03_Instruction_tuning_lab_student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rKn-Y_Pk9WjC"
   },
   "source": [
    "# Instruction-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "height": 166,
    "id": "YwB8OLqiflAl"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import jsonlines\n",
    "\n",
    "from datasets import load_dataset\n",
    "from pprint import pprint\n",
    "\n",
    "from llama import BasicModelRunner\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load instruction tuned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "height": 49
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c82afe9ba8a40eda3ef8fc5805f26db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/7.47k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "instruction_tuned_dataset = load_dataset(\"tatsu-lab/alpaca\", split=\"train\", streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "height": 98
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instruction-tuned dataset:\n",
      "{'instruction': 'Give three tips for staying healthy.', 'input': '', 'output': '1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \\n2. Exercise regularly to keep your body active and strong. \\n3. Get enough sleep and maintain a consistent sleep schedule.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nGive three tips for staying healthy.\\n\\n### Response:\\n1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \\n2. Exercise regularly to keep your body active and strong. \\n3. Get enough sleep and maintain a consistent sleep schedule.'}\n",
      "{'instruction': 'What are the three primary colors?', 'input': '', 'output': 'The three primary colors are red, blue, and yellow.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat are the three primary colors?\\n\\n### Response:\\nThe three primary colors are red, blue, and yellow.'}\n",
      "{'instruction': 'Describe the structure of an atom.', 'input': '', 'output': 'An atom is made up of a nucleus, which contains protons and neutrons, surrounded by electrons that travel in orbits around the nucleus. The protons and neutrons have a positive charge, while the electrons have a negative charge, resulting in an overall neutral atom. The number of each particle determines the atomic number and the type of atom.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nDescribe the structure of an atom.\\n\\n### Response:\\nAn atom is made up of a nucleus, which contains protons and neutrons, surrounded by electrons that travel in orbits around the nucleus. The protons and neutrons have a positive charge, while the electrons have a negative charge, resulting in an overall neutral atom. The number of each particle determines the atomic number and the type of atom.'}\n",
      "{'instruction': 'How can we reduce air pollution?', 'input': '', 'output': 'There are a number of ways to reduce air pollution, such as shifting to renewable energy sources, encouraging the use of public transportation, prohibiting the burning of fossil fuels, implementing policies to reduce emissions from industrial sources, and implementing vehicle emissions standards. Additionally, individuals can do their part to reduce air pollution by reducing car use, avoiding burning materials such as wood, and changing to energy efficient appliances.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nHow can we reduce air pollution?\\n\\n### Response:\\nThere are a number of ways to reduce air pollution, such as shifting to renewable energy sources, encouraging the use of public transportation, prohibiting the burning of fossil fuels, implementing policies to reduce emissions from industrial sources, and implementing vehicle emissions standards. Additionally, individuals can do their part to reduce air pollution by reducing car use, avoiding burning materials such as wood, and changing to energy efficient appliances.'}\n",
      "{'instruction': 'Describe a time when you had to make a difficult decision.', 'input': '', 'output': 'I had to make a difficult decision when I was working as a project manager at a construction company. I was in charge of a project that needed to be completed by a certain date in order to meet the client’s expectations. However, due to unexpected delays, we were not able to meet the deadline and so I had to make a difficult decision. I decided to extend the deadline, but I had to stretch the team’s resources even further and increase the budget. Although it was a risky decision, I ultimately decided to go ahead with it to ensure that the project was completed on time and that the client’s expectations were met. The project was eventually successfully completed and this was seen as a testament to my leadership and decision-making abilities.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nDescribe a time when you had to make a difficult decision.\\n\\n### Response:\\nI had to make a difficult decision when I was working as a project manager at a construction company. I was in charge of a project that needed to be completed by a certain date in order to meet the client’s expectations. However, due to unexpected delays, we were not able to meet the deadline and so I had to make a difficult decision. I decided to extend the deadline, but I had to stretch the team’s resources even further and increase the budget. Although it was a risky decision, I ultimately decided to go ahead with it to ensure that the project was completed on time and that the client’s expectations were met. The project was eventually successfully completed and this was seen as a testament to my leadership and decision-making abilities.'}\n"
     ]
    }
   ],
   "source": [
    "m = 5\n",
    "print(\"Instruction-tuned dataset:\")\n",
    "top_m = list(itertools.islice(instruction_tuned_dataset, m))\n",
    "for j in top_m:\n",
    "  print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two prompt templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "height": 304
