From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
4208 lines
514 KiB
4208 lines
514 KiB
{ |
|
"cells": [ |
|
{ |
|
"cell_type": "markdown", |
|
"id": "dfe37963-1af6-44fc-a841-8e462443f5e6", |
|
"metadata": {}, |
|
"source": [ |
|
"## Udemy Video Subtitle Vectorization (Expert on LLM engineering) \n", |
|
"\n", |
|
"This project will uses subtitle files from Ed Donners excellent LLM engineering course on Udemy.\n", |
|
"\n", |
|
"These can be downloaded using the following process:\n", |
|
"- Useing an android phone, download Udemy app and open the LLM engineering course. \n", |
|
"- There is option to download the videos as single files or section wise. \n", |
|
"- Download them and along with those videos subs or cc are also downloaded as .srt’s.\n", |
|
"- Plug in your laptop to the android phone using USB and select file transfer in the notification.\n", |
|
"- Open a file explorer and copy the subtitle files (srt format)\n", |
|
"- Here’s the location of subs in android \"internal storage/android/data/com.udemy.android/files/udemy-subtitle-downloads\"\n", |
|
"\n", |
|
"the raw srt files are stored in the folder \"subtitles/srts\". The code below will use the langchain textloader but will preprocess the srt files to remove the timestamps.\n", |
|
"\n", |
|
"### Note: this is only for educational and testing purposes and you should contact Ed Donnner to seek his permission if you want to use the subtitles." |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 63, |
|
"id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# imports\n", |
|
"\n", |
|
"import os\n", |
|
"import glob\n", |
|
"from dotenv import load_dotenv\n", |
|
"import gradio as gr" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 64, |
|
"id": "802137aa-8a74-45e0-a487-d1974927d7ca", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# imports for langchain and Chroma and plotly\n", |
|
"\n", |
|
"from langchain.document_loaders import DirectoryLoader, TextLoader\n", |
|
"from langchain.text_splitter import CharacterTextSplitter\n", |
|
"from langchain.schema import Document\n", |
|
"from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", |
|
"from langchain_chroma import Chroma\n", |
|
"import numpy as np\n", |
|
"from sklearn.manifold import TSNE\n", |
|
"import plotly.graph_objects as go\n", |
|
"import re" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 65, |
|
"id": "58c85082-e417-4708-9efe-81a5d55d1424", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# price is a factor for our company, so we're going to use a low cost model\n", |
|
"\n", |
|
"MODEL = \"gpt-4o-mini\"\n", |
|
"db_name = \"vector_db\"" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 66, |
|
"id": "ee78efcb-60fe-449e-a944-40bab26261af", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Load environment variables in a file called .env\n", |
|
"\n", |
|
"load_dotenv()\n", |
|
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 67, |
|
"id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Read in documents using LangChain's loaders\n", |
|
"# Take everything in all the sub-folders of our knowledgebase\n", |
|
"\n", |
|
"folders = glob.glob(\"subtitles/srts/*\")\n", |
|
"\n", |
|
"# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", |
|
"text_loader_kwargs = {'encoding': 'utf-8'}\n", |
|
"# If that doesn't work, some Windows users might need to uncomment the next line instead\n", |
|
"# text_loader_kwargs={'autodetect_encoding': True}\n", |
|
"\n", |
|
"def preprocess_srt_content(content):\n", |
|
" \"\"\"\n", |
|
" Preprocess the content of an SRT file to remove timing information and the WEBVTT header.\n", |
|
" \"\"\"\n", |
|
" # Remove the WEBVTT header\n", |
|
" content = re.sub(r'^WEBVTT\\s*', '', content, flags=re.IGNORECASE)\n", |
|
" # Remove timing lines (e.g., 00:00.680 --> 00:08.540)\n", |
|
" content = re.sub(r'\\d{2}:\\d{2}\\.\\d{3} --> \\d{2}:\\d{2}\\.\\d{3}', '', content)\n", |
|
" # Remove extra newlines and strip leading/trailing whitespace\n", |
|
" return \"\\n\".join(line.strip() for line in content.splitlines() if line.strip())\n", |
|
"\n", |
|
"documents = []\n", |
|
"for folder in folders:\n", |
|
" video_number = os.path.basename(folder)\n", |
|
" loader = DirectoryLoader(folder, glob=\"**/en_US.srt\", loader_cls=TextLoader)\n", |
|
" folder_docs = loader.load()\n", |
|
"\n", |
|
" for doc in folder_docs:\n", |
|
" # Preprocess the document content\n", |
|
" cleaned_content = preprocess_srt_content(doc.page_content)\n", |
|
" # Replace the original content with the cleaned content\n", |
|
" doc.page_content = cleaned_content\n", |
|
" # Add metadata\n", |
|
" doc.metadata[\"video_number\"] = video_number\n", |
|
" documents.append(doc)\n" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"id": "f065d4b1-80b7-4e15-abd4-60a83e752ea8", |
|
"metadata": {}, |
|
"source": [ |
|
"# Please note:\n", |
|
"\n", |
|
"In the next cell, we split the text into chunks.\n", |
|
"\n", |
|
"If you have problems, you can try to fix them by changing the chunk_size from 1,000 to 2,000 and the chunk_overlap from 200 to 400. \n", |
|
"This shouldn't be required; but if it happens to you, please make that change! \n", |
|
"(Note that LangChain may give a warning about a chunk being larger than 1,000 - this can be safely ignored)." |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 68, |
|
"id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", |
|
"chunks = text_splitter.split_documents(documents)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 69, |
|
"id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"text/plain": [ |
|
"217" |
|
] |
|
}, |
|
"execution_count": 69, |
|
"metadata": {}, |
|
"output_type": "execute_result" |
|
} |
|
], |
|
"source": [ |
|
"len(chunks)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 70, |
|
"id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stdout", |
|
"output_type": "stream", |
|
"text": [ |
|
"Video numbers found: 60616407, 59170043, 59507329, 59505329, 60614541, 59471979, 59166453, 59295587, 59295545, 59670259, 59166421, 59295493, 59166461, 59166919, 60616845, 59472873, 59668027, 59472017, 59668181, 60614589, 59473021, 59166443, 59507017, 60619721, 59170055, 59665129, 59295439, 59673721, 59472441, 59507423, 59473201, 59472011, 59671567, 60616927, 59170297, 59667365, 60620395, 59295599, 59669375, 59507435, 59297749, 59297599, 59297603, 59472491, 59297595, 60616663, 59170165, 59472383, 59506713, 59297561, 60620397, 59166951, 59472503, 59295609, 59670933, 59170291, 59295429, 59473071, 59472027, 59166949, 60616629, 60619227, 59297733, 59669211, 59473191, 59667829, 59295423, 59170037, 59170025, 59170227, 59671231, 59673449, 59503703, 59669631, 59166353, 59671441, 59673663, 59668923, 60619619, 59170255, 59508289, 59507785, 60619299, 60619501, 60616623, 59473147, 59170135, 59473089, 59295435, 59472425, 59295579, 59669389, 60617259, 59673639, 59508297, 60619247, 60619289, 59472137, 59669049, 59472693, 60620143, 59295363, 59503705, 59167009, 59508175, 59669217, 59166915, 59295441, 59508055, 59667841, 59472421, 60619123, 59297721, 59508057, 59297601, 59297735, 59670369, 59170223, 59271655, 59297773, 59170057, 59504785, 59473159, 59166281, 60617251, 59295459, 59472413, 59665127, 59295619, 59670121, 59666831, 60619447, 59670171, 60616493, 59473101, 59473019, 59666211, 59671315, 60619439, 59295451, 59297723, 59673431, 59169991, 59472333, 60619149, 59295607, 60619281, 59297575, 59472429, 60619883, 59670073, 59167007, 59671221, 59295553, 59166981, 60595637, 59170235, 59297593, 60614591, 59504887, 60616895, 59166947, 60620025, 60617163, 60622463, 59506611, 59166481, 59472505, 59295431, 59472463, 59167015, 59170233, 60395261, 59508121, 59166847, 60620169, 60616423, 59473137, 59170107, 59297743, 59506507, 59472883, 59295541, 59507489, 60619577, 59507687, 59506929, 59170093, 59166465, 59166317, 59295601, 59509185, 60619651, 59169985, 59505337, 59295527, 59667357, 59673595, 59295549, 59297693, 60620375, 59297585, 59670087, 59472067, 59295583, 60616855, 59295377, 60619275, 59504769, 59507635, 60616833, 59297609, 60619429, 59472307, 59507313, 60617255, 59472007\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"video_numbers = set(chunk.metadata['video_number'] for chunk in chunks)\n", |
|
"print(f\"Video numbers found: {', '.join(video_numbers)}\")" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013", |
|
"metadata": {}, |
|
"source": [ |
|
"## A sidenote on Embeddings, and \"Auto-Encoding LLMs\"\n", |
|
"\n", |
|
"We will be mapping each chunk of text into a Vector that represents the meaning of the text, known as an embedding.\n", |
|
"\n", |
|
"OpenAI offers a model to do this, which we will use by calling their API with some LangChain code.\n", |
|
"\n", |
|
"This model is an example of an \"Auto-Encoding LLM\" which generates an output given a complete input.\n", |
|
"It's different to all the other LLMs we've discussed today, which are known as \"Auto-Regressive LLMs\", and generate future tokens based only on past context.\n", |
|
"\n", |
|
"Another example of an Auto-Encoding LLMs is BERT from Google. In addition to embedding, Auto-encoding LLMs are often used for classification.\n", |
|
"\n", |
|
"### Sidenote\n", |
|
"\n", |
|
"In week 8 we will return to RAG and vector embeddings, and we will use an open-source vector encoder so that the data never leaves our computer - that's an important consideration when building enterprise systems and the data needs to remain internal." |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 71, |
|
"id": "78998399-ac17-4e28-b15f-0b5f51e6ee23", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", |
|
"\n", |
|
"embeddings = OpenAIEmbeddings()\n", |
|
"\n", |
|
"# If you would rather use the free Vector Embeddings from HuggingFace sentence-transformers\n", |
|
"# Then replace embeddings = OpenAIEmbeddings()\n", |
|
"# with:\n", |
|
"# from langchain.embeddings import HuggingFaceEmbeddings\n", |
|
"# embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 72, |
|
"id": "763e51ff-5787-4a56-8176-36b7c5796fe3", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Check if a Chroma Datastore already exists - if so, delete the collection to start from scratch\n", |
|
"\n", |
|
"if os.path.exists(db_name):\n", |
|
" Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 73, |
|
"id": "99fe3a37-480f-4d55-be48-120588d5846b", |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stdout", |
|
"output_type": "stream", |
|
"text": [ |
|
"Vectorstore created with 217 documents\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"# Create our Chroma vectorstore!\n", |
|
"\n", |
|
"vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", |
|
"print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 74, |
|
"id": "057868f6-51a6-4087-94d1-380145821550", |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stdout", |
|
"output_type": "stream", |
|
"text": [ |
|
"The vectors have 1,536 dimensions\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"# Get one vector and find how many dimensions it has\n", |
|
"\n", |
|
"collection = vectorstore._collection\n", |
|
"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", |
|
"dimensions = len(sample_embedding)\n", |
|
"print(f\"The vectors have {dimensions:,} dimensions\")" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "61e393a0-dd4c-419f-842f-60c1cb3b716b", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"id": "b0d45462-a818-441c-b010-b85b32bcf618", |
|
"metadata": {}, |
|
"source": [ |
|
"## Visualizing the Vector Store\n", |
|
"\n", |
|
"Let's take a minute to look at the documents and their embedding vectors to see what's going on." |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 75, |
|
"id": "cfb855dc-1610-4aaf-8e5f-68c26ce640a5", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Convert the video numbers into unique colors that we can visualize\n", |
|
"import hashlib\n", |
|
"\n", |
|
"def video_numbers_to_hex_colors(video_numbers):\n", |
|
" return [f\"#{hashlib.sha256(v.encode()).hexdigest()[:6]}\" for v in video_numbers]\n", |
|
"\n" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 76, |
|
"id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Prework\n", |
|
"\n", |
|
"result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", |
|
"vectors = np.array(result['embeddings'])\n", |
|
"documents = result['documents']\n", |
|
"video_numbers = [metadata['video_number'] for metadata in result['metadatas']]\n", |
|
"colors = video_numbers_to_hex_colors(strings)\n", |
|
"\n" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 77, |
|
"id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"application/vnd.plotly.v1+json": { |
|
"config": { |
|
"plotlyServerURL": "https://plot.ly" |
|
}, |
|
"data": [ |
|
{ |
|
"hoverinfo": "text", |
|
"marker": { |
|
"color": [ |
|
"#d01f72", |
|
"#75195e", |
|
"#3678a7", |
|
"#5b3f83", |
|
"#74a788", |
|
"#571122", |
|
"#4099c1", |
|
"#659222", |
|
"#188ca3", |
|
"#6d4052", |
|
"#35303c", |
|
"#a9e927", |
|
"#29fa15", |
|
"#71c500", |
|
"#9b9d6e", |
|
"#cf7e83", |
|
"#badd6d", |
|
"#85fa26", |
|
"#22463b", |
|
"#ce865d", |
|
"#f59c06", |
|
"#011995", |
|
"#793548", |
|
"#ad8b14", |
|
"#d937bd", |
|
"#2b9f18", |
|
"#046e5c", |
|
"#75b5e3", |
|
"#c959de", |
|
"#72e048", |
|
"#8e8cab", |
|
"#20f2c3", |
|
"#64f999", |
|
"#e69670", |
|
"#6a0fce", |
|
"#d65c3a", |
|
"#7bee34", |
|
"#4f86b8", |
|
"#b43417", |
|
"#4dfb77", |
|
"#2ae342", |
|
"#c3e1f2", |
|
"#12897b", |
|
"#2b3af3", |
|
"#7ea8e9", |
|
"#6ad041", |
|
"#0bdacc", |
|
"#99fe53", |
|
"#4aaf9f", |
|
"#d156c8", |
|
"#505bd9", |
|
"#dc152c", |
|
"#b52bf6", |
|
"#9baca0", |
|
"#a03134", |
|
"#d43c00", |
|
"#5af098", |
|
"#2c168d", |
|
"#c6016b", |
|
"#f090af", |
|
"#482281", |
|
"#39821f", |
|
"#e0a8df", |
|
"#480c89", |
|
"#08808d", |
|
"#ac5faf", |
|
"#0faf59", |
|
"#79c82a", |
|
"#e6e164", |
|
"#0d2037", |
|
"#8afd40", |
|
"#2e1afc", |
|
"#3ec815", |
|
"#fbfef2", |
|
"#a63fa4", |
|
"#b27d2e", |
|
"#ca3592", |
|
"#b9fd23", |
|
"#ac9648", |
|
"#804ce2", |
|
"#9b5e28", |
|
"#a64739", |
|
"#c457d7", |
|
"#de30e4", |
|
"#1f6ab0", |
|
"#6ff3c5", |
|
"#6df6ca", |
|
"#ed694d", |
|
"#2fef1a", |
|
"#335dcf", |
|
"#845aa9", |
|
"#574e28", |
|
"#dc95ec", |
|
"#b2140a", |
|
"#15ae86", |
|
"#70d1d9", |
|
"#6f745a", |
|
"#b3dba5", |
|
"#108c41", |
|
"#268bba", |
|
"#913568", |
|
"#1a6fdf", |
|
"#422abb", |
|
"#cb725f", |
|
"#fe62a5", |
|
"#dfc6c7", |
|
"#b25d7b", |
|
"#bd53b1", |
|
"#796278", |
|
"#048452", |
|
"#c6eff5", |
|
"#d24e5d", |
|
"#fe8e92", |
|
"#22398f", |
|
"#3e5237", |
|
"#8069bc", |
|
"#7740be", |
|
"#cc8ec0", |
|
"#b280bb", |
|
"#91f4db", |
|
"#ac55ba", |
|
"#c97596", |
|
"#116019", |
|
"#43c2e8", |
|
"#2a2d25", |
|
"#fc2b74", |
|
"#ae7afe", |
|
"#92b4fa", |
|
"#dd8cd7", |
|
"#4862ce", |
|
"#af0f59", |
|
"#ad6bd0", |
|
"#3f0a72", |
|
"#e01073", |
|
"#144ada", |
|
"#5cb9ca", |
|
"#51d0da", |
|
"#d6d07a", |
|
"#b61e76", |
|
"#474ff9", |
|
"#68bece", |
|
"#d01b19", |
|
"#ee26df", |
|
"#2ebca4", |
|
"#539908", |
|
"#ec0a37", |
|
"#1a5613", |
|
"#da28db", |
|
"#246fa5", |
|
"#bbfe83", |
|
"#d54222", |
|
"#580c96", |
|
"#02cada", |
|
"#996ff1", |
|
"#e2a239", |
|
"#ae5204", |
|
"#4ce72d", |
|
"#2cde7f", |
|
"#b64eac", |
|
"#591ab9", |
|
"#a958c9", |
|
"#696eaa", |
|
"#4c4355", |
|
"#6a6c06", |
|
"#df5d2e", |
|
"#9780cf", |
|
"#682d42", |
|
"#efed10", |
|
"#1b312a", |
|
"#dbde1c", |
|
"#e1b5db", |
|
"#a95826", |
|
"#4e797a", |
|
"#10384a", |
|
"#9a5ba2", |
|
"#d34482", |
|
"#8a29da", |
|
"#fb9dce", |
|
"#ff2d6a", |
|
"#50f10d", |
|
"#f8d349", |
|
"#7b4427", |
|
"#11a70e", |
|
"#987252", |
|
"#c932c1", |
|
"#2d7f7d", |
|
"#c1e3c5", |
|
"#0c777d", |
|
"#0f8781", |
|
"#dd889c", |
|
"#799a24", |
|
"#4212f1", |
|
"#e6f378", |
|
"#805527", |
|
"#091a90", |
|
"#a9541c", |
|
"#fcdcad", |
|
"#01f59b", |
|
"#94a85d", |
|
"#426575", |
|
"#7f03bd", |
|
"#2dcfac", |
|
"#52b6df", |
|
"#73e76a", |
|
"#d70d97", |
|
"#601568", |
|
"#d4b1ce", |
|
"#7341ee", |
|
"#bb0ee6", |
|
"#f645e0", |
|
"#1c2c7e", |
|
"#7dd58b", |
|
"#4b9a93", |
|
"#9df332", |
|
"#612b32", |
|
"#b1c27d", |
|
"#3626a5" |
|
], |
|
"opacity": 0.8, |
|
"size": 5 |
|
}, |
|
"mode": "markers", |
|
"text": [ |
|
"Video: 59506507<br>Text: Well, I realized that was a whole lot of theory, but I hope it gave you a good intuition that will\nb...", |
|
"Video: 59671315<br>Text: Okay, so here we are, back in Jupyter Lab, ready for the next use of a frontier model, and see this\n...", |
