_fileobj=Path(output_path),\n",
+    "            path_in_repo=Path(output_path).name,\n",
+    "            repo_id=repo_id,\n",
+    "    )\n",
+    "    logs_path = Path(\"/content/artifacts/eval.ipynb\")\n",
+    "    if logs_path.exists():\n",
+    "        hf_api.upload_file(\n",
+    "            path_or_fileobj=logs_path,\n",
+    "            path_in_repo=logs_path.name,\n",
+    "            repo_id=repo_id,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6634eb9",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Execute the model output on a live Weaviate cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f18d161",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import weaviate\n",
+    "from weaviate.embedded import EmbeddedOptions\n",
+    "\n",
+    "client = weaviate.Client(\n",
+    "    embedded_options=EmbeddedOptions()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93f1e832",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "! cat ToySchemas/{dataset[\"train\"][0][\"schemaPath\"]}\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9c223c7",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import Dict\n",
+    "import json\n",
+    "def json_reader(file_path):\n",
+    "    with open(file_path, 'r', encoding='utf-8', errors='replace') as api_ref_fh:\n",
+    "        data = json.load(api_ref_fh)\n",
+    "    return data\n",
+    "\n",
+    "def remove_vectorizer(classes: list[Dict]) -> list[Dict]:\n",
+    "    new_list = []\n",
+    "    for c in classes:\n",
+    "        if \"vectorizer\" in c:\n",
+    "            del c[\"vectorizer\"]\n",
+    "        new_list.append(c)\n",
+    "    return new_list\n",
+    "\n",
+    "def didItExecute(schemaPath, modelOutput):\n",
+    "    client.schema.delete_all()\n",
+    "    schema = json_reader(f'ToySchemas/{schemaPath}')\n",
+    "    schema[\"classes\"] = remove_vectorizer(schema[\"classes\"])\n",
+    "    client.schema.create(schema)\n",
+    "    WeaviateResponse = client.query.raw(modelOutput)\n",
+    "    return WeaviateResponse\n",
+    "\n",
+    "sample = entries[0]\n",
+    "didItExecute(sample[\"schemaPath\"], sample[\"modelOutput\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00a85c65",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "counter = 1\n",
+    "successfulQueries = []\n",
+    "failedQueries = []\n",
+    "failedAPIsCount = {}\n",
+    "failedSchemasCount = {}\n",
+    "for idx, example in enumerate(entries):\n",
+    "    failed = False\n",
+    "    modelQuery = example[\"modelOutput\"]\n",
+    "\n",
+    "    weaviateResponse = didItExecute(example[\"schemaPath\"], modelQuery)\n",
+    "\n",
+    "    if \"errors\" in weaviateResponse.keys():\n",
+    "        failed = True\n",
+    "\n",
+    "    if failed:\n",
+    "        print(\"FAILED! FAILED! FAILED! \\n\")\n",
+    "        print(idx)\n",
+    "        failedQueries.append(example)\n",
+    "        # Update failed Schema tracker\n",
+    "        if example[\"schemaPath\"] in failedSchemasCount.keys():\n",
+    "            failedSchemasCount[example[\"schemaPath\"]] += 1\n",
+    "        else:\n",
+    "            failedSchemasCount[example[\"schemaPath\"]] = 1\n",
+    "        # Update API tracker\n",
+    "        if example[\"apiRefPath\"] in failedAPIsCount.keys():\n",
+    "            failedAPIsCount[example[\"apiRefPath\"]] += 1\n",
+    "        else:\n",
+    "            failedAPIsCount[example[\"apiRefPath\"]] = 1\n",
+    "    else:\n",
+    "        successfulQueries.append(example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "206410c4",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(f\"{len(successfulQueries)} Queries successfully executed!\")\n",
+    "print(f\"{len(failedQueries)} Queries failed to execute!\")\n",
+    "print(\"FAILED API Count \\n\")\n",
+    "print(failedAPIsCount)\n",
+    "print(\"FAILE SCHEMA COUNT \\n\")\n",
+    "print(failedSchemasCount)"
+   ]
+  }
+ ],
+ "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": "3.10.12"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": null,
+   "end_time": null,
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "/content/eval.ipynb",
+   "output_path": "/content/artifacts/eval.ipynb",
+   "parameters": {},
+   "start_time": "2023-10-22T00:28:08.495100",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file