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				+{ 
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				+ "cells": [ 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 3, 
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				+   "id": "0fccdeda-60db-4ac0-bbb0-98d4d5577a40", 
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				+   "metadata": {}, 
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				+   "outputs": [], 
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				+   "source": [ 
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				+    "#!pip install replicate" 
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				+   ] 
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				+  }, 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 18, 
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				+   "id": "69395317-ad78-47b6-a533-2e8a01313e82", 
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				+   "metadata": {}, 
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				+   "outputs": [], 
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				+   "source": [ 
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				+    "SYSTEMP_PROMPT = \"\"\"\n", 
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				+    "You are the most skilled podcast writer, you have won multiple podcast awards for your writing.\n", 
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				+    " \n", 
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				+    "Your job is to write word by word, even \"umm, hmmm, right\" interruptions by the second speaker based on the PDF upload. Keep it extremely engaging, the speakers can get derailed now and then but should discuss the topic. \n", 
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				+    "\n", 
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				+    "Remember Speaker 2 is new to the topic and the conversation should always have realistic anecdotes and analogies sprinkled throughout. The questions should have real world example follow ups etc\n", 
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				+    "\n", 
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				+    "Speaker 1: Leads the conversation and teaches the speaker 2, gives incredible anecdotes and analogies when explaining. Is a captivating teacher that gives great anecdotes\n", 
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				+    "\n", 
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				+    "Speaker 2: Keeps the conversation on track by asking follow up questions. Gets super excited or confused when asking questions. Is a curious mindset that asks very interesting confirmation questions\n", 
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				+    "\n", 
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				+    "Make sure the tangents speaker 2 provides are quite wild or interesting. \n", 
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				+    "\n", 
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				+    "Ensure there are interruptions during explanations or there are \"hmm\" and \"umm\" injected throughout from the second speaker. \n", 
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				+    "\n", 
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				+    "It should be a real podcast with every fine nuance documented in as much detail as possible. Welcome the listeners with a super fun overview and keep it really catchy and almost borderline click bait\n", 
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				+    "\"\"\"" 
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				+   ] 
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				+  }, 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 19, 
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				+   "id": "08c30139-ff2f-4203-8194-d1b5c50acac5", 
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				+   "metadata": {}, 
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				+   "outputs": [], 
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				+   "source": [ 
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				+    "DEFAULT_MODEL = \"meta-llama/Llama-3.1-70B-Instruct\"" 
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				+   ] 
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				+  }, 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 20, 
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				+   "id": "1641060a-d86d-4137-bbbc-ab05cbb1a888", 
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				+   "metadata": {}, 
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				+   "outputs": [], 
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				+   "source": [ 
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				+    "# Import necessary libraries\n", 
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				+    "import torch\n", 
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				+    "from accelerate import Accelerator\n", 
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				+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n", 
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				+    "\n", 
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				+    "from tqdm.notebook import tqdm\n", 
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				+    "import warnings\n", 
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				+    "\n", 
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				+    "warnings.filterwarnings('ignore')" 
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				+   ] 
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				+  }, 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 21, 
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				+   "id": "522fbf7f-8c00-412c-90c7-5cfe2fc94e4c", 
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				+   "metadata": {}, 
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				+   "outputs": [], 
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				+   "source": [ 
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				+    "def read_file_to_string(filename):\n", 
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				+    "    try:\n", 
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				+    "        with open(filename, 'r') as file:\n", 
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				+    "            content = file.read()\n", 
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				+    "        return content\n", 
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				+    "    except FileNotFoundError:\n", 
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				+    "        print(f\"Error: File '{filename}' not found.\")\n", 
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				+    "        return None\n", 
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				+    "    except IOError:\n", 
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				+    "        print(f\"Error: Could not read file '{filename}'.\")\n", 
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				+    "        return None" 
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				+   ] 
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				+  }, 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 26, 
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				+   "id": "8119803c-18f9-47cb-b719-2b34ccc5cc41", 
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				+   "metadata": {}, 
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				+   "outputs": [], 
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				+   "source": [ 
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				+    "INPUT_PROMPT = read_file_to_string('./clean_extracted_text.txt')" 
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				+   ] 
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				+  }, 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 27, 
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				+   "id": "d895ed4f-1f3e-48b4-b7e2-b51d214fd6fb", 
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				+   "metadata": {}, 
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				+   "outputs": [], 
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				+   "source": [ 
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				+    "conversation = [\n", 
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				+    "        {\"role\": \"system\", \"content\": SYSTEMP_PROMPT},\n", 
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				+    "        {\"role\": \"user\", \"content\": INPUT_PROMPT},\n", 
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				+    "    ]" 
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				+   ] 
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				+  }, 
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				+  { 
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				+   "cell_type": "code", 
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				+   "execution_count": 25, 
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				+   "id": "e9753245-dfd8-4eb4-b1f4-219723884d9f", 
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				+   "metadata": {}, 
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				+   "outputs": [ 
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				+    { 
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				+     "data": { 
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				+      "application/vnd.jupyter.widget-view+json": { 
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				+       "model_id": "3ee94e15d1a04e88a6f5ebff149e2e98", 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "config.json:   0%|          | 0.00/855 [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+     "metadata": {}, 
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				+     "output_type": "display_data" 
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				+    }, 
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				+    { 
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				+       "model_id": "8522222de2eb4877a6a2087cc05ad130", 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "model.safetensors.index.json:   0%|          | 0.00/59.6k [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+     "metadata": {}, 
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				+     "output_type": "display_data" 
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				+    }, 
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				+    { 
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				+      "application/vnd.jupyter.widget-view+json": { 
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				+       "model_id": "0852ae52bfef44c1bc487e7f0951826f", 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "Downloading shards:   0%|          | 0/30 [00:00<?, ?it/s]" 
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				+      ] 
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				+     }, 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "model-00001-of-00030.safetensors:   0%|          | 0.00/4.58G [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+     "metadata": {}, 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "model-00002-of-00030.safetensors:   0%|          | 0.00/4.66G [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "model-00003-of-00030.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "model-00004-of-00030.safetensors:   0%|          | 0.00/4.97G [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+     "metadata": {}, 
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				+     "output_type": "display_data" 
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				+    }, 
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				+    { 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "model-00005-of-00030.safetensors:   0%|          | 0.00/4.66G [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+     "metadata": {}, 
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				+     "output_type": "display_data" 
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				+    }, 
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				+    { 
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				+     "data": { 
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				+      "application/vnd.jupyter.widget-view+json": { 
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				+       "model_id": "42f21ca77fe340228f5e58ca0a479750", 
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				+       "version_major": 2, 
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				+       "version_minor": 0 
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				+      }, 
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				+      "text/plain": [ 
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				+       "model-00006-of-00030.safetensors:   0%|          | 0.00/4.66G [00:00<?, ?B/s]" 
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				+      ] 
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				+     }, 
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				+     "metadata": {}, 
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				+     "output_type": "display_data" 
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				+   ], 
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				+   "source": [ 
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				 | 
				 | 
			