|
"Video: 60616895<br>Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", |
|
"Video: 60619275<br>Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", |
|
"Video: 59472693<br>Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", |
|
"Video: 59670121<br>Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", |
|
"Video: 59295619<br>Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", |
|
"Video: 60617163<br>Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", |
|
"Video: 60616423<br>Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", |
|
"Video: 59170227<br>Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", |
|
"Video: 59169985<br>Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", |
|
"Video: 60616927<br>Text: It's time for our first LM experiment at this point.\nSo some of this you may know well, you may know...", |
|
"Video: 59673721<br>Text: And here we are in JupyterLab for the last time, and we are looking here at day five, the last day\no...", |
|
"Video: 59508055<br>Text: I'm so very happy that you've reached this epic moment in the course and that you're hanging in ther...", |
|
"Video: 59670259<br>Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", |
|
"Video: 60616623<br>Text: So we're now going to start week one of the course when we are going to be looking at exploring fron...", |
|
"Video: 59472383<br>Text: And welcome back to the week six folder.\nWe're now at day two, which is the second and final stage o...", |
|
"Video: 59670171<br>Text: So as the very final step on this part four of day two of week eight, we are now going to build an\ne...", |
|
"Video: 59297721<br>Text: And so now the time has come to talk about the most crucial aspect of Rag, which is the idea of vect...", |
|
"Video: 59297599<br>Text: Well, that was a sneaky detour I took you on in the last one.\nI hope you enjoyed it though, and I ho...", |
|
"Video: 59507635<br>Text: Look, I hope you're excited.\nYou really should be.\nYou've been through 80% of the course and it's al...", |
|
"Video: 59669375<br>Text: Here we are for the day.\n2.1 notebook.\nAnd don't let it be said that I don't ever do anything for yo...", |
|
"Video: 59297733<br>Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", |
|
"Video: 59670369<br>Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", |
|
"Video: 59166281<br>Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", |
|
"Video: 59671567<br>Text: Well, the first thing you're going to notice is that I don't have a notebook open for you.\nAnd that'...", |
|
"Video: 59297593<br>Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", |
|
"Video: 59166461<br>Text: And welcome back to the lab.\nHere we are in Jupyter Lab and we are going to go into week two.\nAnd we...", |
|
"Video: 59167007<br>Text: Well, how fabulous is that?\nI hope that you are as wowed as I am by our new airline, I assistant and...", |
|
"Video: 59508121<br>Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", |
|
"Video: 59295579<br>Text: All right.\nAre you excited to see how this goes?\nLet's give it a try.\nSo in this next section, I cre...", |
|
"Video: 60620375<br>Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", |
|
"Video: 59472491<br>Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", |
|
"Video: 59472425<br>Text: Welcome to week six, day three.\nToday is going to be a day that you will either love or you will hat...", |
|
"Video: 59508057<br>Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", |
|
"Video: 60619577<br>Text: And for the final piece of background information, I wanted to take another moment to talk about API...", |
|
"Video: 59170291<br>Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", |
|
"Video: 60619651<br>Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", |
|
"Video: 59473191<br>Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", |
|
"Video: 59170297<br>Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", |
|
"Video: 59167015<br>Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", |
|
"Video: 59170043<br>Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", |
|
"Video: 59473147<br>Text: Well, I'm very relieved.\nI've got that behind me.\nNo more human testing for me.\nWe'll have one final...", |
|
"Video: 59166453<br>Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", |
|
"Video: 59166915<br>Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", |
|
"Video: 59667365<br>Text: Here we are back in Colab, looking at the week seven, day five of the Colab notebooks and I'm on a\nT...", |
|
"Video: 60616845<br>Text: We're on the home stretch.\nThis is the final step in the environment setup, and it's an easy one.\nIt...", |
|
"Video: 59295459<br>Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", |
|
"Video: 59471979<br>Text: So we now turn to the parts of the problem, which is perhaps, let's say, not as glamorous as some\nof...", |
|
"Video: 59503705<br>Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", |
|
"Video: 59472505<br>Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", |
|
"Video: 59669217<br>Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", |
|
"Video: 59671221<br>Text: I gotta tell you, I don't like to toot my horn a whole lot, but I do think that I've done a great\njo...", |
|
"Video: 59503703<br>Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", |
|
"Video: 59473201<br>Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", |
|
"Video: 60622463<br>Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", |
|
"Video: 60619299<br>Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", |
|
"Video: 59295607<br>Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", |
|
"Video: 59297575<br>Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", |
|
"Video: 59507687<br>Text: It's time for action, everybody.\nWe've set up our colab.\nHere we are, week seven, day three.\nWe've g...", |
|
"Video: 59671441<br>Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", |
|
"Video: 59673431<br>Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", |
|
"Video: 59473137<br>Text: Let's get straight to it.\nSo the place where you can see everything that's going on and get knee dee...", |
|
"Video: 59166421<br>Text: Welcome back to the radio day in the lab.\nMore to do.\nLet's keep going.\nWhere we left off is we had ...", |
|
"Video: 59295599<br>Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", |
|
"Video: 59669631<br>Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", |
|
"Video: 59673663<br>Text: But wait, there's more.\nWe need to add some more to the user interface just to make it look more coo...", |
|
"Video: 59506929<br>Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", |
|
"Video: 59504785<br>Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", |
|
"Video: 59505337<br>Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", |
|
"Video: 59271655<br>Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", |
|
"Video: 59472883<br>Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", |
|
"Video: 59673639<br>Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", |
|
"Video: 59472463<br>Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", |
|
"Video: 59297595<br>Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", |
|
"Video: 60619149<br>Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", |
|
"Video: 59297735<br>Text: And at last the time has come to see rag in action.\nAfter all of this talk, and here we are.\nWe're i...", |
|
"Video: 60616407<br>Text: And now over to my Mac people.\nAnd I have news for you.\nIt's exactly the same thing.\nYou go to a fav...", |
|
"Video: 59170235<br>Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", |
|
"Video: 59472067<br>Text: So we've covered steps 1 to 4 of the five step strategy.\nAnd that brings us to step five, which is p...", |
|
"Video: 59472011<br>Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", |
|
"Video: 59295553<br>Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", |
|
"Video: 59297773<br>Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", |
|
"Video: 59295583<br>Text: And here we are back in JupyterLab.\nIt's been a minute.\nWe've been working in Colab for last week, a...", |
|
"Video: 59507329<br>Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", |
|
"Video: 59295429<br>Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", |
|
"Video: 60595637<br>Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", |
|
"Video: 59668027<br>Text: And so here we are at the home page for modal.\nAt modal.com spelt model not not model which is confu...", |
|
"Video: 59295527<br>Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", |
|
"Video: 59295377<br>Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", |
|
"Video: 59666211<br>Text: So before we try our new model and one more recap on the models so far and keep notes of this so we\n...", |
|
"Video: 59170107<br>Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", |
|
"Video: 60616833<br>Text: So I realized that day one of week one has been a pretty long day, and I assure you that the other,\n...", |
|
"Video: 59472413<br>Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", |
|
"Video: 59297561<br>Text: And would you believe at this point you're 55% of the way along the journey?\nUh, it's been a while s...", |
|
"Video: 59669211<br>Text: Well, we took on a lot today and we seem to have been successful.\nThese red icons that you see on th...", |
|
"Video: 59166981<br>Text: Welcome to week two, day five.\nThe last day of week two where a lot is coming together.\nI am so grat...", |
|
"Video: 60619227<br>Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", |
|
"Video: 60620395<br>Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", |
|
"Video: 59665127<br>Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", |
|
"Video: 59668923<br>Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", |
|
"Video: 59504887<br>Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", |
|
"Video: 59170165<br>Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", |
|
"Video: 60617251<br>Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", |
|
"Video: 59166951<br>Text: All right, back to the lab.\nBack to our project.\nTime to work with tools.\nI am in the week two folde...", |
|
"Video: 60619619<br>Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", |
|
"Video: 60616663<br>Text: Well.\nHi there, this is time for PC people to get set up.\nSo all you Mac people out there, you don't...", |
|
"Video: 59508175<br>Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", |
|
"Video: 59670087<br>Text: And welcome to part four of day two of week eight.\nUh, there's a lot happening this week, and I have...", |
|
"Video: 59506713<br>Text: Hi everyone.\nSo the reason I'm so fired up about week seven is that this is the time when we actuall...", |
|
"Video: 60620169<br>Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", |
|
"Video: 59295435<br>Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", |
|
"Video: 59297609<br>Text: Last week, we worked with models that were able to speed up code by a factor of 60,000 times, which\n...", |
|
"Video: 59507489<br>Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", |
|
"Video: 59295549<br>Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", |
|
"Video: 59665129<br>Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", |
|
"Video: 59169991<br>Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", |
|
"Video: 59472027<br>Text: And now the time has come to curate our data set.\nAnd the way we're going to do this is we're going ...", |
|
"Video: 59472307<br>Text: Welcome to week six.\nDay two a day.\nWhen we get back into the data, we look back in anger at our dat...", |
|
"Video: 59508289<br>Text: So here we are now, back in the Colab, in the same one that we kicked off in the previous day.\nIt's ...", |
|
"Video: 59472333<br>Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", |
|
"Video: 59295431<br>Text: Now I want to take a quick moment to give you a flyby of five different ways that llms are used comm...", |
|
"Video: 59673449<br>Text: Well, I have to tell you that I'm a little bit sad.\nThis is the beginning of the beginning of the en...", |
|
"Video: 59669389<br>Text: Well.\nHi there.\nSo you've made it to day two of week eight, and I am super grateful that you've been...", |
|
"Video: 59170057<br>Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", |
|
"Video: 59166949<br>Text: Welcome back to making chatbots.\nLet's keep going.\nSo for the next part we're going to beef up the s...", |
|
"Video: 59473019<br>Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", |
|
"Video: 59297585<br>Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", |
|
"Video: 59170255<br>Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", |
|
"Video: 60614589<br>Text: So we're now going to run a large language model directly on your box using a platform called llama,...", |
|
"Video: 59297601<br>Text: I'm not going to lie, at this point you have every reason to be impatient with me.\nWe've been yammer...", |
|
"Video: 60616629<br>Text: And welcome back to team PC and Team Mac as we come back together again for a quick video.\nIn this o...", |
|
"Video: 59297749<br>Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", |
|
"Video: 59170135<br>Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", |
|
"Video: 59472017<br>Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", |
|
"Video: 59507017<br>Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", |
|
"Video: 60619883<br>Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", |
|
"Video: 59508297<br>Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", |
|
"Video: 60619247<br>Text: We're going to spend a little bit more time with GPT just to try out a few more interesting things.\n...", |
|
"Video: 59504769<br>Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", |
|
"Video: 59170233<br>Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", |
|
"Video: 59671231<br>Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", |
|
"Video: 60620397<br>Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", |
|
"Video: 59170093<br>Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", |
|
"Video: 59473089<br>Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", |
|
"Video: 60395261<br>Text: Let's keep going with our project to equip our LM with a tool.\nWe just created this piece of code to...", |
|
"Video: 60617259<br>Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", |
|
"Video: 59507313<br>Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", |
|
"Video: 60619721<br>Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", |
|
"Video: 59295451<br>Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", |
|
"Video: 59166919<br>Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", |
|
"Video: 59295441<br>Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", |
|
"Video: 59295541<br>Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", |
|
"Video: 59473101<br>Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", |
|
"Video: 59507423<br>Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", |
|
"Video: 59295545<br>Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", |
|
"Video: 59472503<br>Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", |
|
"Video: 60614591<br>Text: The mantra of this course is that the best way to learn is by doing, and we will be doing stuff toge...", |
|
"Video: 59473021<br>Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", |
|
"Video: 60617255<br>Text: I'm now going to talk for a bit about models.\nA term you often hear is the term frontier models, whi...", |
|
"Video: 59667829<br>Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", |
|
"Video: 59505329<br>Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", |
|
"Video: 59669049<br>Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", |
|
"Video: 60619439<br>Text: This now brings us to an extremely important property of LMS called the context window that I want t...", |
|
"Video: 59668181<br>Text: And so it gives me great pleasure to introduce to you the project that I've lined up for you this we...", |
|
"Video: 59472441<br>Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", |
|
