diff --git "a/eval.ipynb" "b/eval.ipynb"
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+++ "b/eval.ipynb"
@@ -0,0 +1,6963 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7677cd17",
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:21:53.688714Z",
+     "iopub.status.busy": "2023-10-24T03:21:53.688271Z",
+     "iopub.status.idle": "2023-10-24T03:21:53.705531Z",
+     "shell.execute_reply": "2023-10-24T03:21:53.704511Z"
+    },
+    "papermill": {
+     "duration": 1.156681,
+     "end_time": "2023-10-24T03:21:53.708564",
+     "exception": false,
+     "start_time": "2023-10-24T03:21:52.551883",
+     "status": "completed"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'hf_dataset': 'weaviate/WithoutRetrieval-SchemaSplit-Test-80',\n",
+       " 'prompt_template': '## Instruction\\nYour task is to write GraphQL for the Natural Language Query provided. Use the provided Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\\n\\n## Natural Language Query\\n{nlcommand}\\n\\n## Schema\\n{schema}\\n\\n## Answer\\n```graphql\\n',\n",
+       " 'push_to_hub': 'substratusai/wgql-WithoutRetrieval-SchemaSplit-Train-80'}"
+      ]
+     },
+     "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": "bda9f3a9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:21:56.032231Z",
+     "iopub.status.busy": "2023-10-24T03:21:56.031912Z",
+     "iopub.status.idle": "2023-10-24T03:22:01.677633Z",
+     "shell.execute_reply": "2023-10-24T03:22:01.676871Z"
+    },
+    "papermill": {
+     "duration": 7.410205,
+     "end_time": "2023-10-24T03:22:02.289735",
+     "exception": false,
+     "start_time": "2023-10-24T03:21:54.879530",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c933c694db0f49fd8565c66bd94ef85d",
+       "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": "33c8774129fa404eb91ba0deaa112e41",
+       "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": "00028559fb3a4c74865d1667802ab9c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/3.90M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9d06eb8b5d8487aa2b0c758d964db8a",
+       "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": "f868d4dcd1f7461498c5afd00aeebfe4",
+       "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: 825\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": "36114732",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:22:04.622336Z",
+     "iopub.status.busy": "2023-10-24T03:22:04.621445Z",
+     "iopub.status.idle": "2023-10-24T03:22:04.628563Z",
+     "shell.execute_reply": "2023-10-24T03:22:04.627696Z"
+    },
+    "papermill": {
+     "duration": 1.073129,
+     "end_time": "2023-10-24T03:22:04.631266",
+     "exception": false,
+     "start_time": "2023-10-24T03:22:03.558137",
+     "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 Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\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",
+      "## 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": "844be37e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:22:07.006036Z",
+     "iopub.status.busy": "2023-10-24T03:22:07.005008Z",
+     "iopub.status.idle": "2023-10-24T03:29:53.671959Z",
+     "shell.execute_reply": "2023-10-24T03:29:53.671080Z"
+    },
+    "papermill": {
+     "duration": 467.932535,
+     "end_time": "2023-10-24T03:29:53.673678",
+     "exception": false,
+     "start_time": "2023-10-24T03:22:05.741143",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e00437a9af0448cd9c474667f4f79002",
+       "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": "aeacb280ff4a4b06b4504326fcdcf2bf",
+       "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": "04e9c0a6e9054888823eb737a577cb25",
+       "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": "22f79914ae334bacaf55175f9b852f02",
+       "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": "ae9e2f1ca1cd4525b9e9a52f1fcaa609",
+       "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": "e44591aa352440d0ac8ceace984fc39f",
+       "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": "71121f34291146d0a5c31260bc8acde1",
+       "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": "b82313af56a24c2c8de8bdd042a26bfc",
+       "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": "0a4b5769e3e54271a7fa3fe0187b5da4",
+       "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": "1e88ed54f3ff49dbb845f1522a56303b",
+       "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": "f1228ebf53d444208308916094b11bfd",
+       "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": "f4daa2e6354748958506738df50dc784",
+       "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": "af483225",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:29:55.723570Z",
+     "iopub.status.busy": "2023-10-24T03:29:55.722545Z",
+     "iopub.status.idle": "2023-10-24T03:29:56.060068Z",
+     "shell.execute_reply": "2023-10-24T03:29:56.059169Z"
+    },
+    "papermill": {
+     "duration": 1.440946,
+     "end_time": "2023-10-24T03:29:56.062160",
+     "exception": false,
+     "start_time": "2023-10-24T03:29:54.621214",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tue Oct 24 03:29:55 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   44C    P0    27W /  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": "5c098929",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:29:57.985321Z",
+     "iopub.status.busy": "2023-10-24T03:29:57.984960Z",
+     "iopub.status.idle": "2023-10-24T03:29:57.991315Z",
+     "shell.execute_reply": "2023-10-24T03:29:57.990495Z"
+    },
+    "papermill": {
+     "duration": 1.022636,
+     "end_time": "2023-10-24T03:29:57.993277",
+     "exception": false,
+     "start_time": "2023-10-24T03:29:56.970641",
+     "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": "e0308186",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:30:00.515335Z",
+     "iopub.status.busy": "2023-10-24T03:30:00.514983Z",
+     "iopub.status.idle": "2023-10-24T03:30:00.522427Z",
+     "shell.execute_reply": "2023-10-24T03:30:00.521615Z"
+    },
+    "papermill": {
+     "duration": 1.569636,
+     "end_time": "2023-10-24T03:30:00.524200",
+     "exception": false,
+     "start_time": "2023-10-24T03:29:58.954564",
+     "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": "6d55baa3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:30:02.403027Z",
+     "iopub.status.busy": "2023-10-24T03:30:02.402236Z",
+     "iopub.status.idle": "2023-10-24T03:30:02.408178Z",
+     "shell.execute_reply": "2023-10-24T03:30:02.407506Z"
+    },
+    "papermill": {
+     "duration": 0.97198,
+     "end_time": "2023-10-24T03:30:02.409733",
+     "exception": false,
+     "start_time": "2023-10-24T03:30:01.437753",
+     "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": "598462d6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:30:04.279027Z",
+     "iopub.status.busy": "2023-10-24T03:30:04.278146Z",
+     "iopub.status.idle": "2023-10-24T03:30:04.282776Z",
+     "shell.execute_reply": "2023-10-24T03:30:04.282110Z"
+    },
+    "papermill": {
+     "duration": 0.918582,
+     "end_time": "2023-10-24T03:30:04.284246",
+     "exception": false,
+     "start_time": "2023-10-24T03:30:03.365664",
+     "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": "b8cce97b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:30:10.372209Z",
+     "iopub.status.busy": "2023-10-24T03:30:10.371342Z",
+     "iopub.status.idle": "2023-10-24T03:30:18.000128Z",
+     "shell.execute_reply": "2023-10-24T03:30:17.999419Z"
+    },
+    "papermill": {
+     "duration": 9.05242,
+     "end_time": "2023-10-24T03:30:18.002359",
+     "exception": false,
+     "start_time": "2023-10-24T03:30:08.949939",
+     "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 Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\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",
+      "## 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 6.46 s, sys: 744 ms, total: 7.21 s\n",
+      "Wall time: 7.62 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": "4d586596",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:30:19.975723Z",
+     "iopub.status.busy": "2023-10-24T03:30:19.975061Z",
+     "iopub.status.idle": "2023-10-24T03:30:19.980289Z",
+     "shell.execute_reply": "2023-10-24T03:30:19.979604Z"
+    },
+    "papermill": {
+     "duration": 1.01748,
+     "end_time": "2023-10-24T03:30:19.982265",
+     "exception": false,