			
				+    "accelerator = Accelerator()\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "model = AutoModelForCausalLM.from_pretrained(\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "    DEFAULT_MODEL,\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "    torch_dtype=torch.bfloat16,\n", 
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				 | 
				 | 
			
			
				+    "    use_safetensors=True,\n", 
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				+    "    device_map=\"auto\",\n", 
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				 | 
				 | 
			
			
				+    ")\n", 
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				 | 
				 | 
			
			
				+    "tokenizer = AutoTokenizer.from_pretrained(DEFAULT_MODEL, use_safetensors=True)\n", 
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				 | 
			
			
				+    "model, tokenizer = accelerator.prepare(model, tokenizer)" 
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				+   ] 
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				+  }, 
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				+   "cell_type": "code", 
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				+   "execution_count": 29, 
			 | 
		
	
		
			
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				 | 
			
			
				+   "id": "662b3567-1fe4-4744-a673-e0f871f4fe9a", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "metadata": {}, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "outputs": [], 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "source": [ 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "prompt = tokenizer.apply_chat_template(conversation, tokenize=False)\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "inputs = tokenizer(prompt, return_tensors=\"pt\")" 
			 | 
		
	
		
			
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				 | 
			
			
				+   ] 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+  }, 
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				+  { 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "cell_type": "code", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "execution_count": null, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "id": "13c51b1c-af72-4a30-99e2-e559b052aaeb", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "metadata": {}, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "outputs": [ 
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				 | 
				 | 
			
			
				+    { 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+     "name": "stderr", 
			 | 
		
	
		
			
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				+     "output_type": "stream", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+     "text": [ 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+     ] 
			 | 
		
	
		
			
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				 | 
			
			
				+    } 
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				+   ], 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "source": [ 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "with torch.no_grad():\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "    output = model.generate(\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "        **inputs,\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "        temperature=0.7,\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "        top_p=0.9,\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "        max_new_tokens=8126\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "    )\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "\n", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "output = tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt):].strip()" 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   ] 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+  }, 
			 | 
		
	
		
			
				 | 
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				+  { 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "cell_type": "code", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "execution_count": null, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "id": "41c83f2a-d0dc-4962-8fe7-cd187a8cb006", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "metadata": {}, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "outputs": [], 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "source": [] 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+  } 
			 | 
		
	
		
			
				 | 
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				+ ], 
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				 | 
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				+ "metadata": { 
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				 | 
				 | 
			
			
				+  "kernelspec": { 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "display_name": "Python 3 (ipykernel)", 
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				 | 
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				+   "language": "python", 
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				 | 
				 | 
			
			
				+   "name": "python3" 
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				+  }, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+  "language_info": { 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+   "codemirror_mode": { 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "name": "ipython", 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+    "version": 3 
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				+   }, 
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				+   "file_extension": ".py", 
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				 | 
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				+   "mimetype": "text/x-python", 
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				 | 
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				+   "name": "python", 
			 | 
		
	
		
			
				 | 
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				+   "nbconvert_exporter": "python", 
			 | 
		
	
		
			
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				+   "pygments_lexer": "ipython3", 
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				+   "version": "3.11.10" 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+  } 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+ }, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+ "nbformat": 4, 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+ "nbformat_minor": 5 
			 | 
		
	
		
			
				 | 
				 | 
			
			
				+} 
			 |