   },
   "outputs": [],
   "source": [
    "prompt_template_with_input = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Input:\n",
    "{input}\n",
    "\n",
    "### Response:\"\"\"\n",
    "\n",
    "prompt_template_without_input = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Response:\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrate prompts (add data to prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "height": 185
   },
   "outputs": [],
   "source": [
    "processed_data = []\n",
    "for j in top_m:\n",
    "  if not j[\"input\"]:\n",
    "    processed_prompt = prompt_template_without_input.format(instruction=j[\"instruction\"])\n",
    "  else:\n",
    "    processed_prompt = prompt_template_with_input.format(instruction=j[\"instruction\"], input=j[\"input\"])\n",
    "\n",
    "  processed_data.append({\"input\": processed_prompt, \"output\": j[\"output\"]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input': 'Below is an instruction that describes a task. Write a response '\n",
      "          'that appropriately completes the request.\\n'\n",
      "          '\\n'\n",
      "          '### Instruction:\\n'\n",
      "          'What are the three primary colors?\\n'\n",
      "          '\\n'\n",
      "          '### Response:',\n",
      " 'output': 'The three primary colors are red, blue, and yellow.'}\n"
     ]
    }
   ],
   "source": [
    "pprint(processed_data[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data to jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "with jsonlines.open(f'alpaca_processed.jsonl', 'w') as writer:\n",
    "    writer.write_all(processed_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare non-instruction-tuned vs. instruction-tuned models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c58be53875048ecb9ec1f61521f5eea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/388 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64a2373d138a4246b5c70296772da54c",
       "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": "129a283fb14d45158f0581cf862782ec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/12.7M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d71e20d47d64fedbddba22bedf26e22",
       "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": "9ba6b2f3b17b4441af9539fab15e8a9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/52002 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['input', 'output'],\n",
      "        num_rows: 52002\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "dataset_path_hf = \"lamini/alpaca\"\n",
    "dataset_hf = load_dataset(dataset_path_hf)\n",
    "print(dataset_hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "height": 83
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAMINI CONFIGURATION\n",
      "{}\n",
      "LAMINI CONFIGURATION\n",
      "{}\n",
      "LAMINI CONFIGURATION\n",
      "{}\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"NoneType\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m non_instruct_model \u001b[38;5;241m=\u001b[39m BasicModelRunner(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmeta-llama/Llama-2-7b-hf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m non_instruct_output \u001b[38;5;241m=\u001b[39m \u001b[43mnon_instruct_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTell me how to train my dog to sit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot instruction-tuned output (Llama 2 Base):\u001b[39m\u001b[38;5;124m\"\u001b[39m, non_instruct_output)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/runners/base_runner.py:28\u001b[0m, in \u001b[0;36mBaseRunner.__call__\u001b[0;34m(self, prompt, system_prompt, output_type, max_tokens)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     23\u001b[0m     prompt: Union[\u001b[38;5;28mstr\u001b[39m, List[\u001b[38;5;28mstr\u001b[39m]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     26\u001b[0m     max_tokens: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     27\u001b[0m ):\n\u001b[0;32m---> 28\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem_prompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/runners/base_runner.py:39\u001b[0m, in \u001b[0;36mBaseRunner.call\u001b[0;34m(self, prompt, system_prompt, output_type, max_tokens)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall\u001b[39m(\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     32\u001b[0m     prompt: Union[\u001b[38;5;28mstr\u001b[39m, List[\u001b[38;5;28mstr\u001b[39m]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     35\u001b[0m     max_tokens: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     36\u001b[0m ):\n\u001b[1;32m     37\u001b[0m     input_objects \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_final_prompts(prompt, system_prompt)\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlamini_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     40\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     41\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     43\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/lamini.py:46\u001b[0m, in \u001b[0;36mLamini.generate\u001b[0;34m(self, prompt, model_name, output_type, max_tokens, stop_tokens)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     33\u001b[0m     prompt: Union[\u001b[38;5;28mstr\u001b[39m, List[\u001b[38;5;28mstr\u001b[39m]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     37\u001b[0m     stop_tokens: Optional[List[\u001b[38;5;28mstr\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     38\u001b[0m ):\n\u001b[1;32m     39\u001b[0m     req_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_llm_req_map(\n\u001b[1;32m     40\u001b[0m         prompt\u001b[38;5;241m=\u001b[39mprompt,\n\u001b[1;32m     41\u001b[0m         model_name\u001b[38;5;241m=\u001b[39mmodel_name \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_name,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     