"Video: 59507785<br>Text: Well, I'm sure you're fed up of me saying that the moment of truth has arrived, but it really has.\nT...", |
|
"Video: 59295587<br>Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", |
|
"Video: 59166465<br>Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", |
|
"Video: 59473071<br>Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", |
|
"Video: 59295423<br>Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", |
|
"Video: 59297723<br>Text: So I know what you're thinking.\nYou're thinking, what's going on here?\nWe're on day five.\nWe're on d...", |
|
"Video: 59166947<br>Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", |
|
"Video: 59666831<br>Text: Take one more moment to look at this very nice diagram that lays it all out, and we will move on.\nNo...", |
|
"Video: 59295493<br>Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", |
|
"Video: 60616855<br>Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", |
|
"Video: 59506611<br>Text: So in a future day, I'm going to be training, fine tuning a model and creating a fine tuned model.\nA...", |
|
"Video: 60616493<br>Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", |
|
"Video: 59166317<br>Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", |
|
"Video: 59295439<br>Text: So I'm aware that there's a big risk that you are getting fed up of leaderboards, because we've done...", |
|
"Video: 59472421<br>Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", |
|
"Video: 59472137<br>Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", |
|
"Video: 59297693<br>Text: So at the end of each week, it's customary for me to give you a challenge, an assignment to do on\nyo...", |
|
"Video: 60620143<br>Text: So we're going to make a call to GPT four.\nOh, that's going to ask it to look through a set of links...", |
|
"Video: 60619501<br>Text: I welcome to day four of our time together.\nThis is a very important day.\nToday we're going to be lo...", |
|
"Video: 59297743<br>Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", |
|
"Video: 59166847<br>Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", |
|
"Video: 59170223<br>Text: Well.\nFantastic.\nIt's coming up to the end of the week, and that means it's coming up to a challenge...", |
|
"Video: 59170037<br>Text: So how does it feel to be 30% of the way down the journey to being a proficient LLM engineer?\nTake a...", |
|
"Video: 59295609<br>Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", |
|
"Video: 60619281<br>Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", |
|
"Video: 59472429<br>Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", |
|
"Video: 59167009<br>Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", |
|
"Video: 59166481<br>Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", |
|
"Video: 59670933<br>Text: I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very sat...", |
|
"Video: 59670073<br>Text: Okay, it's time to complete the Rag workflow in our Jupyter Lab on day 2.3.\nWe've got this function ...", |
|
"Video: 59673595<br>Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", |
|
"Video: 59297603<br>Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", |
|
"Video: 60614541<br>Text: I am delighted to welcome you to the first day of our eight weeks together as you join me on this ad...", |
|
"Video: 59667357<br>Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", |
|
"Video: 59667841<br>Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", |
|
"Video: 59472007<br>Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", |
|
"Video: 59507435<br>Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", |
|
"Video: 59509185<br>Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", |
|
"Video: 59473159<br>Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", |
|
"Video: 60619447<br>Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", |
|
"Video: 59166353<br>Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", |
|
"Video: 60619123<br>Text: So what we're now going to do is we're going to look at some models in practice and start to compare...", |
|
"Video: 59295363<br>Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", |
|
"Video: 60619289<br>Text: And now we'll go a bit faster through the other models.\nWe'll start with Google's Gemini.\nI have the...", |
|
"Video: 59472873<br>Text: So it's quite an adventure that we had at the frontier of what's capable with Llms today, solving a\n...", |
|
"Video: 60619429<br>Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", |
|
"Video: 59295601<br>Text: So it's time to continue our journey into the world of open source and understand which models we sh...", |
|
"Video: 59170025<br>Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", |
|
"Video: 59166443<br>Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", |
|
"Video: 60620025<br>Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", |
|
"Video: 59170055<br>Text: Welcome to the world of Google Colab.\nYou may already be very familiar with Google Colab, even if so..." |
|
], |
|
"type": "scatter", |
|
"x": [ |
|
-12.589552, |
|
3.4522862, |
|
6.075746, |
|
7.942426, |
|
-3.525712, |
|
4.1480594, |
|
4.6078315, |
|
-1.7122985, |
|
-1.6395565, |
|
-9.307264, |
|
-6.770974, |
|
1.4278501, |
|
-3.795615, |
|
-5.48206, |
|
-4.170929, |
|
0.42981502, |
|
-3.5235593, |
|
1.8772042, |
|
17.16095, |
|
15.35386, |
|
-11.031532, |
|
15.838091, |
|
14.824762, |
|
-2.4908643, |
|
-4.1442113, |
|
-6.1486583, |
|
14.927404, |
|
-2.396536, |
|
-3.8051388, |
|
-6.8470283, |
|
7.2692485, |
|
-3.5521216, |
|
-2.7953513, |
|
-3.2857506, |
|
-5.7256823, |
|
9.390827, |
|
-8.941686, |
|
8.362188, |
|
-2.4580688, |
|
-7.4087963, |
|
-0.73915297, |
|
-9.044852, |
|
4.499095, |
|
1.223194, |
|
0.6079307, |
|
-2.3045015, |
|
9.307752, |
|
4.968605, |
|
-3.0444636, |
|
-13.019468, |
|
-1.9913696, |
|
16.247093, |
|
-6.6251817, |
|
-3.236832, |
|
2.7420254, |
|
8.059585, |
|
5.8575497, |
|
1.3678622, |
|
14.408681, |
|
-7.4271216, |
|
4.6005616, |
|
-6.2227287, |
|
-8.091358, |
|
-1.0886598, |
|
3.9747384, |
|
0.32758102, |
|
-5.358367, |
|
0.61464316, |
|
-10.948633, |
|
-13.510744, |
|
-10.267108, |
|
3.5313623, |
|
-4.744116, |
|
0.98348933, |
|
15.8871355, |
|
8.520779, |
|
12.316195, |
|
13.00314, |
|
-7.271094, |
|
-12.220864, |
|
-1.1228861, |
|
8.195982, |
|
15.675435, |
|
3.5282235, |
|
2.7380142, |
|
3.0779696, |
|
-7.539173, |
|
9.471518, |
|
2.180644, |
|
1.8750061, |
|
1.8318319, |
|
-7.089598, |
|
-0.79000425, |
|
0.13995205, |
|
16.312626, |
|
-3.438324, |
|
-4.710372, |
|
6.9159217, |
|
4.997074, |
|
-11.944866, |
|
-6.278514, |
|
-7.310172, |
|
-8.248277, |
|
-0.2617442, |
|
-2.001054, |
|
-2.4265862, |
|
7.9734154, |
|
-4.359084, |
|
1.4919127, |
|
-0.38369736, |
|
2.8925261, |
|
2.770904, |
|
11.788717, |
|
-11.200065, |
|
7.0120173, |
|
-12.489671, |
|
-7.3114347, |
|
-1.5968479, |
|
-2.0740008, |
|
-7.660865, |
|
1.4215823, |
|
3.4180312, |
|
-5.9557977, |
|
-4.101128, |
|
-7.1637955, |
|
1.2174717, |
|
-8.017974, |
|
13.607655, |
|
-8.332471, |
|
12.951081, |
|
13.259139, |
|
7.851571, |
|
11.287736, |
|
-8.430205, |
|
-2.83165, |
|
-9.306727, |
|
1.3151592, |
|
-2.5466766, |
|
9.444017, |
|
-12.522999, |
|
-10.38123, |
|
-7.0192504, |
|
0.9397985, |
|
-9.068451, |
|
4.640919, |
|
-2.51455, |
|
5.657744, |
|
1.8063583, |
|
-15.553587, |
|
0.9260013, |
|
-4.1032104, |
|
4.0678425, |
|
6.9909325, |
|
4.943192, |
|
-2.3060699, |
|
1.6395743, |
|
-0.48130858, |
|
1.4182721, |
|
-0.63343734, |
|
5.6635394, |
|
-3.9217196, |
|
-6.3144593, |
|
8.239023, |
|
8.01618, |
|
-8.5425, |
|
-0.17059784, |
|
-6.761717, |
|
5.7745337, |
|
-1.1535196, |
|
-2.372529, |
|
3.1349926, |
|
14.739626, |
|
-3.0802853, |
|
-13.388992, |
|
3.012913, |
|
10.2796135, |
|
-13.004479, |
|
-0.6004416, |
|
-2.7484965, |
|
4.0349708, |
|
1.1794678, |
|
-3.6047134, |
|
2.0950997, |
|
3.1776624, |
|
5.355312, |
|
9.249312, |
|
-5.047935, |
|
-2.5895002, |
|
-6.023992, |
|
0.42378932, |
|
6.4555655, |
|
11.28314, |
|
-6.1557565, |
|
2.6091251, |
|
-6.8104343, |
|
4.435232, |
|
-6.023258, |
|
16.286194, |
|
-0.5731437, |
|
2.0213904, |
|
8.013111, |
|
-1.5368563, |
|
-10.384564, |
|
-8.238789, |
|
-0.057244953, |
|
-15.348441, |
|
-1.7015631, |
|
6.999166, |
|
2.5275056, |
|
8.751711, |
|
1.0946581, |
|
-8.001234, |
|
2.8864157, |
|
-7.969383, |
|
-0.49457392, |
|
5.2979984, |
|
-7.2938204 |
|
], |
|
"y": [ |
|
-6.5300555, |
|
14.089418, |
|
12.162957, |
|
-0.80311126, |
|
-1.6755519, |
|
-13.505905, |
|
6.5699277, |
|
1.4233526, |
|
3.7408068, |
|
-3.009902, |
|
-1.6519994, |
|
9.911368, |
|
14.304171, |
|
-9.145412, |
|
6.8292613, |
|
4.1779256, |
|
-13.0463, |
|
-11.951641, |
|
5.743851, |
|
10.09115, |
|
-8.627289, |
|
2.584683, |
|
7.23334, |
|
6.759529, |
|
4.1768756, |
|
12.57557, |
|
9.190438, |
|
13.93031, |
|
5.511717, |
|
-11.910828, |
|
5.8589373, |
|
3.6352885, |
|
-13.270146, |
|
-2.0432546, |
|
-9.36256, |
|
-4.0989513, |
|
2.833454, |
|
-4.2829947, |
|
-6.8667107, |
|
-5.736574, |
|
11.985562, |
|
0.33564866, |
|
-7.6441755, |
|
11.567259, |
|
10.677815, |
|
-9.594754, |
|
14.068278, |
|
-2.490878, |
|
-4.379241, |
|
-6.612759, |
|
-12.312431, |
|
2.8374946, |
|
10.107471, |
|
0.86265963, |
|
-7.4858155, |
|
13.485198, |
|
0.44996768, |
|
0.12787041, |
|
9.892149, |
|
-7.652323, |
|
13.810954, |
|
13.420327, |
|
-9.137389, |
|
14.72838, |
|
7.676501, |
|
-12.387229, |
|
14.694999, |
|
-5.226657, |
|
-8.565104, |
|
-5.734247, |
|
0.18139325, |
|
-9.293782, |
|
14.728803, |
|
-13.8647995, |
|
6.203831, |
|
0.3127214, |
|
8.967697, |
|
-1.4659885, |
|
-1.9273498, |
|
3.1576743, |
|
-2.5850005, |
|
5.1483097, |
|
5.489101, |
|
8.593102, |
|
-9.933031, |
|
-4.0722184, |
|
-10.497164, |
|
-10.699288, |
|
1.5761652, |
|
-3.9312649, |
|
-7.012359, |
|
1.2585955, |
|
3.0229156, |
|
-16.250467, |
|
6.635525, |
|
6.7354093, |
|
8.468663, |
|
-1.6286882, |
|
10.374195, |
|
-10.649093, |
|
-4.4278836, |
|
-6.3712683, |
|
2.0350108, |
|
4.080304, |
|
11.325701, |
|
2.5883422, |
|
13.5416975, |
|
-11.214315, |
|
-11.917827, |
|
-2.9803138, |
|
10.627456, |
|
2.9241033, |
|
7.383127, |
|
-11.1040945, |
|
5.136991, |
|
-10.0327215, |
|
-16.114536, |
|
-12.599517, |
|
-2.9481568, |
|
-11.174494, |
|
-8.890177, |
|
-1.0979837, |
|
6.8131933, |
|
1.463663, |
|
0.48312876, |
|
12.914509, |
|
-12.583761, |
|
8.630306, |
|
-4.9693522, |
|
-1.45638, |
|
8.836656, |
|
14.184286, |
|
9.729582, |
|
0.11569965, |
|
-12.046801, |
|
-3.562859, |
|
8.306646, |
|
-0.12532109, |
|
1.8029642, |
|
-7.7512345, |
|
-2.2794526, |
|
11.317182, |
|
8.203367, |
|
-1.5793608, |
|
-7.4279957, |
|
10.902695, |
|
9.414275, |
|
-6.943587, |
|
-9.714286, |
|
-2.107979, |
|
8.535427, |
|
-2.2587268, |
|
4.7189612, |
|
-9.422279, |
|
-9.95208, |
|
1.4961839, |
|
-15.637048, |
|
5.8088226, |
|
-10.609174, |
|
-0.896489, |
|
2.6177058, |
|
-6.1964593, |
|
-10.441606, |
|
-5.6452084, |
|
6.2846713, |
|
-16.04126, |
|
-9.215314, |
|
5.74158, |
|
14.189653, |
|
-6.3043413, |
|
-2.132232, |
|
5.4891644, |
|
9.997777, |
|
-10.8906975, |
|
3.0431316, |
|
-3.8775747, |
|
-7.930391, |
|
6.087151, |
|
13.401266, |
|
-3.5819368, |
|
-10.400259, |
|
-3.2769384, |
|
5.1977687, |
|
11.534197, |
|
1.2013716, |
|
9.190291, |
|
4.5081005, |
|
4.3416286, |
|
2.9420025, |
|
-1.041198, |
|
-0.5506536, |
|
6.695455, |
|
9.870885, |
|
9.233299, |
|
5.8174458, |
|
-13.40291, |
|
5.86892, |
|
5.1465583, |
|
5.9692817, |
|
-8.088498, |
|
-9.699435, |
|
-4.3566523, |
|
-11.696756, |
|
-11.084373, |
|
-9.227834, |
|
-9.344566, |
|
8.588015, |
|
0.74937767, |
|
-1.7350386, |
|
0.8596554, |
|
-8.018119, |
|
9.262971, |
|
0.69532895, |
|
-0.87655604, |
|
9.858918, |
|
12.275479, |
|
-16.078566 |
|
] |
|
} |
|
], |
|
"layout": { |
|
"height": 600, |
|
"margin": { |
|
"b": 10, |
|
"l": 10, |
|
"r": 20, |
|
"t": 40 |
|
}, |
|
"scene": { |
|
"xaxis": { |
|
"title": { |
|
"text": "x" |
|
} |
|
}, |
|
"yaxis": { |
|
"title": { |
|
"text": "y" |
|
} |
|
} |
|
}, |
|
"template": { |
|
"data": { |
|
"bar": [ |
|
{ |
|
"error_x": { |
|
"color": "#2a3f5f" |
|
}, |
|
"error_y": { |
|
"color": "#2a3f5f" |
|
}, |
|
"marker": { |
|
"line": { |
|
"color": "#E5ECF6", |
|
"width": 0.5 |
|
}, |
|
"pattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
} |
|
}, |
|
"type": "bar" |
|
} |
|
], |
|
"barpolar": [ |
|
{ |
|
"marker": { |
|
"line": { |
|
"color": "#E5ECF6", |
|
"width": 0.5 |
|
}, |
|
"pattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
} |
|
}, |
|
"type": "barpolar" |
|
} |
|
], |
|
"carpet": [ |
|
{ |
|
"aaxis": { |
|
"endlinecolor": "#2a3f5f", |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"minorgridcolor": "white", |
|
"startlinecolor": "#2a3f5f" |
|
}, |
|
"baxis": { |
|
"endlinecolor": "#2a3f5f", |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"minorgridcolor": "white", |
|
"startlinecolor": "#2a3f5f" |
|
}, |
|
"type": "carpet" |
|
} |
|
], |
|
"choropleth": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"type": "choropleth" |
|
} |
|
], |
|
"contour": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "contour" |
|
} |
|
], |
|
"contourcarpet": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"type": "contourcarpet" |
|
} |
|
], |
|
"heatmap": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "heatmap" |
|
} |
|
], |
|
"heatmapgl": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "heatmapgl" |
|
} |
|
], |
|
"histogram": [ |
|
{ |
|
"marker": { |
|
"pattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
} |
|
}, |
|
"type": "histogram" |
|
} |
|
], |
|
"histogram2d": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "histogram2d" |
|
} |
|
], |
|
"histogram2dcontour": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "histogram2dcontour" |
|
} |
|
], |
|
"mesh3d": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"type": "mesh3d" |
|
} |
|
], |
|
"parcoords": [ |
|
{ |
|
"line": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "parcoords" |
|
} |
|
], |
|
"pie": [ |
|
{ |
|
"automargin": true, |
|
"type": "pie" |
|
} |
|
], |
|
"scatter": [ |
|
{ |
|
"fillpattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
}, |
|
"type": "scatter" |
|
} |
|
], |
|
"scatter3d": [ |
|
{ |
|
"line": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatter3d" |
|
} |
|
], |
|
"scattercarpet": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattercarpet" |
|
} |
|
], |
|
"scattergeo": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattergeo" |
|
} |
|
], |
|
"scattergl": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattergl" |
|
} |
|
], |
|
"scattermapbox": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattermapbox" |
|
} |
|
], |
|
"scatterpolar": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatterpolar" |
|
} |
|
], |
|
"scatterpolargl": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatterpolargl" |
|
} |
|
], |
|
"scatterternary": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatterternary" |
|
} |
|
], |
|
"surface": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "surface" |
|
} |
|
], |
|
"table": [ |
|
{ |
|
"cells": { |
|
"fill": { |
|
"color": "#EBF0F8" |
|
}, |
|
"line": { |
|
"color": "white" |
|
} |
|
}, |
|
"header": { |
|
"fill": { |
|
"color": "#C8D4E3" |
|
}, |
|
"line": { |
|
"color": "white" |
|
} |
|
}, |
|
"type": "table" |
|
} |
|
] |
|
}, |
|
"layout": { |
|
"annotationdefaults": { |
|
"arrowcolor": "#2a3f5f", |
|
"arrowhead": 0, |
|
"arrowwidth": 1 |
|
}, |
|
"autotypenumbers": "strict", |
|
"coloraxis": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"colorscale": { |
|
"diverging": [ |
|
[ |
|
0, |
|
"#8e0152" |
|
], |
|
[ |
|
0.1, |
|
"#c51b7d" |
|
], |
|
[ |
|
0.2, |
|
"#de77ae" |
|
], |
|
[ |
|
0.3, |
|
"#f1b6da" |
|
], |
|
[ |
|
0.4, |
|
"#fde0ef" |
|
], |
|
[ |
|
0.5, |
|
"#f7f7f7" |
|
], |
|
[ |
|
0.6, |
|
"#e6f5d0" |
|
], |
|
[ |
|
0.7, |
|
"#b8e186" |
|
], |
|
[ |
|
0.8, |
|
"#7fbc41" |
|
], |
|
[ |
|
0.9, |
|
"#4d9221" |
|
], |
|
[ |
|
1, |
|
"#276419" |
|
] |
|
], |
|
"sequential": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"sequentialminus": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
] |
|
}, |
|
"colorway": [ |
|
"#636efa", |
|
"#EF553B", |
|
"#00cc96", |
|
"#ab63fa", |
|
"#FFA15A", |
|
"#19d3f3", |
|
"#FF6692", |
|
"#B6E880", |
|
"#FF97FF", |
|
"#FECB52" |
|
], |
|
"font": { |
|
"color": "#2a3f5f" |
|
}, |
|
"geo": { |
|
"bgcolor": "white", |
|
"lakecolor": "white", |
|
"landcolor": "#E5ECF6", |
|
"showlakes": true, |