+     "start_time": "2023-10-24T03:30:18.964785",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 424])\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": "f48a2dd8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-24T03:30:21.873420Z",
+     "iopub.status.busy": "2023-10-24T03:30:21.872686Z",
+     "iopub.status.idle": "2023-10-24T04:45:40.261618Z",
+     "shell.execute_reply": "2023-10-24T04:45:40.260727Z"
+    },
+    "papermill": {
+     "duration": 4519.365995,
+     "end_time": "2023-10-24T04:45:40.263747",
+     "exception": false,
+     "start_time": "2023-10-24T03:30:20.897752",
+     "status": "completed"
+    },
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running inference for 825 entries in dataset\n",
+      "entry 1 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 2 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 3 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 4 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 5 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 6 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 7 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 8 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 9 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 10 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 11 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 12 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 13 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 14 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 15 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 16 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 17 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 18 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 19 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 20 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 21 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 22 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 23 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 24 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 25 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 26 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 27 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 28 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 29 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 30 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 31 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 32 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 33 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 34 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 35 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 36 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 37 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 38 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 39 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 40 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 41 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 42 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 43 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 44 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 45 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 46 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 47 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 48 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 49 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 50 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 51 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 52 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 53 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 54 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 55 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 56 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 57 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 58 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 59 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 60 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 61 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 62 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 63 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 64 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 65 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 66 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 67 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 75 of 825\n"
+     ]
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+     "output_type": "stream",
+     "text": [
+      "entry 778 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 779 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 780 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 781 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 782 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 783 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 784 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 785 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 786 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 787 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 788 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 789 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 790 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 791 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 792 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 793 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 794 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 795 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 796 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 797 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 798 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 799 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 800 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 801 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 802 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 803 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 804 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 805 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 806 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 807 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 808 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 809 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 810 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 811 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 812 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 813 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 814 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 815 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 816 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 817 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 818 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 819 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 820 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 821 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 822 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 823 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 824 of 825\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry 825 of 825\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": "1aa86fcc",
+   "metadata": {
+    "papermill": {
+     "duration": 1.059079,
+     "end_time": "2023-10-24T04:45:42.478248",
+     "exception": false,
+     "start_time": "2023-10-24T04:45:41.419169",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Store the test dataset with model output in the original HuggingFace Model repo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e7715a2",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": false,
+     "start_time": "2023-10-24T04:45:43.514575",
+     "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": "931e3b5a",
+   "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": "1809d7c2",
+   "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": "9de4c1fd",
+   "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": "5124f713",
+   "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": "a7f8a824",
+   "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": "94e02a6b",
+   "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": "2023-10-24T03:21:48.360391",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file