44\u001b[0m         stop_tokens\u001b[38;5;241m=\u001b[39mstop_tokens,\n\u001b[1;32m     45\u001b[0m     )\n\u001b[0;32m---> 46\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(prompt, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     48\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/inference_queue.py:41\u001b[0m, in \u001b[0;36mInferenceQueue.submit\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Wait for all the results to come back\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[0;32m---> 41\u001b[0m     \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# Combine the results and return them\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcombine_results(results)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/concurrent/futures/_base.py:439\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m    438\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m    443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/concurrent/futures/_base.py:391\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m    390\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 391\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    392\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    393\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m    394\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/inference_queue.py:103\u001b[0m, in \u001b[0;36mprocess_batch\u001b[0;34m(key, api_prefix, batch)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess_batch\u001b[39m(key, api_prefix, batch):\n\u001b[1;32m    102\u001b[0m     url \u001b[38;5;241m=\u001b[39m api_prefix \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompletions\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 103\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mmake_web_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpost\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/rest_requests.py:16\u001b[0m, in \u001b[0;36mmake_web_request\u001b[0;34m(key, url, http_method, json)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_web_request\u001b[39m(key, url, http_method, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     14\u001b[0m     headers \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     15\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m---> 16\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAuthorization\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBearer \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m,\n\u001b[1;32m     17\u001b[0m     }\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m http_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     19\u001b[0m         resp \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mpost(url\u001b[38;5;241m=\u001b[39murl, headers\u001b[38;5;241m=\u001b[39mheaders, json\u001b[38;5;241m=\u001b[39mjson)\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"NoneType\") to str"
     ]
    }
   ],
   "source": [
    "non_instruct_model = BasicModelRunner(\"meta-llama/Llama-2-7b-hf\")\n",
    "non_instruct_output = non_instruct_model(\"Tell me how to train my dog to sit\")\n",
    "print(\"Not instruction-tuned output (Llama 2 Base):\", non_instruct_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAMINI CONFIGURATION\n",
      "{}\n",
      "LAMINI CONFIGURATION\n",
      "{}\n",
      "LAMINI CONFIGURATION\n",
      "{}\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"NoneType\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m instruct_model \u001b[38;5;241m=\u001b[39m BasicModelRunner(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmeta-llama/Llama-2-7b-chat-hf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m instruct_output \u001b[38;5;241m=\u001b[39m \u001b[43minstruct_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTell me how to train my dog to sit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInstruction-tuned output (Llama 2): \u001b[39m\u001b[38;5;124m\"\u001b[39m, instruct_output)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/runners/base_runner.py:28\u001b[0m, in \u001b[0;36mBaseRunner.__call__\u001b[0;34m(self, prompt, system_prompt, output_type, max_tokens)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     23\u001b[0m     prompt: Union[\u001b[38;5;28mstr\u001b[39m, List[\u001b[38;5;28mstr\u001b[39m]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     26\u001b[0m     max_tokens: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     27\u001b[0m ):\n\u001b[0;32m---> 28\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem_prompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/runners/base_runner.py:39\u001b[0m, in \u001b[0;36mBaseRunner.call\u001b[0;34m(self, prompt, system_prompt, output_type, max_tokens)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall\u001b[39m(\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     32\u001b[0m     prompt: Union[\u001b[38;5;28mstr\u001b[39m, List[\u001b[38;5;28mstr\u001b[39m]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     35\u001b[0m     max_tokens: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     36\u001b[0m ):\n\u001b[1;32m     37\u001b[0m     input_objects \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_final_prompts(prompt, system_prompt)\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlamini_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     40\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     41\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     43\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/lamini.py:46\u001b[0m, in \u001b[0;36mLamini.generate\u001b[0;34m(self, prompt, model_name, output_type, max_tokens, stop_tokens)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     33\u001b[0m     prompt: Union[\u001b[38;5;28mstr\u001b[39m, List[\u001b[38;5;28mstr\u001b[39m]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     37\u001b[0m     stop_tokens: Optional[List[\u001b[38;5;28mstr\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     38\u001b[0m ):\n\u001b[1;32m     39\u001b[0m     req_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_llm_req_map(\n\u001b[1;32m     40\u001b[0m         prompt\u001b[38;5;241m=\u001b[39mprompt,\n\u001b[1;32m     41\u001b[0m         model_name\u001b[38;5;241m=\u001b[39mmodel_name \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_name,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     44\u001b[0m         stop_tokens\u001b[38;5;241m=\u001b[39mstop_tokens,\n\u001b[1;32m     45\u001b[0m     )\n\u001b[0;32m---> 46\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(prompt, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     48\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/inference_queue.py:41\u001b[0m, in \u001b[0;36mInferenceQueue.submit\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Wait for all the results to come back\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[0;32m---> 41\u001b[0m     \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# Combine the results and return them\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcombine_results(results)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/concurrent/futures/_base.py:446\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    444\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m    445\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 446\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    447\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    448\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/concurrent/futures/_base.py:391\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m    390\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 391\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    392\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    393\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m    394\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/inference_queue.py:103\u001b[0m, in \u001b[0;36mprocess_batch\u001b[0;34m(key, api_prefix, batch)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess_batch\u001b[39m(key, api_prefix, batch):\n\u001b[1;32m    102\u001b[0m     url \u001b[38;5;241m=\u001b[39m api_prefix \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompletions\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 103\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mmake_web_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpost\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/lamini/api/rest_requests.py:16\u001b[0m, in \u001b[0;36mmake_web_request\u001b[0;34m(key, url, http_method, json)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_web_request\u001b[39m(key, url, http_method, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     14\u001b[0m     headers \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     15\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m---> 16\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAuthorization\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBearer \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m,\n\u001b[1;32m     17\u001b[0m     }\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m http_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     19\u001b[0m         resp \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mpost(url\u001b[38;5;241m=\u001b[39murl, headers\u001b[38;5;241m=\u001b[39mheaders, json\u001b[38;5;241m=\u001b[39mjson)\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"NoneType\") to str"
     ]
    }
   ],
   "source": [
    "instruct_model = BasicModelRunner(\"meta-llama/Llama-2-7b-chat-hf\")\n",
    "instruct_output = instruct_model(\"Tell me how to train my dog to sit\")\n",
    "print(\"Instruction-tuned output (Llama 2): \", instruct_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: This section of the notebook has been updated.\n",
    "\n",
    "Instruction-tuned output (ChatGPT) responds with:\n",
    "\n",
    "> Training your dog to sit is a basic and essential command that can be taught using positive reinforcement. Here's a simple step-by-step guide:\n",
    "> 1. **Prepare Treats:**\n",
    "   Gather small, soft treats that your dog enjoys. Make sure they are easy to chew and won't take too long to eat.\n",
    "> 2. **Find a Quiet Space:**\n",
    "   Choose a quiet area with minimal distractions for the training session. This will help your dog focus better.\n",
    "> 3. **Get Your Dog's Attention:**\n",
    "   Call your dog's name to get their attention. Make sure they are looking at you.\n",
    "> 4. **Use a Treat to Lure:**\n",
    "   Hold a treat close to your dog's nose, and slowly move your hand upward and slightly backward over their head. As you do this, your dog's natural response will be to follow the treat with their nose, causing them to sit.\n",
    "> 5. **Say the Command:**\n",
    "   As your dog starts to sit, say the command \"Sit\" in a clear and firm voice. Use the word consistently every time you want your dog to sit.\n",
    "> 6. **Reward and Praise:**\n",
    "   As soon as your dog sits, immediately reward them with the treat and offer verbal praise. This positive reinforcement will help them associate sitting with positive outcomes.\n",
    "> 7. **Repeat and Practice:**\n",
    "   Repeat the process several times in a row during each training session. Keep the sessions short (around 5-10 minutes) to prevent your dog from losing interest.\n",
    "> 8. **Add Duration:**\n",
    "   Once your dog consistently sits on command, you can gradually increase the duration by waiting a couple of seconds before giving the treat. This helps reinforce the sit command.\n",
    "> 9. **Generalize the Command:**\n",
    "   Practice the \"sit\" command in different locations and with various distractions to help your dog generalize the behavior.\n",