|
"showland": true, |
|
"subunitcolor": "white" |
|
}, |
|
"hoverlabel": { |
|
"align": "left" |
|
}, |
|
"hovermode": "closest", |
|
"mapbox": { |
|
"style": "light" |
|
}, |
|
"paper_bgcolor": "white", |
|
"plot_bgcolor": "#E5ECF6", |
|
"polar": { |
|
"angularaxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
}, |
|
"bgcolor": "#E5ECF6", |
|
"radialaxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
} |
|
}, |
|
"scene": { |
|
"xaxis": { |
|
"backgroundcolor": "#E5ECF6", |
|
"gridcolor": "white", |
|
"gridwidth": 2, |
|
"linecolor": "white", |
|
"showbackground": true, |
|
"ticks": "", |
|
"zerolinecolor": "white" |
|
}, |
|
"yaxis": { |
|
"backgroundcolor": "#E5ECF6", |
|
"gridcolor": "white", |
|
"gridwidth": 2, |
|
"linecolor": "white", |
|
"showbackground": true, |
|
"ticks": "", |
|
"zerolinecolor": "white" |
|
}, |
|
"zaxis": { |
|
"backgroundcolor": "#E5ECF6", |
|
"gridcolor": "white", |
|
"gridwidth": 2, |
|
"linecolor": "white", |
|
"showbackground": true, |
|
"ticks": "", |
|
"zerolinecolor": "white" |
|
} |
|
}, |
|
"shapedefaults": { |
|
"line": { |
|
"color": "#2a3f5f" |
|
} |
|
}, |
|
"ternary": { |
|
"aaxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
}, |
|
"baxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
}, |
|
"bgcolor": "#E5ECF6", |
|
"caxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
} |
|
}, |
|
"title": { |
|
"x": 0.05 |
|
}, |
|
"xaxis": { |
|
"automargin": true, |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "", |
|
"title": { |
|
"standoff": 15 |
|
}, |
|
"zerolinecolor": "white", |
|
"zerolinewidth": 2 |
|
}, |
|
"yaxis": { |
|
"automargin": true, |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "", |
|
"title": { |
|
"standoff": 15 |
|
}, |
|
"zerolinecolor": "white", |
|
"zerolinewidth": 2 |
|
} |
|
} |
|
}, |
|
"title": { |
|
"text": "2D Chroma Vector Store Visualization" |
|
}, |
|
"width": 800 |
|
} |
|
}, |
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABPAAAAJYCAYAAADsXBi6AAAgAElEQVR4XuydB2BUxdqG3930QhJ6B2mKoKAIghVQQEVEr7137L2i/vaKvfeCvVdEERRFBVFBQBDpCEgLLQnpm939Zw5uyIaQbDnn7Jwz77m/97+SMzPf93yTkDyZ4gmKB3xIgARIgARIgARIgARIgARIgARIgARIgARIgASUJOChwFOyLgyKBEiABEiABEiABEiABEiABEiABEiABEiABAwCFHicCCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSgMAEKPIWLw9BIgARIgARIgARIgARIgARIgARIgARIgARIgAKPc4AESIAESIAESIAESIAESIAESIAESIAESIAEFCZAgadwcRgaCZAACZAACZAACZAACZAACZAACZAACZAACVDgcQ6QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgMIEKPAULg5DIwESIAESIAESIAESIAESIAESIAESIAESIAEKPDEHZv+1BM+89hnmL/4HwUAQu3Zpj4vOHIkBfXpUz5DDT70Bq9bkV/97amoKWjTNQ589d8XJxxyC3j26RDSbAqL/LyZOxWcTfsbCJStRVlGJ5qKffr13w5knHIbuXTtU9zPy7FvQuUNrPH7XZRH1rfJL5SLPQcddZeT51L1X7jTUI88YjTTB9pNX7lY5nR1im/LLHLzz6bdYIGq6pXAr0tNSsVuXDjhp5GCMGLqfUrmcf91DWLB4Jb7/+HGkJCfVGdsdD4/Fp1//hMkfPYbTLr1HzPNuuO+mUbbnceuDr+Ln3/7E9x89bowtPw/tiGXcxGkYfd+LmPjew2jbqpnteXNAEiABEiABEiABEiABEiABEiABEqhJQHuBN3fBcpxx2T3YR4ils4RA83g8ePmd8Zg9bwnef+H2aqEmxUF2VgZuuOQUg19FZSWWrVyLL76ZakibS88+BpeIf+p7fFV+XPF/T+LH6XMwbGBfDNp/b2RlpmPFv+vx4bgfsC5/Ex645UIcPnhfoxs3CTyZzz2Pv4kPxn2PyR8+hmZNcndANWveYpx+2b34v6vOwCnHHGrKZ2rh1hLsf9Sl+P3r55GZkW5Kn7U7kaLr/8a8gmMOPxCHDeqHpo1zsWlLoSHAJk6ZgZuvOA2nHTvUaPbtTzPxwpvj8OGLd1gSSySdfvPD77jmjmcMMTz04L47NJGydeCxV+KAfnvg0Tsuxfjvphv16r/37pF0b+o7tQWeVbHc9+Tbhsy8/pKTjfjl5+T0mX8J+bq/8TnKhwRIgARIgARIgARIgARIgARIgAQSSUB7gSdFxh9zF2Piuw9BrqqTT1FxKQ48+jIh9A7HtRedaPyZFHhyJc4rj94QVi+5ou6Bp9/B259MErLjEiFwtsm3up7HX/oIL739Je4dfb4he2o+pWUVOO/aB7FsxRoRy8PIzclyncBbuHQVjj3vVlx30Uk45+QjdkAkZc1XQhb9IFaGNcrONOXz4qdf5+KiGx+xVOANP/1GtGreBK8+duMOMV9+yxMQVhhP3XOF8bFHX/gAv8ycn1CBJ0XyIcdfhT26d8ZzD1y9Q8yh1WcvP3w99uvb05Q6xNpJbYEXaz8NtTv54ruwj1hNGxJ4Db3Pj5MACZAACZAACZAACZAACZAACZCAnQS0F3gbNxdCyrMObVuEcT/omMuNFXJ333Cu8ec7E3jyY35/AEeddZOxbXJnWz/Lyitx8P8uxz69dsXzY66ts8Zr12+ClCuhWOQKvG6d2uKQA/vg6Vc/xZp1G9GyeWNDMoRWTn056RfceO8LeOPJm3H7w68ZsXz99hhIsfja+1/j4/FTjHbp6WnYe49uuPL846pXFcrVTDfc/TzeffZWPPz8+8ZKQpmDXC123JEHQ26j/GPuIiSLlUkjDzvAEG+h559V6/DEyx+JVUrzUVZegRbNGmP4oQOMlYgpKck7ncMnX3QnSkrLMe6N+8Pe2cbnCmMF2z03nmd8bOrv8/DiW+OweNm/gksVeu3eBVdfeAL22K1TddutQrY+8fLHmPTjDBSXlKFzxza44PQRBp9nXvsUz77+efW7Bw/obQirSNjc9tCrmCdWZ5536pG4X6zOOuTAvXHX9dvmQu1n6MnXYZd2rfDSw9fV+7l71pX3Y8achdXvhESmFMaPCbE3eeosY/tt49xGGLhfb1x9wQnG/5bPzuIJBoPG1t1PvvpJrBpbhzRRv4P79zbEc12rHEODP/L8Bxj7wdfGaki5hbvmc+7VY7BazJkJ7zxorEitvW1VzpPHX/pQ8PkHJWXlQl42xlHDDsBFZ4yE1+vBR19OMebidx8+aojN0HPB9Q8bcvy9524z/iiSOVTfFlpZdzk/6nrkykE5l+Tz1seT8MEX3xur6jIz0rCb2KYu2Ya2vfccdHZYF3Ll7fIVa3fYQvv9tFnG6slFQkTLZ9fO7XDuKUcaq2nls2FTgbFNfIxYRStXk06e+ocxJ+W2+FuuPCNse3y9E4UfJAESIAESIAESIAESIAESIAESIIFaBLQXeHXNCLmtVoqm+28ehZFCTMinPoEnP/7Yix8aW29/+uwpNMnbJl1qPr/PXoCzr3rAEILHDj84ookoBZ7f78cu7Vth1GkjkJSUJFZwvS/kwBJDvMhxvvnhN7Ed8llDzsnz1roJqSCFgYzn9Q8m4LqLTxYici+xpbPIWCkoV/iNe/1+IdzyRNttWynlmWJSTnUUEurBZ9/Fmx9NxJ7dO+GGS08xzvgLbRF98aHrjG2VUoAdfur1xiq5O649G3m52UJq/GsIj9OPG2pIwp09H4//0ZBR7whpWPPcQHkm4C0PvFz951J0nXP1Axhy0D64/LzjDA5SYk6bMQ8fv3yXkJwtjSHkO6vWbBCC5HS0btEU4yZNw9j3J0DGKmOXElOKvEniLLOcRlnGNuhI2Nz92Bv4YdpstBGrLi884yjBpiXatwmXvKEcJVfJ7EghMOV5iL3EeYjJola1HykbZa3k9lq5kjNDSFV53p88Y26NkLe3XXMWuovzF6Ugu/PR10U+TfCukF1Sou0sHimUnnzlY1whGMmz9qQEvkvE7hVtPnzpzp2ecbdy9XoccdqNuGrU8cbcCj1S3A0TQrLmn9cUeFWiDoOFpJI5XnbO/wyeUlbJ+C4682icd8rwiARepHOoPoFXWFSCwq3FYZhve+g1Y45/+OKdhuwOzV05lwcLIS+3B7/w5heYJuTw+LfGGJ9DUpoOPelaHH3Ygbjs3P8Z8/rr734NE3ihlZwnjBiEM44X26EFXznPPvnqRzx7/9WGcJX9HHj05ca4kt8RhwwQAq8U512zTYTKecuHBEiABEiABEiABEiABEiABEiABGIhQIFXi9rmgq049ZK70Vj8YP/WU7cIaeY13mhI4MkVPlK6yB/Sa15EEeo+tFJu7OOj0W+v7hHVSgq8LQVF4iD9R4TsSTXa/DZrgSGt5Cq+g/rvWS3hagoXuZpNbgGW23RvvfrM6rHkSjy5Wiz0bkjg1dzS+/fiFTh+1O3GhRo3CukhH7mqr/eQ84QkOlasbjvKEHhSAMkz5aQIDD1X3vqUsXLrIyGOdvbI1Y7yfLXhh/bHndedU/2aXJ0mhcxnr91j/JmUHnJFmZQsUnLJR+Y15KRrMEysrrtdiEO59fmMy+/d4Sw3KQjl9tATjxqE19772lhdGDoDL1I28ry+dz/7bgfRWFdectWkXI347qffGYJIrvLq3bMr9tunJ0YM2c8QOqHn4tGPQa76DJ2BF6pn7e3Xn4uzFW++/6Xq8euKp6LSZ9T5ILHiTrYPPXP/Xga5JfShWy82OO/skSvt1m3YjK8E49AjZeeLb31prJ4LreCrKfDkRS7y3+WFFkeLVZmhR26PlmfFtWvdPGKBF8kciuYSC7k9Xcb/6mOjDSktn4LCYiPHmp+Ti8SKzv+d+394+r4rDaknn76HXyAE+CHVW2hrX2Ih5+cW0dfnYn5KGScfufpRSlC5YlYK45DAk8wl+9Aj45Lb52dNfKl6m/5Oi8IPkAAJkAAJkAAJkAAJkAAJkAAJkEAdBCjwakCRQuGC6x8xBIxcIRXavihfaUjgvf3Jt7jvybfwxdh70WWXtjug/vJbsdX1nheMc9IivQxACrw2LZuEbbldKlYXjTzrZjx828VihU//aoEnt9DK7bnyCa0gfPDWi4xVYTUfucVPyg25xTAk8OSWwdC21NAKLCnXjh8xsLpp/yMvNv79erGiTz5Sgrzx4TeYI27wlWIjEAwYW5Gl9Pn2/Ufq/WS785GxxsUIUz550hCTK1fnCxFyg7js4XSxfXeI0Xafwy4w8gttpw11eNnNT2D9xi2GAHtdjP/gM+8aZ+bV3gYaer+2wIuUTejCjdmTXjG2hUbylIrtpHJLsVw9OOPPhfhr4T/GCrhbrz7L2JIsn9oC79X3voLczlo7B7ndU56tF7rQo654QrnUlmlyHKNeR4p6/XcpQ13xfyVWmV1/93N486mbjdWKUkgNO+V69Nx1l7Cbj2sKPClzpRyUIk+u+NxfrMjsI1Z/1tw2HekW2kjmUKQCT14Mc8lNjxtSOMRa5ixXDL7/+feY8P2vYiv5JnHrc4Vx07Tcyivn1v+OOMhA05DAk/PxKHGhxR3XnR2GUm5BnzbjL/z8+VPVAk9uXz735OHV773/+WRjVeSUT56od1tzJHOM75AACZAACZAACZAACZAACZAACehJgALvv7rL1VyX3fI4du/a0ZAXtS9RaEjgScHy/heTMf3L5+q8tTK0WkyuiDv56EMimm113UIrb7496sybqldXhSScXLnWrVM7o99fhFA4/7qHjPPe5LlvNZ8Roq28jOOFB6+tFng124YE3gM3C2ExbP/qpjWF0L9rN+CYc25Bl45tjdV87cUKJHlOnhSY8xetaFDghVb5hcST3AIqtyP+IARHjti+KKVL70PPM8SZ3DZc85FbaZvk5RgyRG6pfe6Nz+u9oKK2wIuUjaznV5OnY9oXz0RUq7pekkL46tufwdJ/VuPbD7ataKst8ELnuM2Y8GL1KkvZl1ylJ1cqyrPazhfn8NUVTygXuV3XU0sy+nxVhgCVondnj3xnkLjMQq5CkzJLykd5kUpoq3SoXe0z8OS5bvLcPXnDrqylXIk5YsgAY7u2XIUXicCLdA5FIvCWi88JKRXlFlh542/NR27tfV+sjpXbumWe2dkZxpmQZ1x+X8QCT66glPNRXrxS8xxIOY5cdSu3f8vVdaEVeDVFtHyHAi/mTyE2JAESIAESIAESIAESIAESIAES+I8ABZ4AIc/wGiWElzyz6vZrz6rz/LL6BJ7cNinPDZMXKMgtsnU9lWK748FCyMhD/eVFF3Wt6pJb/T4RZ8TJ7avybLFYBd68hctx0oV3Ymcr8Pr23s0QO3XJv0gEXkiKff32g2GXf4TOo2toBZ7kc8IFd4gc0/Hqozca54/tu/fuxrbM0NPviAuNraHyTLLajzzfTZ4L+I7YsnrvE28awrB1y6Z1cq8t8CJlE43Ak0JIrgCs6/KOb3+aCbm1WF5wsX/fPXYQeKH4drYC73ZxLt6JYqVbXfHIFX4nXniHsSryYHEGW+1HriSteYlEXYAeevY9fDDue/z46VO445HXMEuI7G/EjcyhbaKyTW2BV7MfuUX1a7G6Td6ue8gBfTDm/y4UF6dsO+ew9iUWUprJy0jkJRaRzqGGBJ48V/AkcV6lzPNFwbj22YNyZd2h4hxFebFE6JErJOWW2GhW4Ml+RgypewXer7P+NoQyBR7/XiUBEiABEiABEiABEiABEiABErCKgPYCT17uMOKM0RgmbqyseSZbbeA7E3hyS+H/jXkFX0ycilceuQED9umx01rJG1HlGV2Xn3usOPB/ZNh7cvvpxaMfxeLl/+LLNx4wDtePVeBJoSjPRpMrkmqegSdXPR0mtkjKA/3PEpIwVoEXujjh1/HPGaJRPnIbrFwZ2Lyp2EIrVps19MgzA+9+/A08dudlhuB66+lbjIs4Qs+o6x42LnuofauvHEfeepoqzsULnfVWU8TI9rI/eRab3D4aEkW/ffW8sTosUjaRCryff5uLC294pHqra+28n3r1Ezz/xhfVW6vlCjx5W2nonMCZfy7CmVfcZ5xhd9igfaubhy5fkO/t3q1jnQJPSuEDxKUJxw4/CDddHr7yTG617tyhdZiIq6smcvWaXJUpV1zKbZ6jTjvSOOew5lNT4ElZOUtsm669NVvehDxfCEV5u7BcmXf17U8b5xmGVoXK8/oGi9V+8vIRKfAinUP1CTx5FuMlNz2K5SvX4YMX7kBuTlZY3HJL8N5Dz8cp/xtSfZ6jfEF+vkq+tQXeiUcNNj435FP7DDx5XuBG8bVCbpEPPaGLOHbt3N44T48Cr6HPen6cBEiABEiABEiABEiABEiABEggVgLaCzz5w7wUWfJMuNBlCSGY8ny2PXfvbPyrlBhSVt1wSehiB79xkcNHYrXR/EX/GKugzj7p8HrrILeGyi2Vk3/+A/v17YkjBvdHXk42Vqxeh/c+myxu1CzB0/deWX3JRawCTwYht6W++u5XGC3EzsH9eyFfSKP7n3zbOD/ucyEhcsWNrLEKvNDFC1L0yDPrlixfjTHPvGPImok/zsBnr96DtkKgyfPfdvaUlJZj0HFXGtsvcwWDmmJEtgndQnvc8IHGza7yPSnLHnruPVx74YnGbbfykVs+5TbO/7vyTOOmWLntVW7HDV3yIW8JlRJICiopwrp2ahsRm0gFnpREl9/yJH78dQ7kDaUHiotFZE3lhRw//fqnsX3zMCGHH7l92yUT8qZeebvti2ILc1OxpVZuZ5Yr01av22AI5K6C4bwFy4xbXWW8cquzfHYWjxRhchvxNWKrrdwuLVe4fTjuB+PcN3nTb8/ddmnwa4MUiPLMPSmgvhPytfZ5gjUFXkiaSgF8tLgkRUpRKZ1vE4wPH7yvuA34DEhRLM80POWYbeJMSlM5P6b8MsdYKSkFXqRz6C6xRfXn3/7E9x89Xv15KM9wlKs15aq/seKm5cfuuEzcvhx+7qScL3LLslxpt1rE8/R9Vxnbs9/6eJJxVuNH46cYW9nl1lq5Xf7QE64xYpPCW17M8vOvc8NuoZW3H0upLNvIFbJVgvMr4vNL3nr8+hPyDMFuFHgNzjS+QAIkQAIkQAIkQAIkQAIkQAIkECsB7QWeXBWUv7GgTn5ym+b4Nx+oFgfy4P7QI7cYylVy8vB/+QN96NbLhgohhY+8kfaTr38U4mkl5MokuaJsP7G98lxxxpZcORZ64hF4cpzX3v9ayJwpxplfUrTI1YHyTLX2bVoYQ8Qq8GRbebOmvLijSEjHHuLSgxsvOxWZQniOuv5hyG2NUtLUdZlHTT5ym6XcbilXj4WEXM2PyzPZnhn7mSFI5ZZjuXpLXpwgt5SGHnkZwaPiEojvfp4JKQU7iVVncnXjUHFTrXykFL1QxCRZ9+7ZBfKyj0jYRCrw5BhSzMpz32RdpdQtKCoW59mliTMC22CEuPjgxJGDqrd2/jl/qSFx5erCs086wjhDUPKSMmry1FmGBJLiadjAfuLW3+OMC1XkU1888rZceQOucaurqHP3Lh1woWAQ6WUpoRuSDz2oD568+4odpnDtLbQyzpdF/RcLcStzl9tXpaS85Kyjq29ZleJUrjzcLG5RlkLw3FOGiwtPlmKxuPxEXpoS6Rwa+8E3OxV4R4qVs/+sWlfnp5y8/Vdu55Ufv/3h14QUXW4IeHkz8+XiNuUxT7+DD0XNhotzAqUMlCtC5W3FgUDAkPlSwErZOvG9hw3JKh8pXp8XsnShyEFu45bz/tJzjsGAPttW3XIFXkNf/fhxEiABEiABEiABEiABEiABEiCBWAloL/BiBcd2JEACJEACJEACJEACJEACJEACJEACJEACJGAHAQo8OyhzDBIgARIgARIgARIgARIgARIgARIgARIgARKIkQAFXozg2IwESIAESIAESIAESIAESIAESIAESIAESIAE7CBAgWcHZY5BAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAjESoMCLERybkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAdBCjw7KDMMUiABEiABEiABEiABEiABEiABEiABEiABEggRgIUeDGCYzMSIAESIAESIAESIAESIAESIAESIAESIAESsIMABZ4dlDkGCZAACZAACZAACZAACZAACZAACZAACZAACcRIgAIvRnBsRgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJ2EKDAs4MyxyABEiABEiABEiABEiABEiABEiABEiABEiCBGAlQ4MUIjs1IgARIgARIgARIgARIgARIgARIgARIgARIwA4CFHh2UOYYJEACJEACJEACJEACJEACJEACJEACJEACJBAjAQq8GMGxGQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnYQYACzw7KHIMESIAESIAESIAESIAESIAESIAESIAESIAEYiRAgRcjODYjARIgARIgARIgARIgARIgARIgARIgARIgATsIUODZQZljkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkECMBCjwYgTHZiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgBwEKPDsocwwSIAESIAESIAESIAESIAESIAESIAESIAESiJEABV6M4NiMBEiABEiABEiABEiABEiABEiABEiABEiABOwgQIFnB2WOQQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIxEki4wFuzqSzG0NksRCAzPQkpXi8KS32EQgLKEGicnYqyyiqUVwaUiYmBkECrJhnI31KGQJAsSEANAvJrZUZaErYUV6Kswq9GUIxCewKZYk6mJIvvLUv4vaX2k0EhAHni62Wlz49Sfq1UqCoMpWXjdGwsrICf31xyMtRBoE3TDFO5UOCZijMxnVHgJYY7R62fAAUeZ4iKBCjwVKyK3jFR4Oldf1Wzp8BTtTJ6x0WBp3f9Vc2eAk/VyqgRFwWeGnVQKgoKPKXKwWD+I0CBx6mgIgEKPBWrondMFHh611/V7CnwVK2M3nFR4Oldf1Wzp8BTtTJqxEWBp0YdlIqCAk+pcjAYCjzOAYUJUOApXBxNQ6PA07TwiqdNgad4gTQNjwJP08IrnjYFnuIFSnB4FHgJLoCKw1PgqVgVxsQVeJwDKhKgwFOxKnrHRIGnd/1VzZ4CT9XK6B0XBZ7e9Vc1ewo8VSujRlwUeGrUQakoKPCUKgeD+Y8ABR6ngooEKPBUrIreMVHg6V1/VbOnwFO1MnrHRYGnd/1VzZ4CT9XKqBEXBZ4adVAqCgo8pcrBYCjwOAcUJkCBp3BxNA2NAk/TwiueNgWe4gXSNDwKPE0Lr3jaFHiKFyjB4VHgJbgAKg5PgadiVRgTV+BxDqhIgAJPxaroHRMFnt71VzV7CjxVK6N3XBR4etdf1ewp8FStjBpxUeCpUQeloqDAU6ocDOY/AhR4nAoqEqDAU7EqesdEgad3/VXNngJP1croHRcFnt71VzV7CjxVK6NGXBR4atRBqSgo8JQqB4OhwOMcUJgABZ7CxdE0NAo8TQuveNoUeIoXSNPwKPA0LbziaVPgKV6gBIdHgZfgAqg4PAWeilVhTFyBxzmgIgEKPBWrondMFHh611/V7CnwVK2M3nFR4Oldf1Wzp8BTtTJqxEWBp0YdlIqCAk+pcjCY/whQ4HEqqEiAAk/FqugdEwWe3vVXNXsKPFUro3dcFHh611/V7CnwVK2MGnFR4KlRB6WioMBTqhwMhgKPc0BhAhR4ChdH09Ao8DQtvOJpU+ApXiBNw6PA07TwiqdNgad4gRIcHgVeggug4vAUeCpWhTFxBR7ngIoEKPBUrIreMVHg6V1/VbOnwFO1MnrHRYGnd/1VzZ4CT9XKqBEXBZ4adVAqCgo8pcrBYP4jQIHHqaAiAQo8Fauid0wUeHrXX9XsKfBUrYzecVHg6V1/VbOnwFO1MmrERYGnRh2UioICT6lyMBgKPM4BhQlQ4ClcHE1Do8DTtPCKp02Bp3iBNA2PAk/TwiueNgWe4gVKcHgUeAkugIrDU+CpWBXGxBV4nAMqEqDAU7EqesdEgad3/VXNngJP1croHRcFnt71VzV7CjxVK6NGXBR4atRBqSgo8JQqB4P5jwAFHqeCigQo8FSsit4xUeDpXX9Vs6fAU7UyesdFgad3/VXNngJP1cqoERcFnhp1UCoKCjylysFgKPA4BxQmQIGncHE0DY0CT9PCK542BZ7iBdI0PAo8TQuveNoUeIoXKMHhUeAluAAqDk+Bp2JVGBNX4HEOqEiAAk/FqugdEwWe3vVXNXsKPFUro3dcFHh611/V7CnwVK2MGnFR4KlRB6WioMBTqhwM5j8CFHicCioSoMBTsSp6x0SBp3f9Vc2eAk/VyugdFwWe3vVXNXsKPFUro0ZcSgu8zQVbMfreF7BuwxZ8MfbeamInX3wXFixeAXg8xp/lZGfix0+fNP73mk1lapB1cBQUeA4unotDp8BzcXEdnBoFnoOL59LQKfBcWliHp0WB5/ACujR8CjyXFtbhaVHgObyAFoevrMArKS3HKULUDdxvL0yZPidM4B15xmg8cdfl6Nqp7Q54KPDinzEUePEzZA/mE6DAM58pe4yfAAVe/AzZg7kEKPDM5cnezCFAgWcOR/ZiLgEKPHN5sjdzCFDgmcPRrb0oK/BKy8qxcXOh8c8dj7weJvAGHnsl3n/hdrRq3oQCz4KZSYFnAVR2GTcBCry4EbIDCwhQ4FkAlV3GRYACLy58bGwRAQo8i8Cy27gIUODFhY+NLSJAgWcRWJd0q6zAC/H9Y+6iHQTe3sNG4eD+vTBr3mI0a5KLq0Ydj4MH9DaacAVe/DOTAi9+huzBfAIUeOYzZY/xE6DAi58hezCXAAWeuTzZmzkEKPDM4chezCVAgWcuT/ZmDgEKPHM4urUXx5VJw/gAACAASURBVAm8QCCIWx98BYcP7o/9+vbAlGlzMPq+FzDujfuNFXl+8XE+8RGQRwvK0wWJMj6ObG0uAa+YmMFgEPwMN5cre4uPQJLXw7934kPI1iYTkF8r5d/jAfn1kl8wTabL7mIlIL+v3DYvY+2B7UjAfAL83tJ8puwxfgL83jJ+hm7uQc4PMx+P+AHb1L+a61qBVzvgc65+AMcdORAjhuyH9QXlZuajZV8ZaUlI8XhRVO7TMn8mrSaB3MxUlPuqUOELqBkgo9KSQPPcdGwqLAdnpZblVzLp3MwUpKcmobDUh/JKv5IxMij9CGSIOZmSJL63LOP3lvpVX92Mc8T3lr4qP8r4tVLdImkYWfOcNGzeWgm/uVpFQ5LuTLllXrqpiVku8ErLKrB4+b/o3aNLdeCnX3Yvzjh+GA4b1I9baE0oJ7fQmgCRXZhOgFtoTUfKDmsRCAQD+Ld8I5qm5CArObK/HOvbQrvRtxo/F3+GymAZBmQdiQ5pu5O5ywiUFFZg+qcLsGVtCXoMbI8e+7dPeIbcQpvwEjCAOghwCy2nhYoEuIVWxaowJm6h5Ryoj4DjttAWFBZj6MnX4Ym7L8P+fffAT7/+ievvfh7j33wATRvnUOCZMN8p8EyAyC5MJ0CBZzpSdliDwPrKAoxZ9h5WleUjyZOEc9odjsOa7dMgo50JvKKqjXhi/aUoD5QYfYhNjbi45aNom9qtwT75gjMIBPxBvHjlN9iworA64KOu2Bd7De2U0AQo8BKKn4PvhAAFHqeGigQo8FSsCmOiwOMccKTA+/anmbjurucgD3CRS5tTUpLRqX0rfPrqPZjyyxw8/Nx7yN9UgLatmuGGS0/BgD49jDx5iUX8E54CL36G7MF8AhR45jNlj9sJPLPic/yweU71H0iJ99IeV6NRcma9mHYm8GaWfItPNj8e1vagRsfj8Lyzid0lBNYt3YKXrpoYlk3nPq1w2p0DE5ohBV5C8XNwCjzOAQcRoMBzULE0CpUCT6Nix5Cq8ivwos2JAi9aYju+T4EXP0P2YD4BCjzzmbLH7QSu+vtZrBbbZ2s+N3c+FXvndo1J4C0un4WxG24Na3t048uwb/bhxO4SAsWby/D42eOMy3VCz97DOmPE5f0SmiEFXkLxc3AKPM4BBxGgwHNQsTQKlQJPo2LHkCoFXgzQ3N6EAs/tFXZmfhR4zqybU6KevGk2nlv5RXW4u2S0wpjdzodXXOhT31PfGXjjCp7D9K3jjea7ZvTFaU1vQbInxSlIGGcEBH7/cjEmvjIbgaoAWuySJ1bfHYzsJhkRtLTuFQo869iy59gJcAtt7OzY0joCFHjWsWXPsROgwIudnQ4tKfB0qHKUOVLgRQmMr9tCgALPFsxaD/JLwV/4efNfaJ3WBEe13A+5yVkN8qhP4MnGW/2bxSUW5Wia3KbBvviCMwmUF1eiaGMZmnXIgdfrSXgSFHgJLwEDqIMABR6nhYoEKPBUrApjosDjHKiPAAUe58cOBCjwOClUJECBp2JVGFNDAi+RhHzwYZ13JVoG2iEVaYkMhWPbSIACz0bYHCpiAhR4EaPiizYSoMCzETaHipgABV7EqLR8kQJPy7LXnzQFHieFigQo8FSsCmNSVeAt9s7DKxkPogibkYUcnF8+Grv5e7FgGhCgwNOgyA5MkQLPgUXTIGQKPA2K7MAUKfAcWDQbQ6bAsxG2U4aiwHNKpfSKkwJPr3o7JVtVBd5dmZdgnWdlNcZWwQ64rfRZp2BVPs6qqirMmP07SktL0a/PvmiU3UiZmCnwlCkFA6lBgAIvMdOhosCHopVlyGyaiqy26YkJQuFRKfAULo7GoVHgaVz8CFKnwIsAkm6vUODpVnFn5EuB54w66RalqgLviqz/oUpsoQ09HnjwaMmHYiMtf4CLd476qnwY88QYLF660Ogqp1Eubr3+NrRo1iLerk1pT4FnCkZ2YjIBCjyTgUbQXdE/ZVg5KR/BwLaXm+2Zg9b7N46gpT6vUODpU2snZUqB56Rq2R8rBZ79zJUfkQJP+RJpGSAFnpZlVz5pVQXe22lPY2ryhGp+B/iG4bTKK5Tn6YQA//xrDh555uGwUIcPHYGT/neSEuFT4ClRhriCCAYCmPPuFCz7YQ4yGjfCXmccgtZ7doqrz0Q3psCzvwLLv1iP4rXl2wcWd+z0OKcDklISf9lOpDQWVpXjK99WyLu9R6bmoU2SuTe5U+BFWgm+ZycBCjw7aTtvLAo859XM8ogp8CxHzAFiIECBFwM0NrGcgKoCr0qsv/s5+Wv8nTwbXf09MMh3FFLEVRZ84idAgRc/Q/ZQP4EF43/DrDe+rX4pKS0FRz15MTLysh2LjgLP/tIt+XQdyvIrwgRez3M7wJvsDIG31F+B0cVr4EfQyCHbk4RHs9ugmdc8iUeBZ/+85IgNE6DAa5iRzm9Q4Olc/Z3kToHHSaEiAQo8FavCmFQVeE6uTKU/iFVFPsifMdvnpsKr2M+acgvtg0+OwaIlNbbQ3nA7WjRtrgR2rsBTogxxBfH9fe9h3ZxlYX0MHH0S2uzdJa5+E9mYAs9++lvF2Xcrvqmxhba32EI7wDlbaN8o34zPKgrCwF2T2QIHppgnsinw7J+XHLFhAhR4DTPS+Q0KPJ2rT4HH6juIAAWeg4qlUagUeOYWu7wqgAlLS1Dm23ZoU5OMJAzplIUkxSweL7Ewt+7sLZwAV+BxRphFwMmXWEysLMLzZRvDUNyW1Rp7JcsNteY8FHjmcGQv5hKgwDOXp9t6o8BzW0VNyIcr8EyAyC5MJ0CBZzpSdmgCAQo8EyDW6GJufjnm1dzyJT42sGMm2jQyb8uUuRGr1xtX4KlXk2gjUvkMvH/mr8Mf3y9CZk46Bhy+O/KaR3YDM1fgRTsL+L4vGMRDZesxw1dqwDg8NQcXZDQzFQwFnqk42ZlJBCjwTALp0m4o8Fxa2HjSosCLhx7bWkWAAs8qsuw3HgIUePHQ27Ht/A0VmLO+xqHr4pUDO2SifQ4FXqSkKfAiJcX3oiWw4u91ePOBSQgKsSKf7LwMXHDvUchq1PDt1hR40dLm+yECawM+cX+6B429yaZDocAzHSk7NIEABZ4JEF3cBQWei4sba2oUeLGSYzsrCVDgWUmXfcdKgAIvVnJ1t5NbZ79Ztn0LbeP0JAztrN4WWnOzNrc3CjxzebK37QTGj52OPyYvCkNy7CUHoeeAhm/IpcDjTFKRAAWeilVhTBR4nAP1EaDA4/zYgQAFHieFigQo8FSsCmOiwDN/DshLLFYU+pDqVfMSC/MzNrdHCjxzebK37QSmjp+