    "> 10. **Be Patient and Consistent:**\n",
    "    Patience and consistency are key in dog training. Always use positive reinforcement, and avoid punishment. If your dog doesn't succeed initially, go back a step and try again.\n",
    "> \n",
    "> Remember that each dog is unique, and some may learn more quickly than others. Adjust your training approach based on your dog's individual needs and progress."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try smaller models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "model_id": "30a5c45c7b9244d0a650436a069607a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/396 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a576d93bfb0044ea891367b620cf8bb2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d597e805715d46c7ae5acdc4270f76f8",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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    {
     "name": "stderr",
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     "text": [
      "/usr/local/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    },
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   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/pythia-70m\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/pythia-70m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "source": [
    "def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=100):\n",
    "  # Tokenize\n",
    "  input_ids = tokenizer.encode(\n",
    "          text,\n",
    "          return_tensors=\"pt\",\n",
    "          truncation=True,\n",
    "          max_length=max_input_tokens\n",
    "  )\n",
    "\n",
    "  # Generate\n",
    "  device = model.device\n",
    "  generated_tokens_with_prompt = model.generate(\n",
    "    input_ids=input_ids.to(device),\n",
    "    max_length=max_output_tokens\n",
    "  )\n",
    "\n",
    "  # Decode\n",
    "  generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)\n",
    "\n",
    "  # Strip the prompt\n",
    "  generated_text_answer = generated_text_with_prompt[0][len(text):]\n",
    "\n",
    "  return generated_text_answer"
   ]
  },
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      ]
     },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['question', 'answer', 'input_ids', 'attention_mask', 'labels'],\n",
      "        num_rows: 1260\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['question', 'answer', 'input_ids', 'attention_mask', 'labels'],\n",
      "        num_rows: 140\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "finetuning_dataset_path = \"lamini/lamini_docs\"\n",
    "finetuning_dataset = load_dataset(finetuning_dataset_path)\n",
    "print(finetuning_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "height": 81
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'question': 'Can Lamini generate technical documentation or user manuals for software projects?', 'answer': 'Yes, Lamini can generate technical documentation and user manuals for software projects. It uses natural language generation techniques to create clear and concise documentation that is easy to understand for both technical and non-technical users. This can save developers a significant amount of time and effort in creating documentation, allowing them to focus on other aspects of their projects.', 'input_ids': [5804, 418, 4988, 74, 6635, 7681, 10097, 390, 2608, 11595, 84, 323, 3694, 6493, 32, 4374, 13, 418, 4988, 74, 476, 6635, 7681, 10097, 285, 2608, 11595, 84, 323, 3694, 6493, 15, 733, 4648, 3626, 3448, 5978, 5609, 281, 2794, 2590, 285, 44003, 10097, 326, 310, 3477, 281, 2096, 323, 1097, 7681, 285, 1327, 14, 48746, 4212, 15, 831, 476, 5321, 12259, 247, 1534, 2408, 273, 673, 285, 3434, 275, 6153, 10097, 13, 6941, 731, 281, 2770, 327, 643, 7794, 273, 616, 6493, 15], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [5804, 418, 4988, 74, 6635, 7681, 10097, 390, 2608, 11595, 84, 323, 3694, 6493, 32, 4374, 13, 418, 4988, 74, 476, 6635, 7681, 10097, 285, 2608, 11595, 84, 323, 3694, 6493, 15, 733, 4648, 3626, 3448, 5978, 5609, 281, 2794, 2590, 285, 44003, 10097, 326, 310, 3477, 281, 2096, 323, 1097, 7681, 285, 1327, 14, 48746, 4212, 15, 831, 476, 5321, 12259, 247, 1534, 2408, 273, 673, 285, 3434, 275, 6153, 10097, 13, 6941, 731, 281, 2770, 327, 643, 7794, 273, 616, 6493, 15]}\n",
      "\n",
      "\n",
      "I have a question about the following:\n",
      "\n",
      "How do I get the correct documentation to work?\n",
      "\n",
      "A:\n",
      "\n",
      "I think you need to use the following code:\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following code to get the correct documentation.\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following code to get the correct documentation.\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following\n"
     ]
    }
   ],
   "source": [
    "test_sample = finetuning_dataset[\"test\"][0]\n",
    "print(test_sample)\n",
    "\n",
    "print(inference(test_sample[\"question\"], model, tokenizer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare to finetuned small model"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "instruction_model = AutoModelForCausalLM.from_pretrained(\"lamini/lamini_docs_finetuned\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    }
   ],
   "source": [
    "print(inference(test_sample[\"question\"], instruction_model, tokenizer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Pssst! If you were curious how to upload your own dataset to Huggingface\n",
    "# Here is how we did it\n",
    "\n",
    "# !pip install huggingface_hub\n",
    "# !huggingface-cli login\n",
    "\n",
    "# import pandas as pd\n",
    "# import datasets\n",
    "# from datasets import Dataset\n",
    "\n",
    "# finetuning_dataset = Dataset.from_pandas(pd.DataFrame(data=finetuning_dataset))\n",
    "# finetuning_dataset.push_to_hub(dataset_path_hf)"
   ]
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