Hye//EYbk3NuPQNsuDV/gQoHHmaQiAQo8FavCmCjwOAco8DgHoiJAgRcVLr5sEwEKPJtAc5ioCFDgRYWLL9tAgALPBsiaDlFR7sMHj03GP3+vNwjsP2IPHHpin4hoUOBFhMnylyoKilCybBVSG+cgu1N7y8dTfQAKPNUrpGd8FHh61j3SrLkCL1JSGr1HgadRsR2UKgWeg4qlUagUeBoV2yGpUuA5pFAODnPjmkKkZ6ciOyfy20Ap8BJf8JJVa7Hq428Q9PuNYHJyfGic/A+SdumF1AOPhyclNfFB2hwBBZ7NwDlcRAQo8CLCpO1LFHjaln7niVPgcVKoSIACT8WqWBfThq1rMGXBZ/CL/wzoPAwdm+5m3WBx9EyBFwE8ceB96pxlSNpSjIrenRFoEtmtlRH0zFfqIECBx2mhIgEKvMRXZeXHE1Dyz2ojkEDhBgS3bkJbz+9I9viQ3PtQZJx5V+KDtDkCCjybgXO4iAhQ4EWESduXKPC0LT0FHkvvLAIUeM6qVzzRFpRswOOTrkOZr8Toxitunrv0kHvRrnGXeLq1pC0FXsNYc176GmkzFhovBtNTUXDV/1DVqXXDDflGTAQo8GLCxkYWE6DAsxhwBN2HCbw1SxAMVFULPKSmI/veSfB4xeGjGj0UeBoV20GpUuA5qFgJCJUCLwHQVR+SK/BUr5Ce8VHg6VP3X5ZOxGd/vBiW8MDdjsHwXqcrB4ECr/6SJK3djCZ3vBH2UkXf3VA06gjlaumWgCjw3FJJd+VBgZf4etbcQhvYsBLZFcvQxLvMCMyT2xzZt32W+CBtjoACz2bgHC4iAhR4EWHS9iUKPG1Lv/PEKfA4KVQkQIGnYlWsiWnR+jl45ce7wzo/dp8L0b/zUGsGjKNXCjwKvDimjyVNKfAswcpO4yRAgRcnQJOahy6xSKrchORvHkGgaCM8WXlIP/U2JHfvb9IozumGAs85tdIpUgo8naodfa4UeNEzc30LCjzXl9iRCVLgObJsMQf92ayX8cuSCUb7nm3749T+VyE5KSXm/qxqSIHXMNmc58YhbfZS40VuoW2YV7xvUODFS5DtrSBAgWcF1fj6DFb5EFi/HJ7mHeFNTYuvM4e2psBzaOFcHjYFnssLHGd6FHhxAnRjcwo8N1bV+TlR4Dm/htFmIM/CqwpWoVm2uuelUeBFUFVeYhEBJPNeocAzjyV7Mo8ABZ55LNmTeQQo8MxjyZ7MI0CBZx5LN/ZEgefGqsaZEwVenADZ3BICFHiWYGWncRKgwIsTIJubToACz3Sk7NAEAhR4JkBkF6YToMAzHSk7NIEABZ4JEF3cBQWei4sba2oUeLGSYzsrCVDgWUmXfcdKgAIvVnJsZxUBCjyryLLfeAhQ4MVDj22tIkCBZxVZ9hsPAQq8eOi5vy0FnvtrHHWGFHhRI2MDGwhQ4NkAmUNETYACL2pkbGAxAaUFXoUPWR9NR9qMZahqnoOSEwbA303dLfIWl0qr7inwtCq3Y5KlwHNMqbQKlAJPq3JHnSwFXtTI3N+AAs/9NXZihhR4Tqya+2OmwHN/jZ2WocoCL/Pj6ciYMLsaaTA7A5vHnAakJjsNM+ONkgAFXpTA+LotBCjwbMHMQaIkQIEXJTDNXqfA06zgkaRLgRcJJb5jNwEKPLuJc7xICFDgRUKJ79hJQGWBl3Pfp0hZvj4MR8HoY+Dv0spORBwrAQQo8BIAnUM2SIACr0FEfCEBBCjwEgDdQUNS4DmoWHaFSoFnF2mOEw0BCrxoaPFduwhQ4NlFmuNESkBlgZf52e/IGD+zOhWuwIu0qs5/jwLP+TV0YwYUeG6sqvNzosBzfg2tzIACz0q6Du2bAs+hhXN52BR4Li+wQ9OjwHNo4VwctsoCD6Ez8H5bgqqWeTwDz8XzsHZqFHgaFdtBqVLgOahYGoVKgadRsWNIlQIvBmhub0KB5/YKOzM/Cjxn1i3eqKsCVfh57QQsKJiDLrm7Y3CbkUj2qnNeFgVevBVme7MJKC3wzE6W/TmGAAWeY0qlVaAUeFqV2zHJUuA5plQJCZQCLyHY1R6UAk/t+ugaHQWenpV/Z/EzmLr2m+rk9215CM7a7SplYFDgKVMKBvIfAQo8TgUVCVDgqVgVxkSBxzmgIgEKPBWrok5MFHjq1EKZSCjwlCkFA6lBgAJPz+kwevpZ2Fq5pTr5lKQ0PLr/+/B6vEoAocBTogwMotbXyoy0JGwprkRZhZ9sSEAJAhR4SpSBQdQiQIHHKaEiAQo8FauiTkwUeOrUQplIKPCUKQUDocDTfg7cPfNSrCtZVc2heUZr3NHvBWW4UOApUwoG8h8BrsDjVFCRAAWeilVhTBR4nAMqEqDAU7Eq6sREgadOLZSJhAJPmVIwEAo87efAkoJ5eGXhwyiq2IyslByc3/0G7Nq4lzJcKPCUKQUDocDjHFCYQKQCr9K31cgiNaWRwtkwNLcQoMBzSyXdlQcFnrvqaXY2FHhmE3VBfxR4LiiiC1PgFloXFjXClHwBH9aVrkTLjHZIFVtoVXoo8FSqBmORBLgCj/NARQINCbxgMIg1G35EUfEyI/yc7M5o0/xgeDweFdNhTC4hQIHnkkK6LA0KPJcV1OR0KPBMBuqG7ijw3FBF9+VAgee+mrohIwo8N1TRXTlQ4Lmrnm7JpiGBV1SyHKvX/xCWbtuWg5CT1cl0BEWlFfhz1SY0zkxFj3bNhCQ0fQh26BACFHgOKZRmYVLgaVbwKNOlwIsSmA6vU+DpUGXn5UiB57ya6RAxBZ4OVXZWjhR4zqqXLtE2JPDWb/wVm4vmh+FokrsnWjbtayqilRuL8OJ3c1FeWWX026NdU5w7eA9Tx2BnziFAgeecWukUKQWeTtWOPlcKvOiZub4FBZ7rS+zIBCnwHFk21wdNgef6EjsuQQo8x5VMi4AbEniVvkIsW/0FgoFtYs3jSUantiORlpprKp/3pi3EjKXrwvq8fmQ/tMzNNHUcduYMAhR4zqiTblFS4OlW8ejypcCLjpcWb1PgaVFmxyVJgee4kmkRMAWeFmU2LckFwUWY5v8F2Z5sHOoZhKbepqb1HeqIAs90pOzQBAINCTw5RHnFpupVeE1yeiA9zfzPj49+XYzpi9aEZXT1kX3QtgkvzTChzI7rggLPcSXTImAKPC3KHHOSFHgxo3NvQwo899bWyZlR4Dm5eu6NnQLPvbU1O7PF/iV4vOrp6m5zPDm4PeVmpHvSTR2KAs9UnOzMJAKRCDyThqq3mzWbi/H0xNmo9PmN97q3bYLzD9nTjqE5hoIEKPAULApDAgUeJ0F9BCjwOD92IECBx0mhIgEKPBWrwpgo8DgHIiXwbtUH+Nk/Lez1S5IvRM+k3SPtIqL3KPAiwsSXbCagisCTaReUlOOvVZuRl8VLLGyeBsoNR4GnXEkYkCBAgcdpQIHHORAVAQq8qHDxZZsIUODZBJrDREWAAi8qXFq/PME/EeOqvgpjcHPKDWjrbWMqFwo8U3GyM5MIqCTwTEqJ3biAAAWeC4rowhQo8FxYVBNT4go8E2G6pSsKPLdU0l15UOC5q55uyYYCzy2VtD6P8mAFnvW/gKX+ZcZgw5KG4OjkEaYPTIFnOlJ2aAIBCjwTILIL0wlQ4JmOlB2aQIACzwSILu6CAs/FxY01NQq8WMmxnZUEKPCspMu+YyVAgRcrOX3brQmsNS6xyPFYc2g+BZ6+c0vlzCnwVK6OvrFR4Olbe5Uzp8BTuTqJj40CL/E1UC4CCjzlSsKABAEKPE4DFQlQ4KlYFb1jUl3glZYFMWsOkJYaxF69vUhO0rteumRPgadLpZ2VJwWes+qlS7QUeLpUOrY8KfBi4+bqVhR4ri6vY5OjwHNs6VwdOAWeq8vryORUFnhFRUHc/0gQ8v/Lp0M7D6650ouUZEeijilov68Sy36fgI3L5iE9tym67X8Uspuaew5iTIFZ3IgCz2LA7D4mAhR4MWFjI4sJUOBZDNjh3VPgObyAVoRPgWcFVfYZLwEKvHgJsr0VBCjwrKDKPuMhoLLAGz8hgK++2SbvQs8lF3jRc3dPPCk7qu2y3yZg9byp1TGnZjRC3xOuRlJyiqPyiDZYCrxoifF9OwhQ4NlBmWNES4ACL1pier1PgadXvSPKlgIvIkx8yWYCFHg2A+dwERGgwIsIE1+ykYDKAm/itwF8Pj5c4I0614u99tRH4P3x2TMo2bwubEb0HjEKOS062DhL7B+KAs9+5hyxYQIUeA0z4hv2E6DAs5+5k0akwHNStWyKlQLPJtAcJioCFHhR4bLsZf/a1fDNmoHkjp2R3HNPy8ZxSscUeGpUKmljMTJnrUIQHpT1bgt/C2suiFAj2/qjUFngFRYG8cCj27fQthdbaK/VbAstV+D5nPBpxBg1IUCBp0mhHZYmBZ7DCmZzuBR4NgN3wnAUeE6okn4xUuAlvua+Gb+h+PEHAL/fCCb9iJHIOPO8xAeWwAgo8BII/7+hkwrKkPf2b/BUBYw/CSZ5UHDavvA3zkx8cAmIQGWBJ3HISyxm/AFkZuh5iYVxBt6v47Hxn795Bl4CPj84JAnUJECBx/mgIgEKPBWrok5MFHjq1EKZSCjwlCkFA6lBgAIv8dOh+K5b4Pt73vZAPB7kvfoePOnpiQ8uQRFQ4CUIfI1h08XKu+wfl4QFUrJ/Z5T165j44BIQgeoCLwFIOKQCBLiFVoEiMIQdCFDgcVKoSIACT8WqqBMTBZ46tVAmEgo8ZUrBQCjwlJoDRbdeD/+SReEC77X34UlLUypOO4OhwLOTdt1jpS3OR6Ov/gr7YNFhu6Oye6vEB5eACCjwEgCdQzZIgAKvQUR8IQEEKPASAJ1DNkiAAq9BRFq/QIGndfnrTp4Cj5NCRQJcgZf4qvhmz0Txw/dWb6FNG3EMMk87J/GBJTACCrwEwg8NHQii0bcLkfb3WuNPKrq1wNbDewBefS5GqFkFCjwF5iRD2IEABR4nhYoEKPBUrApjosDjHKiPAAUe58eO32SlJyHF60VhKQ8a5vRQhwAFnhq14CUW4XWgwFNjXsoovEVl4r89COTou6VbcqDAU2dOMpLtBCjwOBtUJECBp2JVGBMFHucABR7nQFQEuAIvKlx82SYCFHg2geYwURGgwIsK185fXr/RkG9o2dSkDvXthgJP39qrnDkFnsrV0Tc2Cjx9a69y5hR4Klcn8bFxBV7ia6BcBFliBV6SWIFXxBV4ytVG54Ao8HSuvrq5U+DFWZtAAMkPvwrvlN+NjgKH7oeqq84Uy+m8cXasb3MKPH1rr3LmFHgqV0ff2Cjw9K29yplT4KlcncTHRoGX+BooFwEFnnIlYUCCAAUep4GKBCjw4quK96cZSH7gpbBOqkaPQuCgvvF1rHFrCjyNi69w6hR4ChdH49Ao8DQuvsKpU+ApXBwFQqPAU6AIqoVAgadaRRiPJECBx3mgIgEKvPiqkvzC+/B+MTmsE//xh8F/zrHxdaxxawo8jYuvcOoUeAoXR+PQKPA0Lr7CqVPgKVwcBUKjwFOgCKqFQIGnWkUYDwUe54CqBCjw4quM5991SL70Lniq/EZHwbRUVD19K4JtWsTXscatzRR44oJfLFvnQ/5WP5o1SkLXVim6Xu6r8YwyJ3UKPHM4shdzCVDgmcuTvZlDgALPHI5u7YUCz62VjSMvCrw44LGpZQS4As8ytOw4DgIUeHHA+6+pZ+lKYxWeR/yn6phDgU7t4u9U4x7MFHh/LK/AP/lV1TR3aZ6MPp3TNKZbf+oVCGKruIvF7/EgPRhETlDOaj6SAAUe54GKBCjwVKwKY6LA4xyojwAFHufHDgQo8DgpVCRAgadiVRgTBR7ngGoEzBR44/8oQ4UvUJ1ikrhbZGTfLAg/xacWgaAQduuTxCrSGn/eSPxLtpB4fCjwOAfUJECBp2ZddI+KAk/3GVB//hR4nB8UeJwDjiBAgeeIMmkXJAWediVXPmEzBd6kuWXYWrpd4DXK9GLonhnKM0hEgJVC3W2qdXmyXKvYJECBJ+vBFXiJmJUcsyECFHgNEeLHE0GAAi8R1J0zJgWec2plW6RcgWcbag4UBQEKvChg8VXbCFDg2YaaA0VIwEyBt7HIj+lLKlEpVuGlpngxoGsqmuWIZWZ8diAgV+DlCzTbdSfAFXjbMRX7CyFXcGZ4cjl7SEAZAhR4ypSCgdQgQIHH6VAfAQo8zo8dCFDgcVKoSIACT8WqMCYKPM4B1QiYKfBkbn5hpLaW+ZGd7kVyEleT1VdvnoG3I51AMIBnZr+DqWtmGFuv+7Xojav2OUtchlJruaJqn0iMRwsCFHhalNlxSVLgOa5ktgZMgWcrbmcMRoHnjDrpFiUFnm4Vd0a+FHjOqJNOUZot8HRix1zNJ/DLmll4bOZYeL3iQg95wYc/iKv3ORv7tdnb/MHYIwlESYACL0pgfN0WAhR4tmB27CAUeI4tnXWBU+BZx5Y9x06AAi92dmxpHQEKPOvYsufYCFDgxcaNrawh8Nb8L/DF0u/CBN7ILofi9B4jrRmQvZJAFAQo8KKAxVdtI0CBZxtqRw6ktMDbXLAVo+99Aes2bMEXY++tBrxqTT5uffBVLFyyEm1aNcMtV56BPnt2Mz6+ZlOZIwuhUtAUeCpVg7GECFDgcS6oSIACT8Wq6B0TBZ7e9Vct+zXF+bjxp4fgC/iMFXhJSMaYg69H2+yWqoXKeDQkQIGnYdEdkDIFngOKlMAQlRV4JaXlOOXiuzBwv70wZfqcMIF31pX345AD++D0Y4di2oy/hMx7BZPefwQpyUkUeCZMJgo8EyCyC9MJUOCZjtSWDgMVwIaPgOIZHiQ3D6L5CUFkdnPPOVoUeLZMIw4SBQEKvChg8VVbCKwoWoNJK38UAi+IgW33R9e8jraMy0FIoCECFHgNEeLHE0EgUoEXqKpE+eLp8BesR2qb3ZDWYU8Yvynh42oCygq80rJybNxcaPxzxyOvVwu8TVuKcPipN+CXL58Rhylvuwnt+FG344ZLTsG+e3enwDNhulLgmQCRXZhOgALPdKS2dLjhY2DLhO1DJWV70GlMAN5Ud3yDoavAkzduLv53JbaWlqBbuw7Iycq2ZT5xkIYJUOA1zIhv2E8gMy1J/KLdi8ISn/2Dc0QS2AkBCjxODRUJRCrwtv78Dnz5y6tTyOgxEBndD1QxJcZkIgFlBV4oxz/mLgoTeH/MXYy7Hn0dn712TzWGa+98Fv379MCJRw2iwDNhclDgmQCRXZhOgALPdKS2dLjiPqBi+/cWxpjtRweR0YUCz5YCWDTIZz9/j7//WWb0npqSglMOPRxtmrWwaDR2Gw0BCrxoaPFduwhQ4NlFmuNEQ4ACLxpafNcuApEIvGB5MbZ89URYSEmNmiJ36EV2hclxEkTAcQJv2ox5ePLlj/He87dXI/u/Ma9g187tcOYJh4nVAFUJQumeYVNSPPCK5bcVlQH3JMVMHE8gQ/z23ucPoKoq6PhcdEpg7UdBrP/CX51yciMPejzmjWoFnj+4EgEUINnTCR40UgpfdmYySsqqIBakafPkF2zBUx9/EJbvHp274KTBQ7RhYGWiBfn54sD/JOQ0axrTMOlypVOSB+UVfvE10zkT0x+swDLfq9ji/w1pnjbomno+srydYmLARuoRSEn2IEncRFvO7y3VK47GwB5ZzQAAIABJREFUEcmvl37xvaWP31tqPAviS71i6QYUfPgHPFUB5By3F9J3bx1fh6J1dob43rK8/u8tg8EA1nw0BkGxjTb0pLXpiuYDT4t7fHagNoFG4mcPMx+P2FZj6neLtVfgzZq3GLc/PDbsTLxr7ngG+/fdA8ePGIitZVyaH29BU5O8Yvu8EHhV23/ojrdPtieBeAmkpyShSgq8gKlfYuINi+0bICDPwFv9rtBv04NIbQW0PcWL7N0iX31XGfgW/uCS/0ZJRpp3hPgFg+hIkSc7PUV8k+WDTrPSEHgffRgu8LpIgXeoIlVxZhh+XxW+fPY5LJkx00hgz0EDMey8c6JOJj1VCjyvECVS4DnnF3H/VL6JNb4vq/NN8eahb8azQtpvOy6Fj7MJyDnpFQKvwsfvLZ1dSXdFL7+39IvvK530tdJdFXB2Nr41BVh5xhtiq8l/C4jEMQHt3zgTqR2axJVYVnoyysQv4QINaJWyVX9j07TPAL8PSdlN0GzgSUjJbR7X2GysPoFGGSmmBmm5wNtSuBVDTrwWP3/+NDLSU43gjzjtBtx30yjsvUc3bqE1oZzcQmsCRHZhOgFuoTUdqfIdBj1b4U1+LzzOYBfx28ZDlIld1zPwPpryLRavWmHUgVtozZmOC6dOxeSxr4Z1NuLqa9C+R8+oBnDqFtqF/ptQhm1zKvR09z6EdE/bqPLny2oS4BZaNeuie1RO2UJbJcR3/srNaNY2D6niF4d81CBQMm4uip6YHBZMo/P2R/Yp/eIKMJIttKEBgr4K+Eu2ICmnBTxeb1zjsrEzCDhuC63Eet41D6LfXt0x6rQR+Pr7X40ttV+//SCSxG/31mwqcwZ5haOkwFO4OBqHRoGnY/FL4Ul5OyzxYFDcXlg1TBkYugo8XmJh/hSc8tYbmD9lSljH/Y89Fn2OODKqwZwq8FYH3saG4PjqXJM9eejpfYor8KKqvrovU+CpWxudI3OCwFu9ZAPevncCCvO3Ij0rDSffOBTd9umgc9mUyb182jJsuW1cWDy5o4chc8juccUYjcCLayA2diQBZQXetz/NxHV3PQd5sJBPbOVMSUlGp/at8Omr92D1uo24+f6XsHDpKrRv0wJ3XHs2eu62i1EACrz45yEFXvwM2YP5BCjwzGfqiB6TfxBb+hf/F2qyWH13hPh7QZ0ttLoKPEfMHYcFuXnNGnx4950IVG3bipOWkYkTbr8DjZpGdxaeUwWePANvdXAsioIzkOJpjQ7ec5GBbd/b8XE+AQo859fQjRk4QeC9cP2nWDl/bTX+Rk2zMPqNs9xYDsflFBTbrwsfmoSySX8bsacf0AV5tx8pVsJFflRMXUlT4DluKtgasLICL1YKFHixktvejgIvfobswXwCFHjmM3VGj+J0Oc+/QuIVIRiQv3FW6xILCjxnzCKnRJn/z3LMnTxZnBXmRa8hQ9G0XbuoQ3eqwIs6UTZwFAEKPEeVS5tgnSDw7j7xZZSXbL+oQBZn9JtnoVGTLG3qpHqi/vXie1Tx7WpyqxxTQqXAMwWjazuhwHNtaWNPjAIvdnZsaR0BCjzr2LLn2AlQ4MXOji2tIUCBZw1X9hofAQq8+PixtTUEnCDwvn5lGn7+ZHY1gJ5ildepNx9mDRD2qgQBCjwlyqBsEBR4ypYmcYFR4CWOPUfeOQEKPM4OFQlQ4KlYFb1josDTu/6qZm+FwCsp34rfF05FekoG+uy6H1KTt11ux4cEIiXgBIEXENs0fxs/D4tni6OjurXEAcf2RkpqcqQp8j0HEqDAc2DRbAyZAs9G2E4ZigLPKZXSK04KPL3q7ZRsKfCcUil94qTA06fWTsrUbIFXWLwZt469AgXi/8unY8uuuPm0MchIzXASFsaaYAJOEHgJRsThE0CAAi8B0B00JAWeg4plV6gUeHaR5jjREKDA20arPFAGX9CHRknmnLMRTQ347o4EKPA4K1QjQIGnWkUYjyRgtsAb98sH+HDK2DC4l4y8EQN6DCRwEoiYAAVexKj4oo0EKPBshO3AoSjwHFg0q0OmwLOaMPuPhQAFHjCr5A8sK19i4GuR0hIDsg9AipfbKGKZT2a1ocAziyT7MYsABZ5ZJMP7yf9nHlbM/h6pWY3Qtd9wZOW1sGYgl/ZKgefSwjo8LQo8hxfQpeFT4Lm0sCalRYFnEkg3dUOB56ZquicX3QXeRl8+phT9EFbQ3ll7o2t6N/cU2YGZUOA5sGguD5kCz/wCb1wxHz+9c091x+nZeRh6wSNITuN2zUhpmy3wCoo34baxV9bYQtsFt5z2ENJT0yMNie8pTODnpQsxcf4ceD1eHNajN/brbM33OhR4Ck8CjUKbtXo5xv46GfklRTik65648bAjUFhcBb84/5APCdQmQIHHObEDAQo8TgoVCegu8BaW/Y15pXPDStM+rQP2zR6gYrm0iYkCT5tSOyZRCjzzSzVrwiv4Z9Z3YR3vf+INaNllL/MHc2mPZgs8iYmXWLhzsixevxZP/jAhLLkrBx+Bri1amZ4wBZ7pSNlhlASKK8pw5jtPobLKV93y8kFDMXy3/hR4UbLU5XUKPF0qHUWeFHhRwOKrthHQXeBVBCowseBrVAYrDeYe8Z/BuUPQOLmxbTXgQDsSoMDjrFCNAAWe+RVZOPUzzP/xg7CODznvAeS26GD+YC7t0QqB51JU2qf1+ZwZ+HZB+C8sR/baB0N372U6Gwo805GywygJzFi1FHdMeC+s1YFddsUtQ06kwIuSpS6vU+DpUuko8qTAiwIWX7WNgO4CT4Le6t+KxWULURWsQuf0LmiW0tw2/hyobgIUeJwZqhGgwDO/Ir7KcvzywRhsWrXQ6HzX/Uai56CTzR/IxT1S4Lm4uCantih/LZ76PnwF3lWHHIEuzbkCz2TU0XUXDKL8t1Wo+HMtktvmInNQF3gyU6Lrg2/vQIAr8DgpoiVAgRctMQ3ep8DToMgOTJECz4FF0yBkCjwNiuywFCnwrCtY0YZ/kZrZCOlZudYN4tKeKfBcWliL0vppyUJM+ptn4FmEN6Zuy75fiuJPtq+MTOnaDHlXHhhTX2wUToBn4HFGREOAAi8aWpq8S4GnSaEdliYFnsMKpkm4FHiaFNpBaVLgOahYGoVKgadRsR2UKrfQRl6sLQ9PQdWKLWENmt57OLw59lwcU/rXCmz+5g94M1LRdOQApLVtGnnwDnuTt9A6rGA2h0uBZzNwJwxHgeeEKukXIwWefjV3QsYUeE6okl4xUuDpVW+nZEuB55RK6RUnBV7k9S585TdUzl5T3cCTnoym9x4BT2pS5J3E+GbZ0rVYdu3LgNjGK5+k7Ax0eeICpDRz52poCrwYJ4omzSjwNCl0NGlS4EVDi+/aRYACzy7SHCcaAhR40dDiu3YQoMCzgzLHiJYABV60xPi+HQQo8CKn7M8vRuHzv8C/oQQQ0q7RyXshvV/7yDuI4811r3+LTZ9MC+uh3XXHIvegPeLoVd2mFHjq1kaFyCjwVKiCYjFQ4ClWEIZjEKDA40RQkQAFnopV0TsmCjy9669q9hR4qlZG77go8KKrf9AfgH9tEZKaZcGTbt8FFnLr7NpnvwwLtuOdpyN7r847TaCoFCgo9aBxVhCNMqLLM9FvU+AlugJqj0+Bp3Z9EhIdBV5CsHPQBghQ4HGKqEiAAk/Fquw8psoNRfBtLILchJPWKg8pjbOdlUAE0VLgRQDJQa8EV/nh/6ACWFMFT+8UeE9KhyfN46AMtoVKgee4kmkRMAWeM8oc8FVh5d3vomTOciPgJsP7ovWFw3ca/MqNHixZ6zU+Lr9a7tY2gDZNtm2/dcJDgeeEKiUuRgq8xLFXdmQKPGVLo3VgFHhal1/Z5O0WeIE1y1H+5esIrF2J5B59kXbUOeK34A771XKCqllVWIqyf/LDRs/s1hpJmWkJisiaYSnwrOGakF79QNXNWxEsClQP7z00DUnH23NovJk5U+CZSZN9mUWAAs8skvb0U7lms7jEIgXJjRvVO+DPC5JR6dsu7FJTPDiwe5U9QZowCgWeCRBd3AUFnouLG2tqFHixkmM7KwlQ4FlJl33HSqAhgbdGCLeZv09CaVkxunbrjT59BsPj2fZb4aifgB/F91+M4NaC6qapBwwXEu/sqLvSsUHF6s2oFKvvaj5prfOQ2iLPVTgo8NxTzuCqKlTdJ86bqvnskoSUG523cpQCzz3z0k2ZUOC5qZrbc6kt8NJSgjig+/ZfhKieNQWe6hVKbHwUeInlr+ToFHhKlkX7oCjwtJ8CSgKoT+BJaffpx0/DX+Wrjr3vvkPRo8eAmHIJ5K9GyaNXh7VNat4Gmdc+HlN/ujWqcwXermIFXgZX4Ok2FxyTb10r8AaLFXgncgWeY2rIQJUmQIGndHliDq7mFlrZSbc2AbRvyi20MQNlQ6UIUOApVQ41gqHAU6MOjCKcAAUeZ4SKBOoTeCtXLsAPkz8MC7tN2y4YMvTU2FLhCrzYuNVoJc/Aq9y4VfxJkGfgxU0z/g4qAkEsqqyAJxhE9/R0JHucd7Zb/BTq78E4A+/9cmCtn2fgWQ2b/WtHgALPvSXnJRbura3umVHg6T4D6sifAo+TQkUCFHgqVoUx2bkCT9I2zsD7/FXI1XjyDLzUkefCm+a81TicOdYRcMoW2hIhpN/YvAVb/WKZmXiapSTj9LwmSPVS4lk3OxLXM7fQJo49R945AQo8zg4VCXALrYpVUScmCjx1aqFMJBR4ypSCgdQgQIHH6aAiAVvPwFMRAGNSjkDCBN5mP5LfKoV3oQ/BbsnwnZklrgpM2imf30tL8P3W4rCPH5Wbi93FSjw+7iNAgee+mrohIwo8N1TRfTlQ4LmvpmZmRIFnJk2X9EWB55JCuiwNCjyXFdQl6TQk8FySJtNwEIFECbyUh4rgnb/9vMfgbimoHJ2zU3LTS0rwYzEFnoOmVlyhUuDFhY+NLSJAgWcRWHYbFwEKvLjwub4xBZ7rSxx9ghR40TNjC+sJUOBZz5gjRE+AAi96ZmxhLYFECbzUSzfDU1rjkHCxE7biuSZAWt1bYssCAYzdvLl6C21zsYX2tMZiCy3PwbN2giSodwq8BIHnsPUSoMDjBFGRAAWeilVRJyYKPHVqoUwkFHjKlIKB1CBAgcfpoCIBCjwVq6J3TIkSeNGuwJNV4iUW+sxVCjx9au2kTCnwnFQtfWKlwNOn1rFkSoEXCzWXt6HAc3mBHZoeBZ5DC+fysCnwXF5gB6aXKIEHeQbem+IMvEWRnYHnQLQMOQ4CFHhxwGNTywhQ4FmGlh3HQYACLw54GjSlwNOgyNGmSIEXLTG+bwcBCjw7KHOMaAlQ4EVLjO9bTSBhAs/qxNi/owlQ4Dm6fK4NngLPtaV1dGIUeI4un+XBU+BZjth5A1DgOa9mOkRMgadDlZ2XIwWe82rm9ogp8NxeYWfmR4HnzLq5PWoKPLdX2Jn5UeA5s252RU2BZxdpB41DgeegYmkUKgWeRsV2UKoUeA4qliahUuBpUmiHpUmB57CCaRIuBZ4mhXZYmhR4DiuYzeFS4NkM3AnDUeA5oUr6xUiBp1/NnZAxBZ4TqqRXjBR4etW7vmx/Xb0Gb/45F1srfTi8S2ecskePhMGhwEsYeg5cDwEKPE4PFQlQ4KlYFXViosBTpxbKREKBp0wpGEgNAhR4nA4qEqDAU7EqesdEgad3/UPZry0uwaVfT4A/EKwGclX/fhi8S8eEAKLASwh2DtoAAQo8ThEVCVDgqVgVdWKiwFOnFspEQoGnTCkYCAUe54DiBCjwFC+QhuFR4GlY9DpS/m75P3jytxlhHzlMrMK7pG+fhACiwEsIdg5Kgcc54EACFHgOLJqNIVPg2QjbKUNR4DmlUnrFyRV4etXbKdlS4DmlUvrESYGnT63ry5Qr8DgPSKBhAlyB1zAjvmE/AQo8+5k7aUQKPCdVy6ZYKfBsAs1hoiJAgRcVLr5sEwEKPJtAc5iICVDgRYzK9S8aZ+DN/QtbKyp4Bp7rq80EYyGgjcALBpG6fi085eWobN0GwbT0WHCxjU0EKPBsAu3QYSjwHFo4K8OmwLOSLvuOlQAFXqzk2M5KAhR4VtJl37EQoMCLhRrbWE2AW2itJsz+YyGghcAT8i7n9+lI3bDeQBRMTkHBfgfBn5MTCzK2sYEABZ4NkB08BAWeg4tnVegUeFaRZb/xEKDAi4ce21pFgALPKrLsN1YCFHixkmM7KwlQ4FlJl33HSkAHgZe8ZTPypv0Yhqi84y4o3mOvWLGxnUkECteuQ1p2FtIbNQrrkQLPJMAu7YYCz6WFjSctCrx46LGtVQQo8Kwiy37jIUCBFw89trWCAAWeFVTZZ7wEKPDiJcj2VhCgwLOCKvtsiEBlaSl+fnEsNi5dbrzac/gw9DxiaHUzCryGCOr9cQo8vetfZ/YUeJwUKhKgwFOxKoyJAo9zQDUCFHiqVYTxSAIUeJwHKhLQQeBBbqGdIbbQ5m/fQrtFbKENcAttwqbk3HET8PfE78LGP+LWG9CoRXPjzyjwElYaRwxMgeeIMtkbJAWevbw5WmQEKPAi48S37CVAgWcvb47WMAEKvIYZ8Q37CVDg2c+cIzZMQAuBJzHwEouGJ4ONb/z47EtY9/eisBEHnH0aOuyzbVszBZ6NxXDgUBR4Diya1SFT4FlNmP3HQoACLxZqbGM1AQo8qwmz/2gJUOBFS4zv20GAAs8OyhwjWgLaCLxowfB9Swms/vMvTH1pbPUYmU0a47CbrkFK+rbbgSnwLMXv+M4p8BxfQvMToMAznyl7jJ8ABV78DNmD+QQo8Mxnyh7jI0CBFx8/traGAAWeNVzZa3wEKPDi48fWsRNY+9cCLP/1d2TkNMKugw9GVtMm1Z1R4MXOVYeWFHg6VDnKHCnwogTG120hQIFnC2YOEiUBCrwogfF1ywlQ4FmOmAPEQIACLwZobGI5AQo8yxFzgBgIUODFAE2jJhR4GhU70lQp8CIlxffsJECBZydtZ4y1YPVfmDT3G6SnpGNEn6PRunFb2wO3S+BtyF+EVSt+g9/vQ+u2vdGufR/bc+WAziBAgeeMOukWJQWebhV3Rr4UeM6ok25RUuDpVvHo8qXAi46XFm9T4GlRZsclSYHnuJJZGvCy/CW4+d3rxLnMQWOc7LRsPHb2s8jJyLV03Nqd2yHwios3Ys7Md8OG7t5zOJo262JqrusqK7G5yod2qWnISU42te/6OgsEfFg3fywK/v0eqRnN0LLHuWjUfNtBznyiJ0CBFz0ztrCeAAWe9Yw5QvQEKPCiZ8YW1hOgwLOesZNHoMBzcvUsip0CzyKw7DYuAhR4ceFzXeO3f34dX8z4JCyvK464FgfsdrCtudoh8NaumYdli78Py6tVmz3Qpdtg03L9o6QYf5WUGP15xT8H5+ahfVqaaf3X19GGxR+I29her37Fk5SG3Ye9gaSUbFvGd9sgFHhuq6g78qHAc0cd3ZaFEwRe/qogvnolgNVLgth1Hy+Gn+dBVo7HbaVgPjUIUOBxOtRHgAKP82MHAhR4nBQqEqDAU7EqiYtpwuzxeO2HF8MCuPPE+9G9TQ9bg7JD4JVs3YDZf7wXlpeZK/CqxCrG9zbkY9taxm1P05QUDG+8/UBlK6Eun34rivP/CBtil/53olHLvlYO69q+KfBcW1pHJ0aB5+jyuTZ4Jwi8p6/0I3/l9r+hew304vir5K/a+LiVgFME3syCf/HBuj9R5KvAoWJXyLGt9oTXQ7ls9bykwLOasAP7p8BzYNE0CJkCT4MiR5GiT5wFN+bzuzF35Ryj1eF7jcA5g0ZF0YM5r9oh8GSk8gy8f8UZeFXGGXi9xBl4+5iTgOjFJwTe+wkUeBuWfCy20L5anY83OQ3dh3IFXqwFpsCLlRzbRUJg3vpCfLcsH2X+IAa0zcOgTi0iaQYKvIgw8SWbCagu8Iq3BPHguf4wKtl5HtzwWpLNpDicnQScIPA2VhTj6vnj4P/vKBvJ5/wO/XGIyce72MndKWNR4DmlUjbGSYFnI2wOFTEBCryIUWn14prNq8UPhhnIy7JntVhtuHYJPKuLmsgttNvPwJsszsBrzjPw4iw2BV6cANl8pwQ2lFTg6V+XiB/Ytr9yXI822Lt14wapUeA1iIgvJICA6gJPIuEKvARMjAQP6QSBN23LCjy9fGoYqf0ad8TlnQ5IMD33D0+B5/4aR50hBV7UyNjABgIUeDZA5hBRE3CLwJOJJ+oSi6ihs0G9BCjwOEGsIvDr6i0Yt2BNWPf92jbB0d1bNzgkBV6DiPhCAgg4QeDxDLwETIwED+kEgVfXCrwLOw7AwKadE0zP/cNT4Lm/xlFnSIEXNTI2sIEABZ4NkDlE1ATcJPCiTp4NlCRAgadkWVwRFFfguaKMTKIGAScIPBZMPwJOEHiyKjXPwBssts6e2LqXfsVKQMYUeAmArvqQFHiqV0jP+Jws8IKFGxCY+Q2CJYXw9hoEb8eeehbRhVlT4LmwqA5PiQLP4QVUPHyegad4gRheVAQo8KLCxZdtIuAUgWcTDg5TiwAFHqfEDgQo8DgpVCTgWIFXUYaqsTchWFxQjTX5hBvh6bC7ipgZU5QEKPCiBMbXLSdAgWc5Yg4QAwFuoY0BGptYToACz3LEHCAGAnYLvIAvgC1/rgcCQN4ezZGUkRxD1GxiFwEKPLtIO2gcCjwHFUujUJ0q8ALL/4T/k0fDKuXtPRhJQ87SqHruTZUCz721dWpmFHhOrZy746bAc3d9nZodBZ5TK+fuuO0UeP5KPxY9MxNla4oNqCmN09H9sn2QkpPmbsgOzo4Cz8HFsyp0CjyryLLfeAg4VuBt+Bf+N/4vLPWkA46Fd8DIeHCwrSIEKPAUKYRTwxDXeaavKUJSUQX8eekob9MI8HjiyoYCLy58bGwRAQo8i8Cy27gIUODFhY+NLSJgp8DbMns9lr/9V1gmbY7ojFaH7GJRduw2XgIUePESdGF7CjwXFtUFKTlV4En0/qmfIDD9C6MKnra7Ium4a+FJ4W+2XDAtQYHnhiomLofs+RuQuqGkOoCydrko69I4roAo8OLCx8YWEaDAswgsu42LAAVeXPjY2CICFHgWgXVJtxR4LimkmWlQ4JlJk32ZRcDJAk8yCJYWiX+2wtusrVlI2I8CBCjwFCiCU0MQq+8a/7IKVVVlWBL8C4WBLWid2hnZBw2CR/wn1ocCL1ZybGclAQo8K+my71gJUODFSo7trCRgp8DzV/ix8OkZKF+37ZeJ3EJrZWXN6ZsCzxyOruqFAs9V5XRNMk4XeK4phAqJBINICpQi4ElH0JuU0Igo8BKK3/GD503/F9NKxmET1hm5BJO86NhtCDq2PSDm3CjwYkbHhhYSoMCLHG6VrwqlpeXIbpQFrzd2mR/5iPq+SYGnb+1VztxOgSc58BILlWfDjrFR4DmrXrZES4FnC2YOEiUBCrwogSXg9ZWrijB77np07dwYPbo3syQCb6AMOWXzhcArR9DjRUlaZ1SktLRkrEg6pcCLhBLf2RmBQP5GTJ33lDB34v/E2XcBcfNbdk479O15dszQnCzwFvnX4l+xEnGPpHZo4c2JmQEbqkeAAi+ymqxZtR5/z12KgD+AjKx07NWvhxB5mZE15ltRE6DAixoZG9hAwG6BZ0NKHMJEAhR4JsJ0S1cUeG6ppLvyoMBTu54//LQSTzw7E0GxOk4+Jx+/u/GP2U922QKkVW2q7jYothpuzu4vDhdMzEo8CjyzK6xXf8FgAD/NeASBqgqxmnTbSpsmeV3Qe7eTYwbhVIH3WeVMfOubZ+SdBC/OSxuEXsntY+bAhmoRsEPgecRaVg+mCRveTKzQHiAAOGv1ml9Iux++mW7Iu9DTonUz9O7bXa1iuigaCjwXFdNFqVDg7byYcxf+hfWb8rH37r3QtHFTF1U98lQo8CJnpc2bFHjalNpRiVLgqV2uS6+ZhNVrtlYHmZqWhPdeG2n69p/GJb/DG6gMg1GQuSf8SYlZrUOBp/a8dEJ06zbOxcLlXyMQ8CEjvTH27HYisjJjX8HqRIFXGazCtaXviIWI234BIJ+O3ua4PmO4E0rIGCMgYLXA8wSXIi3pBiHvtp3jFEB/VAbvjCAydV4p2FyE36f+GRZQaloqBg7bV50gXRYJBZ7LCuqSdCjw6i7ki++/it/+nGF8MDU1FVeffTm6dezikqpHngYFXuSstHmTAk+bUjsqUQo8tctll8BL961BVvnyahhVSY1QmNkrYXAo8BKG3lUDV4kVeGUVW4S4awGv2Boez+NEgVchBN51FHjxlF35tlYLvBTPk2Ll5ldhHCqCLwkl7KxVnDOmzcWWTYXVeezaszM6dm6jfH2dGiAFnlMr5+64KfB2rO+GLZtw08O3hn2g3559ceHJ57p7MtSRHQWediVvOGEKvIYZ8Q37CVDg2c88mhFrb6E99cQeOPFYa7b9pPryxTbazahKykJ5SmtxdlhyNKGa+i4Fnqk42ZkJBJwo8GTa3EJrQvEV7iIxAu9pIfC6Kkxlx9DkBRb/LFuDrUXFaNmqKVq3awlxPCYfiwhQ4FkElt3GRYACb0d8hVsLce0DN4V9oLfYRnv56RfFxdqJjSnwnFg1i2OmwLMYMLuPiQAFXkzYbG20YmUR5syz9hILWxOKYDBVBV6gOICS38oQ2CoOQt8zHamdUyLIhq+4gYBTBZ5kz0ss3DAD687BaoG34xbavmIL7T3uBcrMTCFAgWcKRnYSIQF/RRkKF/1mvJ27675ISsuos6UVAi9QFUT5gjJ5YxbSd02DNy2+1f4Rpmzqay9/+Dqmz/7V6JNbaM1D6xEHmG8/wMS8fiPuac0mMTH5xEWAAi8ufGxsEQEKPIvAstu4CKgo8AK+IDa/WoBA4faD0HOOaSS+YUuNK1c2dgYBJws8ZxBmlLEQsFrgyZjkJRZJmIpAsLm4xEJcbiQuQ+FDAvURoMDj/LCLQFV5MRa/eRsBxVvOAAAgAElEQVQqNq81hkxv2gZdT7sTyRnZO4RgtsALlAew8a1NqNpUZYyVlJuE5mc1gzfDWV8jpWaat2g+NmzeiN7d9+AlFiZNXgo8k0AmshsKvETS59g7I0CBx7mhIgEVBV7lSh8K3isKw5XePQ05I3f8JlFFpowpPgIUePHxY2trCNgh8KyJnL26mQAFnpurq1ZuG2d9i38nvhIWVLth56HZ3kMsF3ilc8tQ8FVB2Di5w3KRtXemWpAYTUQEuIU2Ikx6vUSBp1e9nZItBZ5TKqVXnCoKvKotAWx+aUtYITL7ZSB7ML9R02F2UuDpUGXn5UiB57ya6RAxBZ4OVVYjx0QKvLK/y7Dli3CBl3NII2T34y921Zgd0UVBgRcdLy3epsDTosyOS5ICz3El0yJgFQWeBF/ySylKp5YjGAgiuWUy8k5qBG+6s7ZKaDGBLEiSAs8CqOwybgIUeHEjZAcWEKDAswAqu6yTQFVZMZa8fTvKN60xPp7WpDW6nXEXktNt2EJbGcSmNzbCV3ML7dliCy2/L3TkbKXAc2TZrA2aAs9avuw9NgIUeLFxYytrCagq8GTW/rIAguIyi+Rm4pZe3mJo7URQqHcKvPBiBIIBvPvvq/h+w0Q0SWmC0ztcgF65fRSqmB6hUOBtq7M8wyl/41pkZWYjOytHueJXFmxG0F+FtKYtlIvNioAo8Kygyj53RiChl1gIiVe+UN4VYN4lFiXwYT3KsAvEL4n5jaZtE9+RAu/ki+/CgsUrxPzb9hNJTnYmfvz0SeN/8xKL+OcOBV78DNmD+QQo8Mxnyh7jJ6CywIs/O/bgRAIUeOFVG7/uY7yx4oXqP0z1puGpvV5HnpB5fOwjQIEHlJWX4M0PnsWKVUsM8EMHHY1BBwy3rwgNjLRy3LvY+OuP23626t4LnU8eBW+y+AWQix8KPBcX18GpmX2JhRUoxiWtwIvJC+BHAB2FwLurYh80R9236loxvs59OlLgHXnGaDxx1+Xo2qntDrWjwIt/OlPgxc+QPZhPgALPfKbsMX4CFHjxM2QP5hKgwAvned+CmzCncGbYH47e7V7sndfPXPDsrV4CFHjApB8+xw9TvwrjdPVFd6FZ05YJnz1FS/7GkrHbFkOEnvZHn4bm/Q5MeGxWBkCBZyVd9h0rAdUF3lZU4uS0yQiK/4Sew/ztcGXVnrGmzHZREHCkwBt47JV4/4Xb0ar5jr89pcCLovo7eZUCL36G7MF8AhR45jNlj/EToMCLnyF7MJcABV44zx1W4CWJFXi9uQLP3FnXcG8UeMDYd5/A4mXzw2CddMz56NUz8TJ53ZQJWDPp87DYmu17EDqMPLXh4jr4DQo8BxfPxaGrLvBmeDbgttQZYRVoh2y8WHGQi6uiTmqOFHh7DxuFg/v3wqx5i9GsSS6uGnU8Dh7Q26C6dpPc280nHgKZ6UlI8nqxtdQXTzds2xABnknVEKGwj+dlpaKssgoVvkBU7ZzysodnRzilVGFxtmicjg0F4rKI7b+EdGQeDNo9BHKzU5CRmoSCYh/KK/3uSSzGTOQZeO/8+wq+zxdn4KU2wRnGGXj7xNgbm8VKICPNi5RkL4pKqmLtwvHt5i+cjTc/eq46j8a5TXHFqNuQnpae8Nzk2Xfzn7kHVWXbfo7yiJ8Dul94I7Ladkh4bFYFIFcP5YrvLSt9fvH9Jb9WWsWZ/UZPoEVeOjYVVsAvv7lU8PtLccIyLk+dhmWeourkrhKr7+QqPD7WE2jd1Nytyh5xOKul0ywgbtS79cFXcPjg/tivbw9MmTYHo+97AePeuN9YkRewdnjrK6LACCGvZGkhFcgz4SEQcFQl+O/IS/eKEgrdqOaDKi97xcSUf+3x01mVijAOj5iT8ssJ5yXngkoEQr+k0v2r5bwFf2LazJ/F+d05OGLwCOTm5ClTppL8dVj5/URUVVSg/UGHIK9TF2VisyQQ8Re367+3tAQcO7WagJyXxveV8r8U/fmgQGyjfcu3GP96SjHE0wZDknY82sxqTrr2L3/2MPOxXODVFew5Vz+A444ciBFD9uMlFiZUk1toTYDILkwnwC20piNlhyYQ4BZaEyCyC1MJcAutqTjZmUkEuIXWJJDsxlQC3EJrKk52ZhIB1bfQmpQmu4mRgOO20JaWVWDx8n/Ru8f23wqdftm9OOP4YThsUD8KvBgnQs1mFHgmQGQXphOgwDMdKTs0gQAFngkQ2YWpBCjwTMXJzkwiQIEHrK+qwofFpSgRu4lGZGWgZ1qqSXTZTawEKPBiJcd2VhKgwLOSrvP7dpzAKygsxtCTr8MTd1+G/fvugZ9+/RPX3/08xr/5AJo2zqHAM2FOUuCZAJFdmE6AAs90pOzQBAIUeCZAZBemEqDAMxUnOzOJgO4CrzAQwKX5m7HFv+2sNbkB6r5mjdHr/9k7Dzi5ynL//6ZuL9nspuymF9JIJVSlSJGOSJNmFyt2wQoIol6x3IuICoqA4hXwUhRQpGlCKIFASKMlJKRustm+s7O70849s4FJJmVnzswpb/nN/fPx/8me87zP8/29LJvvnkKJZ9MOK6wMBV5h3HiWswQo8JzlK3t16QReGvii51bgZ7+5Gy1tnWgaVY8rv3ARjlgwczALvoW2+C1JgVc8Q1awnwAFnv1MWbF4AhR4xTNkBXsJUODZy5PV7CGgu8Bb1NePG9q7smCeal6Fd3lttT2AWaUgAhR4BWHjSQ4ToMBzGLDk5aUUeEMxp8ArfkdS4BXPkBXsJ0CBZz9TViyMQPqtln2pnsGTJ9SPQFvngPkCpcJq8SwSsJsABZ7dRFnPDgK6C7w3YjF8bWdHFsqPVVfi/KoKO/CyRoEEKPAKBMfTHCVAgecoXumLU+BJH6H9A1Dg2c+UFYsnQIFXPENWKJ5ACkm0xDYhbsQGi1WVlqHGaDJvhwoUX5wVSMAGAhR4NkBkCdsJ6C7w0kDv6I7grz29g2znmbfOXlVXi1K/vW8TtD04xQtS4CkesKTjUeBJGpxLbVPguQRapmUo8GRKS59eKfD0yVrkSSPJTnQkdmRaDAX9qPY1oNxfK3Lb7E0jAhR4GoUt0agUeLvCSj8DL2Jesj02FJQoPXVbpcBTN1uZJ6PAkzk953unwHOesXQrUOBJF5kWDVPgaRGz8ENS4NkTUVtvBL97YSnW7NiOGSNG4DOHH4nhFZX2FNe8CgWe5htA0PEp8AQNRvO2KPA03wCCjk+BJ2gwgrRFgSdIECK1QYEnUhrs5V0CFHjcCyIQSBpJ7EyYt9Cm3rmFtqQMtWgyW+MttFby+f7jj2JVc3PmlFkjR+G6k0+1UoLHHoAABR63hogEKPBETIU9UeBxD4hIgAJPxFTE6YkCT5wshOmEAk+YKNjIHgQo8LgdRCHg1UssjP4kkiu6YT5wD4H5NfCF/KIgsdzHh+/+M6LmQ93f/fh8Ptx10aUoDfK2Mssw9zqhUIGX7O5CvHkbgnXDEWwYUWwbPJ8EsghQ4HFDiEiAAk/EVNgTBR73wFAEKPC4P/YhQIHHTSEiAQo8EVNhT6PqytDS0ef4W2iN7jgG/vstGO27pJd/ZAnCX58KX4mcEo9X4Dn3704hAi+2aSN6n1+Cdzdy6YxZKJs737kmWVk7AhR42kUuxcAUeFLEpF2TFHjaRW5pYAo8S7j0OJgCT4+cZZuSAk+2xPTo1y2BF//PTiQe2H3LaZpu+KPjEFgg58sz0s/Au+WF5/Hajh18Bl6e/6qk4nFTsj2G2MY3EagbgcrDT0Bw+Kh9zi5E4HWbtzQn21p31zKviKw990Pw8YrIPNPhYbkIUODlIsSve0GAAs8L6lwzFwEKvFyE9P46BZ7e+e93ego8bgoRCVDgiZgKe3JL4CWeaUP83q1ZwEMXj0Xw8GEMQRMCkeefQN+rL2am9ZdVYviHvmBejpl9FaZ9Au9CU+Dx2Y6abC/Hx6TAcxwxFyiAAAVeAdB4iuMEKPAcRyz1AhR4UsfnTPMUeM5wZdXiCFDgFcePZztDwC2BZ0QT6L9hHdCx6xZan3kLbYnEt9A6k4baVTvuvxWJzrasIYed82kEa4dn/1llGGUlAXREYugbSOYFhbfQ5oWJBxVBgAKvCHg81TECFHiOoWXhIghQ4BUBT4NTKfA0CNnqiBR4VonxeDcIUOC5QZlrWCXglsAb7KvPfInFym4YPsN8iUWt1C+xsMqZxwNOXoGX5suXWHCXOUmAAs9JuqxdKAEKvELJ8TwnCVDgOUlX/toUePJnaPsEFHi2I2VBGwhQ4NkAkSVsJ+CqwLO9exaUiYCTz8CTiQN7lZMABZ6cuaneNQWe6gnLOR8Fnpy5udU1BZ5bpCVahwJPorA0apUCT6OwJRqVAk+isPZo9e2ubjy6Ycvgn5wycQwm1FTLOch+ui7kGXjKDM9BhCVAgSdsNFo3RoGndfzCDk+BJ2w0QjRGgSdEDGI1QYEnVh7sZhcBCjzuBBEJyCzw0k9H2+LrwyjDfJ4esl+EICJru3raFunF9xa/gFgyNViyxHxRww+OPgyNleV2LeFpHQo8T/Fz8QMQoMDj1hCRAAWeiKmwJwo87oGhCFDgcX/sQ4ACj5tCRAIUeCKmwp5kFXgbff24KbgFHYijzBfAxxKjsTBVpUWgf1/7Nu59/a2sWS+YPhlnTZ2gxPwUeErEqNwQFHjKRarEQBR4SsSo3BAUeMpFautAFHi24lSjGAWeGjmqNgUFnmqJqjGPrALvp4GNeN0fzYRQhSB+EZ9iXofnUyOYIaZYum0HbnppddYRXzzkYBzeOFKJ2SnwlIhRuSEo8JSLVImBKPCUiFG5ISjwlIvU1oEo8GzFqUYxCjw1clRtCgo81RJVYx5ZBd7loTfQh123kL77uT4xCaPN22lV/6QMA79Z/iqe27p9cNQjm0bic/Nnwe9TQ15S4Km+g+WcjwJPztxU75oCT/WE5ZyPAk/O3NzqmgLPLdISrUOBJ1FYGrVKgadR2BKNKqvAezDQiof8OzOk5xlV+GJijETki2+1Jdo3WGREeVnxxQSqQIEnUBhsJUOAAo+bQUQCFHgipsKeKPC4B4YiQIHH/bEPAQo8bgoRCVDgiZgKe5JV4KVg4Gl/F1b4ezApVYYTU3Uo1ehFFirvXAo8ldOVdzYKPHmzU7lzCjyV05V3Ngo8ebNzo3MKPDcoS7YGBZ5kgWnSLgWeJkFLNqasAk8yzGzXAgEKPAuweKhrBCjwXEPNhSwQoMCzAIuHukaAAs811FIuRIEnZWzONk2B5yxfVi+MAAVeYdx4lrMEKPCc5cvq1glQ4FlnxjOcJ0CB5zxjrmCdAAWedWY8w3kCFHjOM5Z5BQo8mdNzqHcKPIfA7qdsy/oE1jzeh2iXgfHzw5h5gnkTm9+99WVaiQJPprT06ZUCT5+sZZmUAk+WpPTqkwJPr7xlmZYCT5ak9OqTAk+vvK1OS4FnlZgGx1PguRPyQDSFf/13D5JxI7Pg7JNLMeXIUncakGwVCjzJAtOkXQo8TYKWaEwKPLHCivfFEIvEUF5fCUVedFwQYAq8grDxJIcJUOA5DJjlCyJAgVcQNm1OosDTJur8B6XAy59VMUfuWBvHs3/uzSoxYkoQ77m0spiyyp5LgadstFIPRoEndXyuNW+Yv7BJbjJfHTJgIDA2AH+dc5daU+C5FmvOhZqXb8GGf7+JVMpA5chqzDpnDkIVJTnPU/EACjwVU5V/Jgo8+TNUcQIKPBVTtW8mCjz7WCpTiQLPnSh3XYHXbV6Bt3s9XoF3YPYUeO7sSxlW2Rbpxsvbt2BiTR1mNYzytGUKPE/xS7G4kTAQfyZuyrtUpt/gvBACIwKO9E+B5wjWAxZNRaMwenrNPOux5yV2scgAXrxlCYzdF9lj9LwxmHziNHcbFGQ1CjxBgmAbWQQo8LghRCRAgSdiKuL0RIEnThbCdEKB514Uez4Db+ycEGa/vww+5y7McG8wB1aiwHMAqoQlX2zejJ8v/Q+S7/yt+PTJM/CxOYd6NgkFnmfopVk4tTOJ+PI9flNjdp6+Ci84I+TIDBR4jmDdb9GBp5/BwKLFMJIpBEaPRvmlF8JfXj54bOu6nXj9wZVZ51WOrsG8Sxa616BAK1HgCRQGW8kQoMDjZhCRAAWeiKmI0xMFnjhZCNMJBZ4wUbCRPQiIIvBexz+wzvcfBFGCmcaZGIfDmJOLBL7/9L+wpnVHZkWf+VCpP55xEUqDQRe72L0UBZ4n2KVa1Og1EHtmIKvn4KQgAuYjE5z4UOA5QXXfmqmOTvT88uasL4SPPAJl7z9h8M9SiRRevuN59Hf2ZY6ZdvosNMzw9qphd+jsuwoFnlfkue5QBCjwuD9EJCC6wEs+8hgSD/3TvOjEj8C5ZyFwwrEiYlS2Jwo8ZaMtfDAKvMLZ8UznCIgg8Lb6luNZ/DpryJNSV6HWN865wVk5i8C3/2MK1I7WzJ+lBd6fzrwYJQFnbkfMhZ8CLxchfj1NILEugaT51vH0xz/MvPpuXhC+kM8ROBR4jmDdp2h89RpE73sw68/9TU2o+tTHMn8Wj8aweelGxHr60TBzFIZPaXCnOQFXocATMBS2BAo8bgIRCYgs8FIr1yB+3U+ysIV+dDX8B00REaWSPVHgKRlrcUNR4BXHj2c7Q0AEgfeS7y6sx6KsARcYl2Iy+JsnZ1Lft+ry7Vvxk+efytxCe+bUWfjIwYe4tfw+61DgeYZeuoWNmDH4Egt/lbPPSaDAc2drGIkkIr++BamOjsyCZReci/CM6e40INkqFHiSBaZJuxR4mgQt2ZgiC7zEXfcg+eAjWUSDl5yPwAfPlIyyvO1S4MmbnWOdU+A5hpaFiyAggsDb3xV4J6auxjDf2CIm46lWCfAlFvsSS/an0LKsE/0tMdRMLUfd7GqrWIU53ojHBm/LQMCZW0yFGdShRijwHAK7n7JGTw/6n10Ko7sbodkHIzT9IPcWl2wlCjzJAtOkXQo8TYKWbEw3BV5/fxJbm3swYVwNAoHcdwbs9wq8H14F/7SpklGWt10KPHmzc6xzCjzH0LJwEQREEHjp9vkMvCJCVPBUUa7Ae/NPWxDd1p8h3HRCPRoW1kpF3EilkNy5DUZ/dLBvf1UtAnUjpJpBhGYp8ERIgT3sTYACj3tCRAIUeCKmwp7cEnjPvbgdP//1ckSjcdTXl+Pqry/ElEm5f3YcfAbew/80f1AzHwlyzpl8Bp7LW5YCz2XgMixHgSdDSvr1KIrA0488Jx6KgAgCLxFJYPXNb2e1WTa6FNM+Mkaq8FLdnUh2tGT1HBw5Br7SXW/15Cc/AhR4+XHiUe4SoMBzlzdXy48ABV5+nHiUuwTcEHiplIFLP/c4Ojt3//J3xrTh+Pl173F3WK5mmQAFnmVk6p9Agad+xjJOSIEnY2rq9yyCwEvGUlh90wYYCSMDvGZaJSaeLdfbLpOtzUj19mRtmkBtPfw1depvJBsnpMCzESZL2UaAAs82lCxkIwEKPBthspRtBNwQeJu3RvCZrz2V1XN5eQj/d/upts3BQs4QoMBzhqvUVSnwpI5P2eYp8JSNVurBRBB4aYDtq7qx+fGdMOIGSoaFMOm80SipC0vF1oj1I9G8aXfP6VszGifA59EbhqWCt0ezFHiyJqd23xR4aucr63QUeLImp3bfbgi8NMFrf/oCli7bnoF53llT8YlLZqgNV4HpKPAUCNHuESjw7CbKenYQoMCzgyJr2E1AFIGXnis5kMJAZxxlDWHzJRC5H0RsNws76qWff5eKdMHw+RGoHgZfSC4JaQeDYmtQ4BVLkOc7QYACzwmqrFksAQq8YgnyfCcIuCXw+voS+Ns/1+ONtzpx2PyROPn4cfBL+vOjEzmIWpMCT9RkPOyLAs9D+Fz6gAQo8Lg5RCQgksATkQ97cp8ABZ77zLlibgIUeLkZ8Qj3CVDguc+cK+Ym4JbAy90JjxCRAAWeiKl43BMFnscBcPn9EqDA48YQkQAFnoip6N0TBZ7e+Ys6PQWeqMno3RcFnt75izo9BZ6oyYjRFwWeGDkI1QUFnlBxsJl3CFDgcSuISIACT8RU9O6JAk/v/EWdngJP1GT07osCT+/8RZ2eAk/UZMToiwJPjByE6oICT6g42IxGAm/j2gheXNSOcKkfR53UgPqRJcxfcAIUeIIHpGF7FHgahi7ByBR4EoSkYYsUeBqGLsHIFHgShORhixR4HsIXdWkKPFGT0bsv1a/A27Yxilt+uBaGsSvnsvIgvnT9NFRUBfUOXvDpKfAED0jD9ijwNAxdgpEp8CQIScMWKfA0DF2CkSnwJAjJwxYp8DyEL+rSFHiiJqN3X6oLvMfua8aSR1uyQj7/svGYfVit3sELPj0FnuABadgeBZ6GoUswMgWeBCFp2CIFnoahSzAyBZ4EIXnYIgWeh/BFXZoCT9Rk9O5LdYG39KlWPPKXrVkhf/LKKRg/tULv4AWfngJP8IA0bI8CT8PQJRiZAk+CkCRpMTWQQKy5A+GmOvhDgaK6psArCh9PdogABZ5DYBUpS4GnSJB2jkGBZydN1rKLgOoCLxE3cNdN67H+tcggsiNOqMdpFzbZhY91HCJAgecQWJYtmAAFXsHoeKKDBCjwHISrUenois3Y/qsnkIzGEKytQOM3TkbJxIaCCVDgFYyOJzpIgALPQbgKlKbAUyBEu0egwLObKOvZQUB1gfcuo9bt/SgtC6CyJmQHNtZwmAAFnsOAWd4yAQo8y8h4ggsEKPBcgKzBEhsuvwuJjt7MpKUHjcLYaz5Q8OQUeAWj44kOEqDAcxCuAqUp8BQI0e4RKPDsJsp6dhDQReDZwYo13CNAgecea66UHwEKvPw48Sh3CVDguctbxdWSnVGs/8Kfskbzl4cx+XcfL3hcCryC0fFEBwlQ4DkIV4HSFHgKhGj3CBR4dhNlPTsIUODZQZE17CZAgWc3UdYrlgAF3oEJGjDwIv6NFf7n0WRMxPGps1HqKysWOc/PgwAFXh6QeEhOAs03Po7IC+szx9WePhcNFx+R87wDHUCBVzA6nuggAQo8B+EqUJoCT4EQ7R6BAs9uoqxnBwEKPDsosobdBCjw7CbKesUSUFXgDXQ2I/LmMwjXjUHVlML+wv6E7z485Nt9Bc9kzMKXUj8sFjnPz4MABV4ekHhITgKpWAKd/1iJvvUtqJg1BjUnzYLP78t5HgVewYh4ogcEKPA8gC7RkhR4EoXlVqsUeG6R5jpWCFDgWaHFY90i4LbAa4ml8M+2GLYOpHBQeQCn1YdRXsRfXtziJMo6HZGt2Ny6HA3VUzC6broobdnah4oCL7LxZWy8+woYifggq9o5p2DMmd+xzO0G31ex1bch67wfpG5HNYZZrsUTrBGgwLPGi0e7Q4BX4LnDmatYI0CBZ42XbkdT4OmWeB7zUuDlAYmHuE6AAs915FwwDwJuC7ybtvSh1ZR4735mVwZx3oiSPDrlIWu3LcaiVb9BykgOwpg/+VwcOvVDyoFRUeBt/Ot30PPmkqyspn/5AQQrh1vK71b/9ViDZZlzSszbZ69P3oEw+O+QJZAFHEyBVwA0nuI4AQo8xxFzgQIIUOAVAE2jUyjwNAo731Ep8PIlxePcJECB5yZtrpUvATcFXiRp4Kcbo1mtVQb9uGIcn+GVT173PXMl2nrezhwaCITx8RP/CL/Pn8/p0hyjosB7+y/fQGT9C0ULvO3YjN/5f4RWNCNkSruLU5djAY6WJluZG6XAkzk9dXunwFM3W5kno8CTOT3ne6fAc56xdCtQ4EkXmRYNU+B5F/NAXxQvPf8IWlu2YPL0QzBr7jHeNSPYym4KvPToe1+BN7cqiHMaePVQPtvi3iVfQWdkW+bQYKAEHzvxTgq8fOB5fEzv5pV4+3+/mrmFdti809F0+jcL6ippXoHZ7NuIemM0X2BREMHCTqLAK4wbz3KWAAWes3xZvTACFHiFcdPlLAo8XZK2MCcFngVYPNQ1AhR4rqHOWiiVSuGeP1yL7VvXZf78fad+FPMOe783DQm2qtsCb+9n4J1uPgOvjM/Ay2tXrN/+HJ5c8T8wDGPweN5Cmxc2YQ6y4yUWwgyjYSNWBF7voy3oW9QJX5kPFWeNROm8ag2JcWQ3CFDguUGZa1glQIFnlZhex1Pg6ZV3XtNS4OWFiQe5TIACz2Xg7yzXvnMb7vz1FVmLj2yagos/da03DQm2qtsCT7DxpWunvWcztrSt4EsspEuODctOIF+B17+sC923b8kat+7qKQiO5JXGsu8BEfunwBMxFfZEgcc9MBQBCjzuj30IUOBxU4hIgALPm1RiA/34zU8/g1QykWlg2uyjcNo5X/CmIcFWpcATLBC2AxWfgcdY5SeQr8Dr+Usz+pa0Zw1cdWEjyo7mm4Ll3wXiTUCBJ14m7AigwOMuoMDjHrBEgALPEi4e7BIBCjyXQO9nmVdfWYzHH75tUOLV1o3EOZd+GzXDGrxrSKCVKfAECoOtDBKgwONGEJFAvgJvnyvwAkDdd3kFnoiZqtATBZ4KKao3AwWeepnaORGvwLOTpiK1KPAUCVKxMSjwvA00NtCHzo4W1DeMgT9g/o2Kn0ECFHjcCKIRoMATLRH2kyaQr8BLH8tn4HHPuEWAAs8t0lzHCgEKPCu09DuWAk+/zHNOTIGXExEP8IAABZ4H0LlkTgI6CLzeaATNLRuRMmmMNgVuVUVNTi48wDsCFHjesefKByZgReCRo+QEkkD42T4ENiaQnBRC7PBSQNDf+1HgSb7XFG2fAk/RYG0aiwLPJpAqlaHAUylNdWahwFMnS5UmUV3gDZhXXr7y2lIkkvHB2Hw+P+ZOPwwV5VUqxajULBR4SsWpzDAUeMpEmXOQsvt7EVo2kDkudlgJ+s+uyHmeFwdQ4HlBnWvmIkCBl4uQ3l+nwNM7//1OT4HHTSEiAQo8EVNhT6oLvOadW7B+02tZQY8330I8ZtREhi8oAQo8b4Pp6osOXq06rKzc20YEW50CD+jaYWe8k4UAACAASURBVGDZQwm0bU6hcXoAh34ggJJyn2BJFdmOufmrr+sAYkamkFHlR8+3a4ss7MzpFHjOcGXV4ghQ4BXHT/WzKfBUT7iA+SjwCoDGUxwnQIHnOGIuUAAB1QVeR3c7Xl37UhaZyeNmYlRDUwG0eIobBCjw3KC8/zUeeW05XmneNPjFaQ2jcfashQj6/d41JNDKFHjA334SQ6R9t9iaeEgAR10QFCgle1qp+J8uBFrM+2jf+SRHBND7FTEfvUCBZ0/mrGIvAQo8e3mqVo0CT7VEbZiHAs8GiCxhOwEKPNuRsqANBFQXeGlE6za+ih2tWwdp1dWOwLSJs+GnlLBh9zhTggLPGa65qq5v34m/vPJs1mGnTp+HBY3jc52qxdd1F3h9PQbuvz6WlXVplQ/nfi+sXP6B9QmU/28EvmgKRlUA0QvLkZwYEnJOCjwhY9G+KQo87bfAkAAo8Lg/9iFAgcdNISIBCjwRU2FPOgi8dMrpZ+GlbwssKylj6IIToMDzJqDF61/H02+/kbX4/KYJOG3aXG8aEmxV3QVeOg5drsBLz2qYt9AGWpNImVffISjubcIUeIJ9o2A7gwQo8LgRhiJAgcf9QYHHPSAFAQo892N66G/34pkl/0ZVVRXOPOtDmDPvEPebEHxFXQSe4DGwvT0IUOB5sx26+6P47dKnEE/uunUwfZXqxw85GqOqxHz2l9uU3BZ4O9GNzb5WNKIOowwxMkg/A+/FB+No32YMPgPvsLMDCJeJK7fc3iNerEeB5wV1rpmLAAVeLkJ6f50CT+/89zs9r8DjphCRAAWeu6k89+wi3HXnbzOLBgIBXHXtL9DQMMLdRgRfjQJP8IA0bI8Cz7vQ26IRPLdx7eDVqgvNq+8aq4d514xgK7sp8F71bcET/pUwzP9Lf96TmoaFxhTBiLAdEQhQ4ImQAnvYmwAFHvfEUAQo8Lg/9iFAgcdNISIBrwRe+sf/SCKFAfNvZCXms8grg37o8PvyP/zul3hp2XNZW+Hjn/oiFh56lIjbw7OeKPA8Q8+FD0CAAo9bQ0QCbgq8PwUWoR2RDIYgAvhc8mTo8V9vd9JPDPSge+MLCJbVoGrMAvh8cr6shQLPnf3CVawRoMCzxku3oynwdEs8j3kp8PKAxENcJ+CVwOuMpdCb3P3WuIqAD7VhOX9QtRIar8DLjxYFXn6ceJR7BCjw3GPNlfIn4KnA85kCL0GBl39aQx/Z37kFm578LyRjuyRpxYhpGHv8N6WUeBR4du0K1rGTAAWenTTVq0WBp16mRU9EgVc0QhawQCCBfmwPrTCfeAyMTiwwf0++/zeVeSXwtvUnYez2d+YPqEBjqflQZg0+fAZe7pAp8HIz4hHuEqDAc5c3V8uPgJsCb41vM570r+IttPlFY/moHS/9Ge1vPp513viTvofyevluU6bAsxw/T3CBAAWeC5AlXkJKgbd5WwuuuuEPeGPdJjSOqsd3v/xhLJg9dTCGbW19EschRusUeGLkoEMXcV8Uz5TfgD5/++C4lamROLL3GwiiZJ/xvRJ4O8x7ZxOp3QYv6PdhZPpe2iI+RncfjGfXm7+2DsN/5GTzDW3F1SuiFZ5aJAEKvCIB8nTbCcgo8Hbu6MWaZdvQ0FiFWfNH2c6EBb0n4KbAS08r4kssvE/Bng6aX7wTnev+nVWMAs8etqxCAmkCFHjcB0MRkFLgffTLP8bx712AS885Cc8uW2PKvNvw+D0/RygYoMCzYb9T4NkAkSXyIrAptARrSu/NOnZW/wUYF3+vMAJvIJlCexxImZfh+c3L7+rMCwRLAoULN2NHN5JXPgB0RQdn9M1shP/aM+AL6XFVX14bQ6KDKPAkCkuTVmUTeG+u3olf/+hpJOLpVz+YLxw4cSIu+gzfeK3adnVb4KnGT6R5YpGdePvRa5CM7/o5pnzEdIw7/kreQitSSOxFagIUeFLH53jz0gm8to5unHLxlXju4ZsRNN+KmP6cd9k1uPLzF+Gw+dMp8GzYMhR4NkBkibwI7E/gze6/BGPihwsj8NKNpOVdwrwIL2jePpuWeMV8knc+D+OB5VklAledDt8h44opy3M9IkCB5xF4LntAArIJvJt/+DRee2VH1jw/uvUMVA8rZcoKEaDAUyhMc5R4Xxd6Ni/jSyzUipXTCEKAAk+QIARtQzqB9/KqtbjuF3fiwduvzyD9+rW/xuELZuKCM49DW3dMUNTytFVqPqDfb94mGDWf/cUPCThJIIZeLCr9CaK+d26hxQgc0/cN8yl45fssW1UWxEAiiVh8jwfSOdmcQ7Vjtz+LxH3ZAq/k+2cgsHC8QyuyrJME6qrD6OiJZT0n0cn1WJsEchFIf68Mh/yI9CUw8M5VbbnO8fLrN163GGuWb89q4ad/OAs1FHhexmL72iXmngyaL4Hq5c+WtrNlwcIJVJrfL+OJlBTfKwufkmfKRmBYVRhdvTGkdl2Yzg8JZBEYbv7dw86PzzA/dhbcu9azy1bjl7+/D3f/9prMl773k9tw0KQx+Mj5Jzu5NGuTAAk4QCBuvsRio/ESfObrK8b75u/3+XcOLOtZyeS2LrR+4R6kOnfdehI+uBF1vziXt9B6lggX9pLAQN92dHeuhs8fMoXNXITCtV62w7U9ILDGvPrux999CvF3ZOOJp03BZV/Z9ypsD1rjkiRAAiRAAiRAAiSgNAHHBd7y1Wtxzc/uwN/v+GEG5Ne+fzOOWngwzjvjWP4GxYbtlX68l8+8TTCRdNTF2tApS+hEIGT+5j5p/n5Ahd9Gpbr6EFu8Fr7KEpQcbb6Ahy+xkHYrp690SosHfre0HmF8YCdatpnPgzR2/YrZ5y/BiDHnIxistF6MZ2QIpL9Xpq+iT5jP7zT/nxSfHc0RvPLCVoxsqsK8hY1S9MwmrRHgz5bWePFodwikrwpNX3siy/dKd6hwFa8JhM2/F8TNTensZVFeT8n1CyWQvqLdzo/jAq+jqwcnXvB1LPnbr1BWuuvywVMvuRI/+vZlmH/wVD4Dz4Y0+Qw8GyCyhO0E0peT9/Un0C/BLWG2D8+CwhLgM/AKj2ag50XEoyuzCpTWvA/B0kmFF+WZkO0ZeIxMDwJ8Bp4eOcs2ZW1l2Hw0SxLRAT42SLbsVO6Xz8BTOd3iZ5PuGXjpkT/5tRtw6LzpuOySM/DPfy8dvKX2n3++AQHz13vb2vqKp6J5BQo8zTeAoONT4AkajOZtUeAVvgHifW9goHtJtsAbdgqC4abCi/JMCjzuASEJUOAJGYv2TVHgab8FhARAgSdkLMI0JaXA27q9Fd/58e/wxlubMbZxBL7/9Y9h1rQJg1Ap8IrfWxR4xTNkBfsJUODZz5QViydAgVc4Q8NIor/7KST7Nw0WCZXPQEnVUYUX5JmDBHgFHjeCiAQo8ERMhT1R4HEPiEiAAk/EVMTpSUqBNxQ+CrziNxcFXvEMWcF+AhR49jNlxeIJUOAVzzCV6Daffxc0/9n37dPFV9evAgWefpnLMDEFngwp6dcjBZ5+mcswMQWeDCl51yMFnnfshV2ZAk/YaLRujAJP6/iFHZ4CT9hotG2MAk/b6IUeXHaBF0/0IWK+NbumYpz5kpiA0KzZXP4EKPDyZ8Uj3SNAgeceaxlXosCTMTWHe6bAcxgwyxdEgAKvIGw8yWECFHgOA2Z5ywQo8Cwj4wkuEJBZ4G3euRQvvHozkskYKstG4j1zrjBF3hgXqHEJpwlQ4DlNmPULIUCBVwg1fc6hwNMn67wnpcDLGxUPdJEABZ6LsLlU3gQo8PJGxQNdIkCB5xJoLmOJgKwCzzBSeODpTyCR6M/MO2r4PBwz91uW5ufBYhKgwBMzF927osDTfQcMPT8FHvfHPgQo8LgpRCRAgSdiKuyJAo97QDQCFHiiJcJ+0gRkFXgdPRvw+IvfzgqxNFyLs977WwarAAEKPAVCVHAECjwFQ7VxJAo8G2GqUooCT5Uk1ZqDAk+tPFWZhgJPlSTVmYMCT50sVZpEVoGXzmDRKz/EjvZVmTgOnvQhzJzwQeniSSVTaNvRhZq6SoRLQ9L170TDFHhOUGXNYglQ4BVLUO3zKfDUzreg6SjwCsLGkxwmQIG3G3AM3UigB+Vocpg6y+ciQIGXixC/7jYBCjy3iXO9fAjILPDi5u2zb2x+CJ2RjWiqPxQTRh0Dn8+Xz9jCHJMWdw/f/h90tfUgGA7gfWcfhhmHThamP68aocDzijzXHYoABR73x1AEKPC4P/YhUFbiR8D8wSTSnyQdEhCGQHV5CAOxJAYSKWF68qKRjYH7sM3/j8GlqzAV0+KXI4RKL1rhmiaBuqowOnti0HtXciuIRIACT6Q02Mu7BMrC5s+WAb/5JteEbVDefL4Dz93bjIFoCvNOrsdhHxxlW23VCj1y52K8tXpTZqy0xLvsmvMRCgdVG9XSPFXmz5aJRBJ9Mf5X3BI4HuwogfRFC1295s+W3JaOcpa1OAWerMk52DevwHMQLksXTIBX4AG92IKVoWuyGI5NfgBjUmcVzJUnFkeAV+AVx49n20+AAs9+pqxYPAG7r8Br39aPu654HamkkWnu/Z8fh5nHDC++WQUr/P66+xDt6cua7PzLT8bo8Q0KTpv/SLwCL39WPNI9ArwCzz3WMq5EgSdjag73TIHnMGCWL4gABR6w3f8fbAj8KYtfrTELMxJfK4gpTyqeAAVe8QxZwV4ChQg8I5FA6q2NSHV2wT+yAYHxY2DeI2hvY6ymNQG7Bd7Kx1vx1G2bs5jOPrEeJ3xqrNacDzT88qdfw9N/fynz5aZJI3Hu507SnhUFnvZbQEgAFHhCxiJMUxR4wkQhTiMUeOJkwU52E6DAA5JGP5aHv4s4OjNgZsWvQDWmc6t4RIACzyPwXPaABAoReImlLyPV3JKp6Z8+BUHzH348IGAY6HtrC2KbdyAwrAoVMybBVyL/CwfsFni8As/a3jTMffX6S+uxbvVmNDTWYf7R01FSFrZWRMGjKfAUDFWBkSjwFAjRwREo8ByEK2tpCjxZk1O7bwq8Xfn2oxXNwccQN7owwjgWtamZagcv+HQUeIIHpGF7VgWekUwi/vATgPkX/Hc//qoKBE84WkN63o8cfXU9Ii+uyTQSahiGYae91/vGiuzAboGXbofPwCsyFJ4OCjxuAhEJUOCJmIo4PVHgiZOFMJ1Q4AkTBRvZgwAFHreDiAQo8ERMRe+erAq8tLiL/2sRjP7+DDjfiOEIHXWo3iA9mr79kSVItHZkrV5/wUnwl5V61JE9yzoh8OzpjFV0JkCBp3P64s5OgSduNiJ0RoEnQgqC9UCBJ1ggbGeQAAUeN4KIBCjwRExF754sCzwTV/r22eTyVTBicaC8DMHD58NfU603SI+m71r8MgY2bN29ut+P+gtPhj8k99tCKfA82lBcdkgCFHjcICISoMATMRVxeqLAEycLYTqhwBMmCjayBwEKPG4HEQlQ4ImYit49FSLw0sSMRBJGJLJL3PEFFp5tokRXBF1PvoBkTy8QDKD68NkonSL/ixko8DzbUlx4CAIUeNweIhKgwBMxFXF6osATJwthOqHAEyYKNkKBxz0gOAEKPMED0rC9QgWehqiEHdlIpZDojCBYWQ5fWO4r796FTIEn7HbTujEKPO/jT6UMtLTFUV2VRKB8O8KpMfBB/hf3FEOWAq8YeuqfS4GnfsaWJ6TAs4yMJ7hAgFfguQCZS1gmQIFnGRlPcJgABZ7DgFm+IAIUeAVh40kOE6DAcxhwjvId3Qnc+1ALtrd1IRlqxpEnL8LMuTswKnolSpKTvG3Ow9Up8DyEL8HSFHgShOR2ixR4bhPnevkQ8Ergtcf7sDrShvpQGWZWDs+nVR6jEQEKPI3ClmRUCjxJgtKsTQo8zQKXZFwKPG+D+vtjrVjxWgSxwCYYSMAfSOHir9yOquBkNPVe521zHq5OgechfAmWpsCTICS3W6TAc5s418uHgBcC781oB27duhKxVHKwxUOrR+HDo2fm0662x/Saz9Ba8fJylJkPwp89bz6C5jOcVP5Q4KmcrpyzUeDJmZvqXess8LpizehOtKI0UIW68FgEfGr/d1GmvUyB521av/nTVuzs6EfMvzHTyGkfeQCjmyKY2HO7t815uDoFnofwJViaAk+CkNxukQLPbeJcLx8CXgi8tLxbHWnNau/6ye9BdbAkn5a1O6attQ2/vOGniPT0DM4+dsIEXP71r5oST41nOO0vUJkFXk9/BC9tfBk7IzsxypTTh4ybh/KSCu32rWoDU+Cplqga8+gq8NoG3sbOgQ2ZECuCwzC2fJ4aoSowBQWetyEuXxPBw0+0Iu7fgZSvF/WNLTj9ww9gWOJM1A9c6m1zHq5OgechfAmWpsCTICS3W6TAc5s418uHgBcC747mNXi5e0dWe1dPOnLwdlp+9iXw8AMP4t+PPZ71hU994fOYcfAsZXHJLPD+teZxtJm3h7/7aaxtxHHTjlE2K10Go8CzL+nepQl0/jmGZJeBqpODqL2wBD6/ffV1qqSrwNvQuxQDyWhW1FOqjkLQx18EirD/KfC8T+HVtb1Y9UY3yoe/gVlHLMfw8AxUx99vvshC32+2FHje70uRO6DAEzkdj3qjwPMIPJcdkoAXAu+tvk78avMrSBqpwd54C+3Qm5QCT55/iRPmbeF/ffH/zGfOGJmmQ8EQzj/kXHmGYKf7JUCBZ8/GSLSksOUzvTB2PUFh8FN/eSmq3q/32xELpaurwNscXYHeRHsGm9+8fXZq1dGmnPAVipLn2UiAAs9GmCxlGwEKPNtQKlmIAk/JWIsbigKvOH482xkCXgi89CSt5kss1pi30TaEyrV4iUXSnDfW34HSylHwWbzUpLW1FTfd8LPMLbQTJk/G577yJd5C68y/EkVXffy1J7Gze2emDq/AKxqpEAUo8OyJoXdxHC0/688qVnl0CA1XlNqzgGZVdBV4A8lebO5biUSqf/C/qaPLZpqP4WjQLH1xx6XAEzcbnTujwNM5/dyzU+DlZqTdERR42kUuxcBeCTwp4NjUZMe2F7HltfsA8+qscPlwTFjwaZSU1VmqHon0YOXLr/AlFpaoeXMwn4HnDXenV6XAs4fwfq/A+7J5Bd4JvAKvEMK6Crw0K8MwMJCKIOwrg9+v7jNhC9kXXp9Dged1Alx/fwQo8LgvhiJAgcf9sQ8BCjxuChEJUOA5m0oyMYBXF10zKO/e/dSMmo9xsy92dmHJq8v8DLx80Bsd2+DrboExfBx8ldZkbj71eYz9BCjw7GO65zPwKs1bZ+suCdtXXLNKOgs8zaKWalwKPKni0qZZCjxtoi5oUAq8grCpfRIFntr5yjodBZ6zyfW2r8P6l27JWqSkYgQOOuoKZxeWvLrSAm/DS/Cte35XQj4fjFknAKOnSZ6Y+u17JfBSSKI3uNF8yYOBsvgYBAw+pF/93Zb/hBR4+bPike4RoMBzjzVXyp8ABV7+rHQ8kgJPx9RzzEyBx00hIgEKPGdTMcwXdbz14s3o69qUWahx2tkYPu49zi4seXVlBZ55y5fv37cCycTuhCqGwTiKV2SKvmW9EHgpJNBc8RhigV0P6w8YFWiKnGz+b7nouFztL9Ubg7G9F76YeaVzjflG21GVpvDU42UGFHiubjUulicBCrw8QfEwVwlQ4LmKW7rFKPCki8z5hinwnGfMFawToMCzzszqGekXWLS8/W/EojtRM3IuakfNs1pCu+NVFngwBZ6PAk+6Pe2FwOsNvY2WsiVZrIb1z0Nt7GDp+DnVsJEykHrTFJyJ3Y8p8I+ogM/8R4cPBZ4OKcs3IwWefJnp0DEFng4pFz4jBV7h7JQ9kwJP2WiFGCyaSGBlRzd6zb/EHFRdibEVZXn1RYGXFyYe5DIBZQVemiNvoXV5N9mzHAWePRztrmJEY0it78wuWxZCYPIwu5fab72Nr+zEkjteQ09rP2adOBbv+fB0+APuXf1HgedKzFzEIgEKPIvAeLgrBCjwXMEs7SIUeNJG51zjFHjOsdW9cty8AuG+TVsRie++Le/E0SMwoTL3bVYUeLrvHjHnV1rgmcj5Egsx991QXXkh8FKIY3vl4xjw73kL7SnmLbT5/YJGPsrWO/byCry+7gH8/hNPIjGw++q/Yz41E4d8YLL1QQo8gwKvQHA8zVECFHiO4mXxAglQ4BUITpPTKPA0CdrKmBR4VmjxWCsEmqN9eGTrjqxTJlVV4PhRDTnLUODlRMQDPCCgusDzACmXLJKAFwIv3fK7L7GAP4WK2Hj4ESpyEvVO9+oZeBuWteDBa5dmAR2/oAHnXHuEa5Ap8FxDzYUsEKDAswCLh7pGgALPNdRSLkSBJ2VszjZNgecsX52rd8Xi+OvGrVkI5tXVYuHw2pxYKPByIuIBHhCgwPMAOpcckoBXAo+xiEsg2jWA2z7pzRV4SfMFJ23BVxEqSaIJsxHtDYoLip1pR4ACT7vIpRiYAk+KmDxrkgLPM/TiLkyBJ242KnT2SnsnlrXteg5QQ2kJTmkagRJ/IOdoFHg5EfEADwhQ4HkAnUtS4HEPWCaw5zPwph/XhGM+MRN+h9+Am0QMKyt+j0igeXCtUlRjdtenETaqLffPE0jACQIUeE5QZc1iCVDgFUtQ7fMp8NTOt6DpKPAKwsaTLBDoTyYRNV9iMawkjHwfoU2BZwEwD3WNAAWea6iFW6ht3UvYtORuxAeiGDXneIw78lwheuQVeELEwCZMAjtDK/F62V8HWaQFns/8D/7Y3hMxNnYM+ZCAEAQo8ISIgU3sRYACj1tiKAIUeNwf+xCgwOOmEJEABZ6IqbAnCjw990BfVwuW3/4NGKndLwWYespnMWLm0Z4DocDzPAI28A4BCjxuBdEJUOCJnpCe/VHg6Zl7vlNT4OVLSqPjKPA0CluiUSnwJApLo1Yp8DQKe49Rd6xZjHX/uiVr+FFzTsDkEz/hORAKPM8jYAPvEEiYt9CuqrwNEf+2wSvwylCDgwdvoa0iIxIQggAFnhAxsIm9CFDgcUsMRYACj/tjHwIUeNwUIhKgwBMxFfZEgafnHuAVeHrmzqmtE0i/xKI99CqCYb7Ewjo9nuE0AQo8pwmzfiEEKPAKoabPORR4+mSd96QUeHmj4oEuEqDAcxE2l8qbAAVe3qiUO3DwGXjP3ot4X4TPwFMuXQ5kN4HykgBCQT+6euN2l2Y9EiiYAAVeweh4ooMEKPAchKtAaQo8BUK0ewQKPLuJsp4dBCjw7KDIGnYToMCzm6i1egPdA4hs70bpsDJUNFRaO1nRo3kLraLBSj4WBZ7kASraPgWeosFKPhYFnuQBOtw+BZ7DgGUsT4EnY2rq9+yUwNu05nWsX74C5TXVOPjY96C8ulp9mJzQNgIUeLahtFyoe3Mntj2/yXyRhDF47vBZIzHi4FGW64h6Qm+sH79b+Q88u+01TKodjc/OPR0TakbmbJcCLyciHuABAQo8D6BzyZwEKPByIuIBHhCgwPMAukRLUuBJFJZbrVLguUWa61gh4ITAS8u7p+78c6aNquF1OOurX0AoHLbSGo/VmAAFnnfhb3hiLfrbopkGfObtedPOORg+n8+7pmxc+caXHsDjb7+cqTiyYhh+d/JX4Pf5h1yFAs/GEFjKNgIUeLahZCEbCVDg2QiTpWwjQIFnG0olC1HgKRlrcUNR4BXHj2c7Q8AJgbfoz/diw4pVWQ2f9oXLMGL8OGeGYFXlCFDgeRfp3gLPbwq8qR+YhfT/qvD57GM3YktPa9YovznpSxhb3UCBp0LAms1AgadZ4JKMS4EnSVCatUmBp1ngFselwLMITIfDKfB0SFm+GZ0QeC8/+gRWPrUoC8YHr/gyahrq5QPEjj0hQIHnCfbBRfe+hbZ+5kg0zFbnFlpegefd3uLK9hOgwLOfKSsWT4ACr3iGrGA/AQo8+5mqVJECT6U0bZqFAs8mkCxjKwEnBF5/by+euP0utG7aAn/Aj3knHY85xx9ra98spjYBGQXeQG83tqx+ASUV1WiauRA+v7xXrL37Eosy8yUW5Yq9xCL9DLxfr3gYLza/MfgMvM/MPQ0Ta3ILSt5Cq/b3HFmno8CTNTm1+6bAUztfWaejwJM1OXf6psBzh7NUq1DgSRWXNs06IfDS8AzDQOeOnSirqkBpRYU2PDmoPQRkE3iR9hY8efP3EItGBgE0TJyBYz/1Xaklnj1JqlOFAk+dLFWahAJPpTTVmYUCT50sVZqEAk+lNO2fhQLPfqbSV6TAkz5CJQdwSuApCYtDuUZANoG38p9/wRuLH8ri877PfR/14w5yjRkXcpYABZ6zfFm9MAIUeIVx41nOEqDAc5YvqxdGgAKvMG66nEWBp0vSFuakwLMAi4e6RoACzzXUXMgCARUEXvoKvBGTZ1mYmoeKTIACT+R09O2NAk/f7EWenAJP5HT07Y0CT9/s85mcAi8fSpodQ4GnWeCSjOuUwDPM+dv7Yygz31xZHgxKQoNtikJANoG39y209ROm4bjLruIttKJsKBv6oMCzASJL2E6AAs92pCxoAwEKPBsgsoTtBCjwbEeqVEEKPKXitGcYCjx7OLKKvQScEHj9ySQe3rIDzdG+wWaPbKjDwvph9jbOakoTkE3gpcNIv8Ri08rnzec+1qJxxgLzBS4U1yptUgo8ldK0Z5a1eA6LAr9Dv68XC5Jn4SjjEnsKW6hCgWcBFg91jQAFnmuouZAFAhR4FmBpeCgFnoah5xqZAi8XIX7dCwJOCLzndrZjWWtH1jiXThqHYSUhL0bkmhISkFHgSYiZLVsgQIFnAZYGh7ZhM/4QvAwpJDPTnp68EgcbJ7k6PQWeq7i5WJ4EKPDyBMXDXCVAgecqbukWo8CTLjLnG6bAc54xV7BOwAmB97dNzdjUG81q5uSmkTioutJ6gzxDSwIUeFrGLvTQFHhCx+N6c8v9D+Mx/41Z684zTsfJya+42gsFnqu4uVieBCjw8gTFw1wlQIHnKm7pFqPAG8HX/wAAIABJREFUky4y5xumwHOeMVewTsAJgbe+pxePbNmeaaYqFMTFE8ciHPBbb5BnaEmAAk/L2IUemgJP6Hhcb45X4LmOnAtKRIACT6KwNGqVAk+jsAsYlQKvAGiqn0KBp3rCcs7nhMBLk9gYieLVzh6UhwJYOLwWFXyRhZwbxKOuKfA8As9lD0iAAo+bY28CfAYe9wQJ7J9APgIvFTGfkxyNwT+ihhhJwBUCFHiuYJZ2EQo8aaNzrnEKPOfYsnLhBJwSeIV3xDNJAKDA4y4QjYCuAm9LywDe2jyAiU0lGDeqRLRYtO+Ht9BqvwWEBJBL4PU/uhyxRasHew9OHInSj74Pfj4nWcgsVWqKAk+lNO2fhQLPfqbSV6TAkz5CJQegwFMyVumHosCTPkLlBtBR4C1Z3oP7n2rPZPmB44bh2EOqlctW5oEo8GROT93ehxJ4yW3t6L3pkazhw6fMR+mxB6sLhJMJQYACT4gYhG2CAk/YaLxrjALPO/Zc+cAEKPC4O0QkQIEnYip696SjwLvmt1vR05vIBF9VEcS1n20SaiO0+KJo9fWjyahEjREWqjc3mqHAc4My17BKYCiBF1v6JvofXJpVMjRnAsouOtrqMjyeBCwRoMCzhEu7gynwtIs898AUeLkZ8Qj3CVDguc+cK+YmQIGXmxGPcJcABR4gmsB7KdCKNf62wY2QfkXS0clGjE9VubsxPF6NAs/jACRf3ogPIPbkPUi8tQL+hiaUHHee+Uy6cUVPNZTAM/rjiPzibzB6zGfgpT8+H8o/dSKCk0YVvS4LkMBQBCjwuD+GIkCBx/2xDwEKPG4KEQlQ4ImYCnuiwOMeEI2AjgJv71tozzm+Du+dL4YgS8DAPeF1SBqpzFYZbpTh9ETx8kG0vTdUPxR4MqUlXq8DT/0V8eVPZRrzVVSj4tM/Mo14oKhmcz0DL9UeQeyZ12BE+hE6dCqCUyjvigLOk/MiQIGXFyZtD6LA0zb6Aw9OgcdNISIBCjwRU2FPFHjcA6IR0FHgpTMQ9SUWcaRwd2itqfF2fyjwRPu3hv0MRSBlGNjZ1YdhlaUIB9PXkLr/id7+faTad2QtXP6xa+AfXpxQyyXw3J+UK5IAQIHHXTAUAQo87o99CFDgcVOISIACT8RU2BMFHveAaAR0FXii5bBnP8vNW2hX7XEL7TGJJowzn4Wn04dX4MmZ9vbOKO57fj16ojGUhAM485AJmDK6xvVhvLoCz/VBuSAJmAQo8LgNKPC4BywRoMCzhIsHu0SAAs8l0FzGEgGdBV48lcTSlo3YGu3C1OoGzBveBL/5jCDZPs0J8y+oPRuwMdGDWeE6nFs1EVX+kGxjZPqlwBMzOr7EIoCQefVWV29czIDY1X4J/GnRm9jaFsl8rbIsjMtPdf8trF48A49bggS8IkCB5xV5OdblFXhy5ORqlxR4ruLmYnkSoMDLE5Tkh7W9sQlvPbgY/Z0RNB4xC5PPeA98AW9u2ckHpc4C7/82rMDrnbtvaTpy5ESc0Dg1H2xCHfPD9uXYHo9melpY2oCP1hwkVI9WmqHAs0KLx7pFgFfguUXa3nX++6EVGIgns4p+8bTZqCiV95ccew7DW2jt3S+sZg8BCjx7OKpahQJP1WSLmIsCrwh4PNUxAhR4jqEVpnCstw/PXP17pGK7r9CYes6xGPe+Q4Tpce9GdBV46avvbljxlPlcr91P9qovrcBnZ7xH2Kz211h3Kobv7nwx60tVgTB+VH+oVHPs2SwFnrTRKd04BZ6c8f579TYsfXN7pvlpjbX44BGT5BxmP11T4CkTpVKDUOApFaftw1Dg2Y5U/oIUePJnqOIEFHgqppo9U9uaDXjltw9k/WHdjPGY//lzhR1eV4GXDuTG1YvRE+/PZDOmchg+NlU+8cUr8IT914uNKUSAAk/OMNMvsFi+vhUbWrrROKwCh0xpQEmwuDe/ikSCAk+kNNjLuwQo8LgXhiJAgcf9sQ8BCjxuChEJUOCJmIq9PcUiUTxzTfoKvESmMK/As5exndXe6m7F/RtXYSARR3W4DOdPnIvR5dV2LuFKLT4DzxXMXERzAhR4mm8AQcenwBM0GM3bosDTfAPkGJ8Cj/uDAo97QAoCFHhSxFR0k3s+A2/UoTMw9eyj4fPzGXhFg3WoQMy8lba9vxcjyqqkfIGFQ1g8LctbaD3Fz8UPQIACj1tDRAIUeCKmwp4o8LgHhiJAgcf9QYHHPSAFAQo8KWLSrkmdb6HVLmxJBqbAkyQozdqkwNMscEnGpcCTJCjN2qTA0yxwi+NKKfAu/Nx1eH3tRsDnGxy3urIcix/45eD/f1tbn0UEPHxvAryFlntCRAIUeCKmwp4o8LgHRCNAgSdaIuwnTYACj/tARAIUeCKmwp4o8LgHhiIgpcA7/cPfwo3XfRFTJjbtMxsFXvEbngKveIasYD8BCjz7mbJi8QQo8IpnyAr2EqDAs5cnq9lDgALPHo6sYi8BCjx7ebKaPQQo8OzhqGoVKQXesed8Gffccg1GNdRR4DmwMynwHIDKkkUTUFXgGdEEkut6gbiBwIQy+IaXFM2KBdwjQIHnHmuulB8BCrz8OPEodwlQ4LnLm6vlR4ACLz9OPMpdAhR47vKWbTUpBd7891+GYw6fg+Wr16K+rgZfuew8HHPE3EH2vAKv+C1IgVc8Q1awn4CKAs+IpZBY1AqjP7kLmPlUgOARdfDXU+LZv4OcqUiB5wxXVi2cAAVe4ex4pnMEKPCcY8vKhROgwCucHc90jgAFnnNsVagsrMB7fd0mJJLv/KX2HdKhYBBTJ47BVTfchlPedziOXDgTi55dgW/96BY89McfD16R19o1oEIuns5QWhJAwHy+YG9/wtM+uDgJ7EmgqiKEgYEkYomUMmASW/owsKwja57ghHKUzKtVZkbVBxleXYL2ngEYhuqTcj5ZCFSVh1AS8qMnGsdAXJ3vl7LwZ5/7J1AaDiAY8CHSx58tuUfEIVBpfr9MmD9X9sey/84pTofsREcCddVhdEbiSKX4w6WO+eeaub7G3gs9fIb5ybVoPl//3k9uQ19/toyrra7EVV/9yD6nf/yr/4VzTz8WZ5x4pFJ/uc+HkxPHmD9fDb4gJMlvGk7gZc0CCQT9PqTMby8qbct46wA6Ht+RRaRibi0qZlYXSImnuU0gFPQP/vBvy3/43G6e6ylJIC1J/OZ/wxPJ9PdL7kwlQ5ZwKPM/4eaPlvzZUsLolG5ZxZ8tlQ5Mk+FCAfNnyyR/ttQkbstjhs2/e9j5sU3gHaipaN8A1m7YgrkzJ2cOufTyH+LD570fJx93KG+htSFN3kJrA0SWsJ2AirfQpiElX+sxn4EXGeTlqw8jeGgdfMFdb9jmR3wCvIVW/Ix069DuW2g3R5dic99SVARGYlr1qSj18xcMuu0pO+blLbR2UGQNuwnwFlq7ibKeHQR4C60dFNWtIewttAdC3tkVwUkXfgM3/uByHLXwYDy9dCWu+MFv8cif/gvDh1VT4NmwVynwbIDIErYTUFXgDYIyn4FnmC+x8FUFbefGgs4SoMBzli+rWydgp8Db0LsYL7b/LtNEbXg8Thx5LfwIWG+MZ2hNgAJP6/iFHZ4CT9hotG6MAk/r+HMOL53AS0+06LkV+Nlv7kZLWyeaRtXjyi9chCMWzBwcli+xyJl5zgMo8HIi4gEeEFBa4HnAk0vaQ4ACzx6OrGIfATsF3uKdN2B7/6qs5k4Z9RNUhxrta5iVtCBAgadFzNINSYEnXWRaNEyBp0XMBQ8ppcAbaloKvIL3QuZECrziGbKC/QQo8OxnyorFE6DAK54hK9hLwE6Bt6z991jfuyjToM98VfZZTb9Gib/S3qZZTXkCFHjKRyzlgBR4UsamfNMUeMpHXNSAFHhF4VPzZAo8NXOVfSoKPNkTVLN/Cjw1c5V5KjsFXm+iFUvafoGu2GbzBQRBzK+9GFMqT5IZD3v3iAAFnkfgueyQBCjwuEFEJECBJ2Iq4vREgSdOFsJ0QoEnTBRsZA8CFHjcDiISoMATMRW9e7JT4KVJGkYKnfEtqAzWI+Qv1xsupy+YAAVeweh4ooMEKPAchMvSBROgwCsYnRYnUuBpEbO1ISnwrPHi0e4QoMBzhzNXsUaAAs8aLx7tPAG7BZ7zHXMFHQhQ4ImVcl+0DQMDEdQOGy9WYy53Q4HnMnAulxcBCry8MGl7EAWettEfeHAKPG4KEQlQ4ImYCnuiwOMeEI0ABZ5oibCfNAEKPHH2wfPP/hKvrrp/sKHGpkNw/MnXIxwqE6dBFzuhwHMRNpfKmwAFXt6otDyQAk/L2IceWnaBt71lJ2774914fe1bmD1zOj7ziUtQU1XFpCUnQIFnb4AJJPFUyfN4NfQWapPVOGHgcIxNjbZ3EQ2qUeBpELJkI1LgSRaYJu1S4IkR9Pbtq/CPv30xq5mFh38Gc+ZdJEaDLndBgecycC6XFwEKvLwwaXsQBZ620R94cNkF3pVX/Qhvvb0xM+DCBXPx7a9+nklLToACz94Anw4vw5Lwy5mipUYJvtB7EcLm//GTPwEKvPxZ8Uh3CFDgucOZq1gjQIFnjZdTR69c/mcse+F3WeUnTT4ex514tVNLCl2XAk/oeLRtjgJP2+jzGpwCLy9Meh0ks8Dr6OrCZV/8pvnQbSMTWnl5Gf50y//oFaKC01Lg2RvqHWUPojnQklX0w9EPYExqpL0LKV6NAk/xgCUcjwJPwtA0aJkCT4yQ+/o7cf/dHzaff9cz2JDP58NpZ96IkaPniNGgy11Q4LkMnMvlRYACLy9M2h5Egadt9AceXGaBl56KV+Cpuakp8OzNde8r8ErMK+8uj1zMK/AsYqbAswiMhztOgALPccRcoAACFHgFQHPolK7OzVi96l7EB3oxY9bZ2sq7NF4KPIc2GcsWRYACryh8yp9Mgad8xNYHlF3gpZ+Bd+vtf8ba9W/zGXjW4xf2DAo8e6OJI4F/lyzlM/CKxEqBVyRAnm47AQo825GyoA0EKPBsgMgSthOgwLMdKQvaQIACzwaICpegwFM43EJHk13gFTo3zxObAAWe2Pno2h0Fnq7Jizs3BZ642ejcGQWezumLOzsFnrjZ6NwZBZ7O6eeenQIvNyPtjqDA0y5yKQamwJMiJu2apMDTLnLhB6bAEz4iLRukwNMyduGHpsATPiItG6TA0zL2vIemwMsblT4HUuDpk7VMk1LgyZRWYb2+8cYOrFi+CSnzJTRz543FjBmjCyvk4lkUeC7C5lJ5EaDAywsTD3KZAAWey8C5XF4EKPDywsSDXCZAgecycMmWo8CTLDA32qXAc4My17BKgALPKjG5jt/R3IWHH16Z1fSpp89BY2ON0INQ4DkTj2EkYaQ2w+dvMN+SWOHMIopWpcBTNFjJx6LAkzxARdunwFM0WMnHosCTPECH26fAcxiwjOUp8GRMTf2eKfCcz7ilJzm4yIiqgPOL7bXCilc2Y9mLb2f96ey5Y3DYYRNd78XKghR4Vmjld2wqtQPx2I9hJDfCQBDh8OcQCB2f38k8ChR43AQiEqDAEzEV9kSBxz0gIgEKPBFTEacnCjxxshCmEwo8YaJgI3sQoMBzbjukDODWxb14dm3/4CJHTC7BZ4+rhN/n3Jp7V97e3IlHHl6V9cennT4boxtr3WuigJUo8AqAluOU2MB/I5VYnDkqLfFKy/9kXolXav9iClakwFMwVAVGosBTIEQFR6DAUzBUBUaiwFMgRAdHoMBzEK6spSnwZE1O7b4p8JzL94X1A/jVU5GsBS4/vhKHTSpxbtH9VN7zGXhz5ozBzFmNrq5fyGIUeIVQG/qcWN/lSKW2Zh0UKv0xAoHp9i+mYEUKPAVDVWAkCjwFQlRwBAo8BUNVYCQKPAVCdHAECjwH4cpamgKv8OQSb+/EwL9WmVeKAOFT5iI4bnjhxXhmFoH0X0r7Ygn0x1IkYzOBe16I4pGVfVlVT59Thg8dVm7zSuqVo8CzP9NE/B9IxH6XKewPHISwKfAAv/2LKViRAk/BUBUYiQJPgRAVHIECT8FQFRiJAk+BEB0cgQLPQbiylqbAA1JdcSTWReGrDSI0yXyAeh63Eia3daD7qr8CA4ld0ZeEUH39+QiMFvsWQFn2KQWec0k1dyZx9d+6MBA376U1P+GgDz84uwaja91/Fp5zUzpTmQLPGa7JxH+QTCw1X2IxGsHQuXyRhQXMFHgWYPFQ1whQ4LmGmgtZIECBZwEWD3WNAAWea6ilXIgCT8rYnG1ad4GX2NaH3ru3wXjnSq/QzCpUnDUqJ/T+v7+Mvnufzzqu/OPHouSEWTnP5QG5CVDg5WZUzBEb2xJ4fE0/0s/DO/ngUowfHiymnDbnUuBpE7U0g1LgSROVVo1S4GkVtzTDUuBJE5VWjVLgaRW35WEp8CwjU/8E3QVe9G/bEXutJyvoqsvGIzA8PGT4saXr0HvTY1nHVHzx/QgfPkX9TePChBR4LkDmEpYJUOBZRsYTHCZAgecwYJYviAAFXkHYeJLDBCjwHAbM8gURoMArCJs2J1HgaRN1/oNS4BUm8Azz0qXeXz2G+AtvDcIOv3cayj99vHkLWB733+Yfj7ZHUuBpG73Qg1PgCR2Pls1R4GkZu/BDU+AJH5GWDVLgaRm78ENT4AkfkacNUuB5il/MxXUXeIkt5i209+6+hTY8owrlH8h9C+27aSZ3dpvPa/LBX18lZsCSdkWBJ2lwirdNgWdfwNE3V6LrmUcRqKrBsPedjdDwkfYV16gSBZ5GYUs0KgWeRGFp1CoFnkZhSzQqBZ5EYXnQKgWeB9BFX1J3gZfOp5CXWIieq+z9UeDJnqCa/VPg2ZNrdO0qbL7pu4Cx60Uqgeo6TPj2LxGsrLFnAY2qaCXwzO2SfGUAaE/BPz8MXx1fvCPqVqfAEzUZvfuiwNM7f1Gnp8ATNRkx+qLAEyMHobqgwBMqDjbzDgEKPG4FEQlQ4NmTyo57bkbnkkezijV+7ApUHXKMPQtoVEUngRe7pRvJF/oR708gGTJQ/s1hKJlRrlHa8oxKgSdPVjp1SoGnU9ryzEqBJ09WXnRKgecFdcHXpMATPCBN26PA0zR4wcemwLMnoLYn7kPr3+7IKjbu6z9D2YRp9iygURVdBF6qOYn+77Yh0h5FMpEaTLi7KYbxP5+I0soSjRKXY1QKPDly0q1LCjzdEpdjXgo8OXLyqksKPK/IC7wuBZ7A4WjcGgWexuELPDoFnj3hJPv7sO3WHyB9K236U3fSeWg466P2FNesik4CL3LFDvR29mcSbm+IouSz1Zhy+DjNUhd/XAo88TPSsUMKPB1TF39mCjzxM/KyQwo8L+kLujYFnqDBaN4WBZ7mG0DQ8Snw7A0mtn0z/BVVCFbV2ltYo2q6CLx0pG3XNSP2Qt9gukl/Cm/M3YnxZzRR4Am43ynwBAyFLYECj5tARAIUeCKmIk5PFHjiZCFMJxR4wkTBRvYgQIHH7SAiAQo8EVPRuyedBF4ynsKKn7+K+PYYOob3IVAfxDEfmY+SirDem0DA6SnwBAyFLVHgcQ8ISYACT8hYhGmKAk+YKMRphAJPnCzYyW4CFHjcDSISoMATMRW9e9JJ4KWTTj//bvvaViRMmdd40HCESkN6bwBBp6fAEzQYzdviFXiabwBBx6fAEzQYQdqiwBMkCJHaoMATKQ328i4BCjzuBREJUOCJmIrePekm8PROW57pKfDkyUqnTinwdEpbnlkp8OTJyotOKfC8oC74mhR4ggekaXsUeJoGL/jYFHiCB6RhexR47oUej0QQ7+pC2ejR8Pn97i3s8Upvt7+ExetuQXf/TsxuPBVHT/4k/L7AkF1R4HkcGpffLwEKPG4MEQlQ4ImYijg9UeCJk4UwnVDgCRMFG9mDAAUet4OIBCjwRExF754o8NzJf8d/nsLG+/8KI5FAedNYTL/8ywjV1LizuIerRONduOWZDyGRHMh08b6pn8fCcedT4HmYC5cujAAFXmHceJazBCjwnOUre3UKPNkTdKB/CjwHoLJk0QQo8IpGyAIOEKDAcwAqSxZFgAKvKHx5nRzv6sTy71wJwzAyx4885jhMuPCSvM6X+aD1rUtx34pvZY0wYfihOH/eDRR4Mgerae8UeJoGL/jYFHiCB+RxexR4Hgcg4vIUeCKmwp4o8OzdA75oP4Lrt8EXTyIxbgRSw9W/csRegruqUeA5QZU1iyFAgVcMvfzO7VyzCm/c/MusgysmTMTBV34nvwISHZVIpfDYhlexuq0Z0+tG4rixY3DbcxfzCjyJMmSrByZAgcfdISIBCjwRUxGnJwo8cbIQphMKPHuiiPQCUfOfhgbA57Onps5VKPBsTD8WR9mSlUB/bFdRc38OHDaTEq8AxBR4BUDjKY4SoMBzFO9g8ZR52+zqn1yPvq1bM4tN/tgnUX/YEc4v7vIKt65YgifffiOz6vHjD8LJ48ozz8CbOeokHDf1s+Yz8IZ+BiCfgedycFwuLwIUeHlh4kEuE6DAcxm4ZMtR4EkWmBvtUuAVT3nJEuCpp3b9MNvYBFxycRJlZbR4xZClwCuGXva5gW2tCL+yNusPE+NGIn7wJPsW0aQSBZ4mQUs0JgWeO2HFIz1ofvxfGGhtRd3CQzF8/iHuLOzyKp/+11/Q1R/NrBoOBHHn6R8xhZ21n2ko8FwOjsvlRYACLy9MPMhlAhR4LgOXbDkKPMkCc6NdCrziKLe1ATffnP2b6Pe+Fzj++FRxhTU/mwLPvg3g6+xB6bOrswrGpo9DcpJpm/mxRIACzxIuHuwCAQo8FyBrtMTXnroPW3s6MxM3VdXiF8efa5kABZ5lZDzBBQIUeC5A5hKWCVDgWUam1QkUeFrFnd+wFHgwH0wNtKwYQH97CqMWhFFSG8gPnnnU6tU+3H9/9m+mJ082cMklux92nXcxHpghQIFn72YIvbEJwbd23f6VqqvGwMIZQHDoW6Ds7UCNahR4auSo0hQUeCql6f0sr7U242cvPoVIrB+V4VJ849DjMaN+tOXGKPAsI+MJLhCgwHMBMpewTIACzzIyrU6gwNMq7vyGpcADlt3YgZ2rdj0fLFjux+HfqEX1uFBeAAcGgFtv9aOjY/fhH/pQCtOm5XU6DzoAAQo8B7bGQNx8iUUCRmWZA8X1KEmBp0fOMk1JgSdTWnL0Gk8lsaWnA40VtSgJBgtqmgKvIGw8yWECFHgOA2b5gghQ4BWETZuTKPC0iTr/QXUXeJHmBJ6+yrwPdo/P6ENLMe8z+b+ls6cHWPKMH70RA/PmAVOm8Oq7/Hfg/o+kwCuWIM93ggAFnhNUWbMYAiIJvGg0hv+99wUsW74J48bW4dILD8e4McOKGY/nSkqAAk/S4BRvmwJP8YAlHY8CT9LgXGqbAs8l0DItQ4G3r8BrOqoMcz5RLVOMyvVKgadcpEoMRIGnRIxKDSGSwLvjrufw78W732BaN7wSP//hufD7rb0AoZCAWto78PCiJWg2XzIxc9JEnPreI1FaUlJIKZ5jAwEKPBsgsoTtBCjwbEfKgjYQoMCzAaLCJSjwFA630NF0F3hpbi/d1Dn4DLz0J1Dqw2Ffq0XtpHChSHmeDQQo8GyAyBK2E6DAsx0pCxZJQCSB962rH0Dz9q6siX587QfRODr/K9oLwZFKGbjxrr+go9u8HP6dz4IZ03D2CccVUo7n2ECAAs8GiCxhOwEKPNuRsqANBCjwbICocAkKPIXDLXQ0Crzsl1iMnBdGaV3+L7EolDvPG5oABR53iIgEKPBETEXvnkQSeHtfgddQX4kbrnf+Cryd7Z246X/vydoI9cNq8aVLPqT35vBwego8D+Fz6QMSoMDj5hCRAAWeiKmI0xMFnjhZCNMJBZ4wUbCRPQhQ4HE7iEiAAk/EVPTuSSSB59Uz8HgFnnj/DlDgiZcJOwIo8LgLRCRAgSdiKuL0RIEnThbCdEKBJ0wUbIQCj3tAcAIUeIIHpGF7Igk8L/HzGXhe0t93bQo8sfJgN7sIUOBxJ4hIgAJPxFTE6YkCT5wshOmEAk+YKNgIBR73gOAEKPAED0jD9ijwNAxdgpEp8CQIScMWKfA0DF2CkSnwJAjJwxYp8DyEL+rSFHiiJqN3X7yFVu/8RZ2eAk/UZPTtiwJP3+xFnpwCT+R09O2NAk/f7EWenAJP5HS8740Cz/sMhOuAAk+4SDxvyDBi8CdfgpHaDvgnAcE5Zk8+V/uiwHMVNxfLkwAFXp6geJhrBCjwXEPNhSwQoMCzAIuHukaAAs811FzIAgEKPAuwNDyUAk/D0HONTIGXi5AmXzcMBHf8C8HWpxEfVYJURWlG2hmho0yJ9x5XQVDguYqbi+VJgAIvT1A8zDUCFHiuoeZCFghQ4FmAxUNdI0CB5xpqLmSBAAWeBVgaHkqBp2HouUamwMtFSI+vhzfehZINv0cqEMDAtIkwguVIlY3dNby/DkbJJ10FQYHnKm4ulicBCrw8QfEw1whQ4LmGmgtZIECBZwEWD3WNAAWea6i5kAUCFHgWYGl4KAWehqHnGpkCLxchPb5esexT8EfWmcMa6Js+xZR2PiQrzf/1BWD4JwIl57kKggLPVdxcLE8CFHh5guJhrhGgwHMNNReyQIACzwIsHuoaAQo811BzIQsEKPAswNLwUAo8DUPPNTIFXi5Ceny9bOU3EWxfOjhssroSA02jkKqeBsNXDSN8Lnz+eldBUOC5ipuL5UmAAi9PUDzMNQIUeK6h5kIWCFDgWYDFQ10jQIHnGmouZIEABZ4FWBoeSoGnYei5RqbAy0VIj6/7e99G2apvwt+/A0agBH0HfR3JhrnmlXgNJgC/6xAo8FxHzgXzIECBlwckHuIqAVkFXiqRQttLrejd0ouq8VWomz/cvODb3ZcluRqUZotR4GkWuCTjUuBJEpRmbVLgaRa4xXEp8CwC0+FwCjwdUs5zxlQS/t63zGffjTFfWlGe50nOHEaB5wxXVi2OAAVecfzBEgC7AAAgAElEQVR4tv0EZBV4mx/ehM5V7Rkgwxc2oPGkJvsBsaInBCjwPMHORXMQoMDjFhGRAAWeiKmI0xMFnjhZCNMJBZ4wUbCRPQhQ4HE7iEiAAk/EVPTuSUaBZ6QMvPqLVUjFU5nwghUhzPjSLL3DVGh6CjyFwlRoFAo8hcJUaBQKPIXCdGAUCjwHoMpekgJP9gTV7J8CT81cZZ+KAk/2BNXrX0aBl07hjd++hljHQCaQspHlmPKJg9QLSNOJKPA0DV7wsSnwBA9I0/Yo8DQNPs+xKfDyBKXTYRR4OqUtz6y6CjyjfQCpLX0wEuaTB0eG4W/y9lZmeXbM0J0a5tuV1/Y8hK39z6I+PAPTqy5AyF9meTwKPMvIeILDBGQVeD1v92DzgxuR7EsgVBnG2A+MQ8W4SodpsbxbBCjw3CLNdawQoMCzQovHukWAAs8t0nKuQ4EnZ26Odk2B5yheFi+QgI4Cz+hLIrmqE6Zrynz8kyrM94iUFkjR+dOinV145dEn0Nfdg4OOOhxjD57h/KIFrLCq60680nlr5szRZYfixBH/Y7kSBZ5lZDzBIQLJNzuQfK0DdUeMRvXMOnREYugbSDq0mjNlk+YttLH2fpQML4M/yBdYOEPZm6oUeN5w56pDE6DA4w4RkQAFnoipiNMTBZ44WQjTCQWeMFGwkT0I6CjwUjvMq+/ejmTtA9/IUgQmVAi5N+J9/bjvBzcg0rb7QfQnfu6TmDBvtnD9/n3bxeiKb8zq67wxf0dZYLilXinwLOHiwQ4RiP91HWJ/fH2weiAYQMM3FyBxbKN0As8hPNKXfcuMdtN6P6bPTmH0WDnHocCTMzfVu6bAUz1hOeejwJMzN7e6psBzi7RH6yQTSbz62EpsXrUJo6c3Yvap8xEMB4fshgLPo7C47JAEdBR4sl2Bt3n1a/jXTbdk5Thp4Xwcf9lHPdndbRtiaF7Vh5EzStEwtSSrhydbvoFtfc9l/izoL8eFY/4Fn89vqVcKPEu4eLATBMwXQEQ/+gSMzl3PjwsE/CgZX4Xy3x5HgecEb5drPv6gHw/dHRhc1WdelHjp5xM49Og9Lst2uZ9Cl6PAK5Qcz3OSAAWek3RZu1ACFHiFktPjPAo8xXP+z61PDAq8dz8HHTsTJ37xlCGnpsBTfFNIOp6OAi8dlUzPwOva0YK/Xv2jrB0279STsPDs013fdW88FsGzv26F8c7fcw/7RB0O/kB1po/O2Nt4cufXEU1sRzhQiSPqvo3x5cdZ7pMCzzIynmA3AVPg9V7wKPDO7bIUeHYD9q5eynwp7xUfDyG++90eGNkIfPcXce+asrByLGpg80oDsV5g3Ew/GqeE0NUrR+8WxuShEhOgwJM4PIVbp8BTOFwbRqPAswGiyCXu+NQtiHaaPzm98wmUBHHZny6H33/gq0wo8EROVN/edBV4siX+yj8fx7IHHxlsu2HieJzypc+gpNz9F2/c/4Wt6Nyy+y+KZbVBXHTnmCycKSTRGXsLVcFx5gssCnuuIAWebDtUzX7jD65H7LZXB4dL30I74juHIP7e0bwCT/K4ZRZ4KfPxi8v+msDAO0+B8Pt9OOS0MEpHyfVcRsm3ENvPQYACj1tERAIUeCKmIk5PFHjiZOFIJ/decRdaN7RkateMqsUlv/rEkGtR4DkSBYsWSYACr0iALp7e19ODvp4Iho0eZd7y5c2D6PcWeKU1AVx4xxjzlxf29kOB5+LG4lJDElDhJRaMeF8Cst5C270jhRUPmZcQvvNJf+9tnBrAxKOZMgmIQ4ACT5ws2MluAhR43A1DEaDAU3x/bHt1Cx77738g2hFBaVUZTvrq6Rg7ZxwFnuK5qzgeBZ6KqTo3U65baO1amQLPLpL7r7OmdxFW9/8HfsOHBZWnY3LpIc4uqED19PfKspKAlG+hVQC/IyPI+BKLgYiBF+9JZh5jkBZ4E+cH0Thfvuf3ORIqiwpBgAJPiBjYxF4EKPC4JSjwNNgDKfNBT80dUQyrKEG5eZvsnp9kPIn2La2obaxDqCSUkwavwMuJiAd4QIACzwPoki+ZeYnFdPMlFgdlv8TCrtEo8OwiuW+dLQOv4rHO7JeinFX3ddSHhv4llHMdyVGZAk+OnHTocvPKJDYtM5C+FbhmpB+HnhnGABI6jC70jPG+FLYt7hrssenoagTLd70kRccPBZ6OqYs/MwWe+Bl52SGvwPOSvk1rt0f6cNNjq7G1PYKA+RvOCw6fivfNaiq4OgVeweh4ooMEKPAchMvSBROgwCsYXc4TX+z5O1ZFn8w6bmHlGZhTcVLOc3U+gAJP5/TFmz3eb77EIgrUjwoiHPLzJRYeRxSLJLHoi+sQ2bLrzShVY0twzI2TEa7K/uW/x226tjwFnmuouZAFAhR4FmBpeCgFngKh//Hp17HkjebMJGmJ918XHoma8sKuOKHAU2BTKDgCBZ6CoSowEgWecyHu9wq84d9AfXCsc4sqUJkCT4EQFRyh3LytOxSkwPM62vUPtWHFL7dmtTH3S02YdOZwr1vzZH0KPE+wc9EcBCjwuEWGIiC0wGvv7MG3fngLtu/swN/v+GFmjs3bWnDVDX/AG+s2oXFUPb775Q9jweypg1/f1tanXeJX/99SbO80f725x+dLJ8/BwWML+48xBZ52W0iKgSnwpIhJuyYp8JyNfM9n4M2rOBlTyw53dkEFqlPgKRCigiNQ4IkRKgVedg4UeGLsS3aRTYACjztCSoHXG+3HRZ+7DsceOQ+Lnl+RJfA++uUf4/j3LsCl55yEZ5etMWXebXj8np+bv9kLaCnwnnmzGXcufj2Tc2NdBa7+4KHwF/j2Rwo8ftMQkQAFnoipsCcKPIf3QCIKo2054A/DV78A8On7rKZ8SVPg5UuquONau/vxjxXNiA4kcdyMBkxvqimuoOJnU+CJEXCsJ4HFX34LPZt33UJbOaYEx940BeFKPb+3UuCJsS/ZBQUe90D+BIS9Ai/a14/W9q7Bf77/8zszAq+toxunXHwlnnv4ZgQDu/5jc95l1+DKz1+Ew+ZP11LgpRksW9+CF95qwciaMrx/zlhUlYbz3wV7HUmBVzC6fU6Mmz/Y9/bEUFNXCp95azM/hROgwCucHc90jgAFnnNsEeuCseLHMMz/3fU3zXHwzb7C/F6q57Oa8iVNgZcvqcKP6+mL4+r/W2U+zy02WCT9+9IrzphBiTcEUgq8wveb3WcmoklsXdINmC8E5ksswoiZL/tLi3h+SEAUArwCT5QkxOxDWIH3Lq6XV72ZJfBeXrUW1/3iTjx4+/UZol+/9tc4fMFMXHDmcdoKPDu3FwWePTTffq0drzy9BUbSQPXwMhxxygRUVBcuVu3pSt4qFHjyZqdy5xR4zqVrbHoYxuZHshbwzbwcvmGznFtUgcoUeM6HuHRdG255Ym3WQsfOHImPHjPR+cUlXYECT9LgFG+bV+ApHrCk41HgSRqcS217KvDSV9M1t7TtM2qT+Vy7YTVVg3++t8B7dtlq/PL39+Hu316TOe97P7kNB00ag4+cfzLiCfNd9fwURcBvXimWvlYsmTJ/NcdPQQTisRT+/KvlSJny7t3P5Bl1OPaMSQXV40lAIOBDyvzX2zC4L7kfxCEQNB/KnuB/dxwJpG/9PxBde39W7ar5n0d4hHkrLT8HJJD+Xpl+hEbS/O9Pit8vHdkpa7d148r/XZFV+yPHTMAHD+MLVg4EnD9bOrIVWbRIAunvl+lvkyn+nadIkjzdTgLBgN/8e3hqcG/yQwJ7E0i/EMrOj8/8y3XeW+3ppSvx4KNL9ln/grPeh8Pnz/j/9s4ETM6qyt+nqvdO0p2NkJAEEsISIGwRBREIIJuyKWImrOJo/uAoigQRFYd9EQkOCAIuLDogMCogIAyi/FkE2YcJCDEQAiEJ6Wzd6U7vVTVfFUkn3SGp+tY69963eHjkCd937jnv71qk33xL4dcHCrxXXpsnF1x9W79n4p194Q2y715T5Pijpsmylg+f6cAnOIG66rSk02lZ09kbvIjjZzYtapMn7n2rH4Uhw2rliJMmO04m+PgN9VXS5d3m0NWDpA9OkTOjJjCyoUZWtnZ5oiTqytTLdTdL78uXe7fQerd6eZ+Udwtt5R7ncgttka2R/66sqUrL6vYevi9j/L/RPc++Kw++vLiwwk5jG+TsI3cqcOfz0QRqPTb5P/Bo6+D3luwRPQSGeN+XPd7vLTv5vaWeUOhERnh3bDW39kimdK0CNYcIbNFYE+m0vgReKSsPFHirWlrlkOmz5On7r5e6tc95+8xJ58rl35spe07ZnltoS4Fa5BhuoQ0PMef9NP/47+dJy/L1b0Xeff9xsu2UYG8GDt+R+RW4hdb8DG2cgFtoY07Ve4lFdvlL3rsrvOeI8hKLkmBzC21JmCI5aLX3LLz8H3aOGVYXST2bi3ALrc3pmjsbt9Cam53NnXMLrc3php+trLfQltL+QIGXP+crZ18lH99jssw86Sh5+PHnCrfUPnzHVd4tdmkEXilQEXgRUCpeIv8Ci7kvN0mbd1Xo+O2HythJQ4ufxBGbJIDAY3NoJIDA05iK2z0h8NzOX+v0CDytybjdFwLP7fy1To/A05qMjr7UCrzHnnpJzrn4xvwDr7zn2mWkqqpSJo4fLffecqks+mC5fP+KX8jctxfK+K1GyYWzTpNddpxQILp4xfornnQgNq8LrsAzLzMXOkbguZCyeTMi8MzLzPaO1Qq8XI9Ud/5NKnoWSLZqO+mq3de7L5pbTm3fj+vmQ+C5krRZcyLwzMrLlW4ReK4kHWxOtQIv2DgIvKDcNjwPgRcFRWpETQCBF55oj2RldapHhuVqhB+bw/PMV0DgRcORKtER0Crw6lvvlKquV/oG7arbTzoHHRvd4FRSTQCBpzqevuZWN7fII/c/KAsXvCfjJ2wtRxx7lDQMbTSj+QBdIvACQOOU2Akg8GJHbPQCCDyj44uneQRePFypGo4AAi8cv/fSa+TpmmXSnctIfa5KPt2zpYzIRvsQ1HAdmnk2As/M3GzuWqXA866+a1x1gfeqx54+9Ln0EFk9/N9tjoLZNiCAwDNjO9z5y9tlwdvz+5qdMGlbOfGrXzKj+QBdIvACQOOU2Akg8GJHbPQCCDyj44uneQRePFypGo4AAi84v5zk5J7a96RD1r/9b1SuTj7btVXwopxZIIDAYyOEIdDxTot0Lm2TQdsPl+oR0bwIQaXA8yA1rLpMUpnmPlyZyvHSNvSbYfBxrkEEEHhmhDX7oiukq7Ozr9ma2lqZdcH3zGg+QJcIvADQOCV2Agi82BEbvQACz+j44mkegRcP16BVFza3y93/WCzvt3TInqMb5Yu7jJX66oqg5Yw9D4EXPLrmVLfcV7OwX4Eq7ybakzonBi/KmQg89kAoAssemS/Nzy0p1EilUzJm+mQZtOPwUDXzJ2sVeJXdc6W+7beSyq6RXEWDtA3+kvcsvK1Dz0sBMwgg8MzIiSvwzMiJLu0mgMCzO9+w0yHwwhK08HwEnp5Qs95LXH74lzdkeXt3X1Of2nq4nLqHez/0IPDC7cu/VH8gC73baNd9pmSGyV494WVBuK7MP5sr8MzPsBwTZHuy8vYVz3ov6lq/es3YIbL1V3cL3Y5WgZcfLOfdQluRbZJsxZaetawMPSsFzCGAwDMjKxOegZdatlgqn3lQUiuWSG7iLtKz75EiNcGuYOYKPDP2pWtdIvBcS9zfvAg8f7ycOBqBpyfmJa2dcuHjb/ZraPSQWrnooMl6mkyoEwReOND5F1i8Udkiy9JdMi5TLztkhkjK+4tPOAIIvHD8XD07252Rt6/8u3MCz9W8mVsEgccuiIRANivVv50tqdUr+8plJu8lvQd9IVB5BF4gbJwUMwEEXsyADS+PwDM8wDjat1HgdXX3yOKmJhk3ekupqjTnT/25Am/9DkfgxfH/dmqGJYDAC0vQ3fNdu4XW3aSZPE8Agcc+iIJAalWTVN/1k/6lhm0hXTPODlQegRcIGyfFTACBFzNgw8sj8AwPMI72bRN4b7w1X66+9TfSumaNDG1okG+feqLstN22caCLpSbPwPsQKwIvlu1F0ZAEEHghATp+uksvsXA8aufHR+A5vwWiAcAVeNFwpIpqAgg81fGUvTkEXtkj0NeAbQLv21fOlkUfLO0DPda7Cu8n583SB56ONksAgccG0UgAgacxFbd70vwMPLeTcXt6BJ7b+Uc5Pc/Ai5ImtTQSQOBpTEVPTwg8PVmo6cQmgdfc2iqn//ulkvNeBrHuU+ndQnvn1Zer4U0jpRFwReB15jqkU9ZIY2oEz6grbWuU9SgEXlnxs/hHEEDgsS00EkDgaUyFnriFlj2gkQACT2MqenpC4OnJQk0nNgm8PNTr77hbnnzhpT6+n953bzl9erCH3aoJKeJGnnl+gTzw6JvSmxU5fNp2coj3t7aPCwLvtezzMif3d8nmsjI8taUcVHGs1Eq9tijoZwMCCDy2gzYCCDxtidBPngACj32gkQACT2Mq9ITAYw9sjgACj/2xEQHbBF5vJiMPPfGUvPnOAtlt++3l0E/tI5UVFSS/lsC776+SS2b/tR+PM7+yr+w+ZYwqRrYLvLZci9yfubUf853SH5Op6f0D5dArOVmYbpfV6R4Zma2RrbJ1vHM2EMnNnxSHwOte1i7p+iqpHFQVQ8eUtJ0AAs/2hM2cD4FnZm62d43Asz1hM+dD4JmZW1JdI/CSIm3QOrYJPIPQl6XVPz32pvzhodf7rX3EwTvK8UdPKUs/m1rUdoG3IDdX/pZ5uN/4I1Kj5YiKGYFyeKWqWVamuvvOnZQZJBO8v/lESyBKgZft7JWme+ZJ58LVhSaHHjBWhu4/LtqGqWY9AQSe9REbOSACz8jYrG8agWd9xEYOiMAzMrbEmkbgJYbanIUQeOZkFUWnCxaukkuv4Qq8KFiGqZGRjDyY+bXkr8Rb99m/4ijZOuX/dubuVFaeqlrer51BUin7dA8P0yLnfgSBKAXeqiffl5anFvVbZczMXaVmFLdRs/lKJ4DAK50VRyZHAIGXHGtWKp0AAq90VhyZHAEEXnKsTVwJgWdiajH3jMCLGbDC8s+9vFDu+9PrPAOvzNl05NrkH9mXpV1aZWJqsoxLTwrUUf6VLU9UL/OU4PqXt4zIVssevUMD1eOkTROIUuAt/e2b0jF/vcDNrzr8MxOlYeooIoBAyQQQeCWj4sAECSDwEoTNUiUTQOCVjIoDEySAwEsQtoFLIfAMDC3ulhF4cROmfhACtt9CG4TJ5s5pSnfJ65WrJetJvBqpkD27GyV/FR6faAlEKfA6FqyWpXe80ddgxeBqGXvGbpKu4Zmd0aZmdzUEnt35mjodAs/U5OzuG4Fnd76mTofAMzW5ZPpG4CXD2ahVEHhGxeVMswg8/1HnX2TRkeqVwbkqXmDhH19JZ0Qp8PILdryzWlpfWSqVnrwb8oktpWpobUl9cBAE1hFA4LEXNBJA4GlMhZ4QeOwBjQQQeBpT0dMTAk9PFmo6QeCpiYJGNiCAwGM7aCQQtcDTOCM9mUUAgWdWXq50i8BzJWmz5kTgmZWXK90i8FxJOticCLxg3Kw+C4FndbzGDofAMzY6qxtH4Fkdr5HDIfCMjM36phF41kds5IAIPCNjs75pBJ71EYcaEIEXCp+dJyPw7MxVy1QruzLy4ooO6fber7DnsBoZW19VUmsIvJIwcVDCBBB4CQNnuaIEEHhFEXFAGQgg8MoAnSWLEkDgFUXEAWUggMArA3SDlkTgGRRWUq0i8JIi7d46a3qz8qt5zdKV/fDtqCnv75O3bZTRdcVfroDAc2+/mDAxAs+ElNzqEYHnVt6mTIvAMyUpt/pE4LmVtynTIvBMSao8fSLwysNd9aoIPNXxGN3cGy1d8uD7bf1m+MTIOpm2ZX3RuRB4RRFxQBkIIPDKAJ0lN0sAgccG0UgAgacxFXpC4LEHNBJA4GlMRU9PCDw9WajpBIGnJgrrGlnQ1iP/9e7qfnN9evQgmTqi+Js2EXi6t8PSzFuyMrdYxqZ3kob0FrqbjbA7BF6EMCkVCQEbBF7nmnZZ+NYCqR1UJ+O2nSCpdP56bT4mE0DgmZyevb0j8OzN1uTJEHgmpxd/7wi8+BkbtwICz7jIjGr4sSVr5JWVnYWetxlUJcdtPUQqS/jhDIGnN+YXeu6XuT1PFxpMe38dUHOqjKvYRW/DEXaGwIsQJqUiIWC6wGtevlIe/92D0tPVVeAxesJ42f/owzcr8bpSq+W16l9LU+UrMjQzSaZ0nSpDcuMi4UmRaAgg8KLhSJVoCSDwouVJtWgIIPCi4WhrFQSercmGmAuBFwIep5ZEIP8svG7vOXjDqitKOj5/EAKvZFSJHtib65G7O34gOe+vdZ8R6a3lM7VnJtpHuRZD4G2a/NuLemTue92y84RqmTCmtJfVlCtHm9Y1XeC9+NenZf6cN/pFcvD0Y2TkmC03GdNLtdfJ4opn+/59Xt4d2P5jm2I1fhYEnvERWjkAAs/KWI0fCoFnfISxDoDAixWvmcUReGbmZnvXCDydCffkuuWejvMReOv9pc6gEu7qoWfa5T8faS2smvLufvzqMQ1y8MfqEu7CzeVcFHiPDvqadElzv8APa/+Z1OSGubkJFE6NwFMYCi0JAo9NoJEAAk9jKnp6QuDpyUJNJwg8NVHQyAYEEHh6twO30HbI2hcr6w0p4c7OuGq5tLRl+lZtHFwhN507MuEu3FzOdIEX5BbagVfgNWS3kWkdV7q5AZROjcBTGozjbSHwHN8ASsdH4CkNRklbCDwlQWhqA4GnKQ16WUcAgad7L/ASC935JN3dQIE3sjEtP53lzstNkua94XqmC7z8LH5fYjHwGXi7dp8mg7NblTMG1h5AAIHHltBIAIGnMRV6QuCxBzZHAIHH/tiIAAKPTaGRAAJPYyr0xDPwPnoPbHgLbf6I044cIofvXR/rhnnPe95e07JemTSpRoYNLf35mrE2VYbiNgi8MmBjyZgJIPBiBkz5QAQQeIGwcVLMBBB4MQM2vDwCz/AA42gfgRcHVWqGJYDAC0uQ8+MggMDbNNUkX2Lx339eLf/zakehmYrKlBx3TKNsu21NHJGrr+lH4PV29Ejvmh6pHeHJVe9ZhXwgEBcBBF5cZKkbhgACLww9zo2LAAIvLrJ21EXg2ZFjpFMg8CLFSbGICCDwIgJJmUgJIPAixRmoWNuajPzspuWS2+BFIhO9N99OP97NFxiUKvCann9fmp59X3LeAxzrRg2WCcdNlso63hYcaBNyUlECCLyiiDigDAQQeGWAzpJFCSDwiiJy+gAEntPxf/TwCDw2hUYCCDyNqdATAq/8e2CNJ/Cuv3F5v0YQeBWyqq1bOrrWv0hkQ0DdLZ0y95ZX+jEb+bGtZMwB25Q/UDqwkgACz8pYjR8KgWd8hFYOgMCzMtbIhkLgRYbSnkIIPHuytGkSBJ5I1vtZvKtFpLohf5ugTemaOwsCT0d2j/21VV56ub3QDLfQVktdzeYFXvPc5bLwT/P6hVc3erBsd8KuOgKlC+sIIPCsi9SKgRB4VsRo3RAIPOsijXQgBF6kOO0ohsCzI0fbpnBd4LV7Fxi992S19HqP+arwHu01ft9eGbxV1raYjZsHgacnsne9l1gs8/ESi4x3hVrb60ukt6VdqrYYIoN3GS3pavPNeCm30GYzWZl3+6uSvxJv3Wfro3eQxu1G6AmUTqwigMCzKk5rhkHgWROlVYMg8KyKM/JhEHiRIzW/IALP/AxtnMB1gTf/0WppX7Y+2UrvmfOTP99tY9RGzYTAMyqufs02P/W29K5eL7BqxjTKkKnjzB1obeelCLz8ob2ewGx6abH0tnbL0J1GSsOk4cbPzgB6CSDw9GbjcmcIPJfT1zs7Ak9vNho6Q+BpSEFZDwg8ZYHQToGA6wLvH/9VLdkBvm7ycd3eQ+fZIOUkgMArJ/3ga2e7emXlY3P7FUjXVMnwQ3YIXlTJmaUKPCXt0oYjBBB4jgRt2JgIPMMCc6RdBJ4jQQccE4EXEJzNpyHwbE7X3NlcF3hLXqyQFXMr+gJs3CYr4/frNTdQSzpH4JkbpOtX4JmbHJ2bSACBZ2Jq9veMwLM/YxMnROCZmFpyPSPwkmNtzEoIPGOicqpR1wVeLiey8p8V0vZBSuq3yMqIHbOSXu/znNoLmoZF4GlKw18vLj8Dzx8pjoZAeAIIvPAMqRA9AQRe9EypGJ4AAi88Q5srIPBsTjfgbAi8gOA4LVYCrgu8WOFSPDABBF5gdJwYEwFuoY0JLGVDEUDghcLHyTERQODFBJayoQgg8ELhs/5kBJ71EfsfEIHnnxlnxE8AgRc/Y1bwT8B0gZfLZaQ7u0Kq0sMlnTL/Daz+E7TvDASefZnaMBECz4YU7ZsBgWdfpjZMhMCzIcX4ZkDgxcfW2MoIPGOjs7pxBJ7V8Ro7nMkCryOzWJZ1PSy92TWevKuTUTWHSX3lNsZmQeMfEkDgsRM0EkDgaUyFnhB47AGNBBB4GlPR0xMCT08WajpB4KmJgkY2IIDAYztoJGCywFvUcbd0ZZb2Ya1KN8r4+i9pxExPPggg8HzA4tDECCDwEkPNQj4IIPB8wOLQxAgg8BJDbeRCCDwjY4u3aQRevHypHowAAi8YN86Kl4DJAm/+mhtEvFtoN/xsM+grUpEaFC80qsdKAIEXK16KBySAwAsIjtNiJYDAixUvxQMSQOAFBOfIaQg8R4L2MyYCzw8tjk2KAAIvKdKs44eAyQJvedcTsrrn1b5xB1ft5N1Ge6if8YseO7fifVlQuVQas4Nkas8kqZaqoudwQDgCCLxw/Dg7HgIIvHi4UjUcAQReOH6cHQ8BBF48XG2pisCzJckI50DgRQiTUpERQOBFhpJCERIwWeDlJCutPXOkPbNQatNjpKFq90hfZPFaxQL5W83rfbRH5YbJsR2flLSkIkyAUgMJIPDYExoJIP3f5MAAAB+NSURBVPA0pkJPCDz2gEYCCDyNqejpCYGnJws1nSDw1ERBIxsQQOCxHTQSMFngxc3zD7V/k2Xp5n7LTO+YJsNyg+Ne2un6CDyn449s+Ex3pyx/4QFZs+QtaZj0MRm5h3d1biq4fEfgRRYNhSIkgMCLECalIiOAwIsMpZWFEHhWxhpuKAReOH6cHQ8BBF48XKkajgACb9P8Hqv5H3m7YlHfAWnv2rsvtR/q3UZbGQ46Z2+WAAKPDVIKgZXvL5LaIYOlvrHxIw9/+7cXSus762+x33L/f5Ex+88opfRHHoPAC4yOE2MkgMCLES6lAxNA4AVG58SJCDwnYvY3JALPHy+OToYAAi8ZzqzijwACb9O8Vqfa5aHa5yT/v3l5N61riuyQGe8PMEf7JoDA843MqRN6Orvk/suulIWvfXh7+8eP+5zsd8qJ/Rj0rFklr1/3Fe8lN7m+X68dMVYmn359YFYIvMDoODFGAgi8GOFSOjABBF5gdE6ciMBzImZ/QyLw/PHi6GQIIPCS4cwq/ggg8DbPK+s9Z29lqk0acnW8wMLf1gp8NAIvMDonTnzh3vvl6V/f0W/WGT+6TMbssH3fr+WyWZlzzUmS9W6jXfcZMmmqTPqXHwZmhMALjI4TYySAwIsRLqUDE0DgBUbnxIkIPCdi9jckAs8fL45OhgACLxnOrOKPAALPHy+Ojp8AAi9+xiavcP8VV8n851/sN8LBp8+U3Y/o/wbq5jeflXcfuFZyPV1S1biFbDv9fKnbYuvAoyPwAqPjxBgJIPBihEvpwAQQeIHROXEiAs+JmP0NicDzx4ujkyGAwEuGM6v4I4DA88eLo+MngMCLn7HJKyyc85r8/oJLvLtjP7w9dtCwYXLyf/xY6hsaNhor/yKLrpWLpW7UNpJKV4QaG4EXCh8nx0QAgRcTWMqGIoDAC4XP+pMReNZH7H9ABJ5/ZpwRPwEEXvyMWcE/AQSef2acES8BBF68fG2onpd4cx59TOoaG2Tq0UdJ45ajYh8LgRc7YhYIQACBFwAap8ROAIEXO2KjF0DgGR1fPM0j8OLhStVwBBB44fhxdjwEEHjxcKVqcALlFngVFTmprfH6956j1tGd9v4nFXwYzrSGAALPmiitGgSBZ1Wc1gyDwLMmylgGQeDFgtXsogg8s/OztXsEnq3Jmj0XAs/s/GzsvpwCL53OSePgrKxTdvmbNFtaPYmXQ+LZuNf8zITA80OLY5MigMBLijTr+CGAwPNDy71jEXjuZV50YgReUUQcUAYCCLwyQGfJogQQeEURcUDCBMop8OpqslJX8+Gz1dZ91nSmpasbgZfwNlC3HAJPXSQ05BFA4LENNBJA4GlMRU9PCDw9WajpBIGnJgoa2YAAAo/toJEAAk9jKm73VE6BV1Odk0G12X4BtLanpacXgef2rhRB4Lm+A3TOj8DTmYvrXSHwXN8Bm58fgcf+2IgAAo9NoZEAAk9jKvSEwGMPaCNQToGXZzG4LivVVR9ehdfdk5K2jrQ2RPRTBgIIvDJAZ8miBBB4RRFxQBkIIPDKAN2gJRF4BoWVVKsIvKRIs44fAgg8P7Q4NikCCLykSLNOqQTKLfDyfaY9Z5fzXmKRE+RdqbnZfhwCz/aEzZwPgWdmbrZ3jcCzPeFw8yHwwvGz8mwEnpWxGj8UAs/4CK0cAIFnZaxGD6VB4BkNkOZjIYDAiwUrRUMSQOCFBMjpsRBA4MWC1ZqiCDxrooxuEARedCypFB0BBF50LKkUHQEEXnQsqRQNAQReNBypEi0BBF60PKkWDQEEXjQcqRItAQRetDxtq4bAsy3RCOZB4EUAkRKRE0DgRY6UghEQQOBFAJESkRJA4EWKk2IREUDgRQSSMpESQOBFipNiERFA4EUE0tIyCDxLgw0zFgIvDD3OjYsAAi8ustQNQwCBF4Ye58ZBAIEXB1VqhiWAwAtLkPPjIIDAi4MqNcMSQOCFJWj3+Qg8u/MNNB0CLxA2ToqZAAIvZsCUD0QAgRcIGyfFSACBFyNcSgcmgMALjI4TYySAwIsRLqUDE0DgBUbnxIkIPCdi9jckAs8fL45OhgACLxnOrOKPAALPHy+Ojp8AAi9+xqzgnwACzz8zzoifAAIvfsas4J8AAs8/M5fOQOC5lHaJsyLwSgTFYYkSQOAlipvFSiSAwCsRFIclRgCBlxhqFvJBAIHnAxaHJkYAgZcYahbyQQCB5wOWg4ci8BwMvdjICLxihPj35SCAwCsHddYsRgCBV4wQ/z5pAgi8pImzXikEEHilUOKYpAkg8JImznqlEEDglULJ3WMQeO5mv8nJEXhsCo0EEHgaU6EnBB57QBsBBJ62ROgnTwCBxz7QSACBpzEVekLgsQc2R0C1wFvZ3CrnXXazfLBslfzxtsv65pjxtYvlzXnviqRShV9rGFwvT957XeGfF6/oIPGQBBB4IQFyeiwEEHixYKVoSAIIvJAAOT1yAgi8yJFSMAICCLwIIFIicgIIvMiRUjACAgi8CCBaXEKtwFvT3ikneKJu2if3kCf+/mo/gXfkKefJtRefKdtNHLtRNAi88LsVgReeIRWiJ4DAi54pFcMTQOCFZ0iFaAkg8KLlSbVoCCDwouFIlWgJIPCi5Um1aAgg8KLhaGsVtQKvvaNTlq9sKfx94ezb+wm8acd9S+6++QIZvcVwBF4MOxOBFwNUSoYmgMALjZACMRBA4MUAlZKhCCDwQuHj5JgIIPBiAkvZUAQQeKHwcXJMBBB4MYG1pKxagbeO78tz/rmRwNvzsJlywN67ySuvzZORwxvlrJnHywH77F44hSvwwu9MBF54hlSIngACL3qmVAxPAIEXniEVoiWAwIuWJ9WiIYDAi4YjVaIlgMCLlifVoiGAwIuGo61VyirwVqxaLUuaVmzEduzokTKscUjh1wcKvGw2Jz+86ldyxEF7yyf32lmeeOZVOe/ym+WBX19RuCKvua3b1qwSm6u6Ki1p7/mCnd2ZxNZkIQgUI1BfUyk9mYz09OaKHWrkv0+tfaankc073HTDoCppbe+RnJ3b0uFkzR19nShp78p/X2bNHYTOrSJQXZmWioqUdHj7kg8ENBDIef/hzv/esjeTlW6+KzVEQg9rCTTUV0lbR69k+c0le+IjCOT/4CHKT8r7Miz5x5innvtfue+Rpzdaf/oxB8nee+70kQLvo5r98revlC8cOU2OOuSTsqazN8p5nKxV5f0GKy8T+I+Zk/GrHbrGE8u9mZxkPInPBwJaCOR/89/R1SvsSi2J0EdNVYVUev8d7+rJFL4z+UBAA4H8nsz/4TC/t9SQBj2sI5C/aCF/cQjflewJTQTqaioKF9KUblU0dU8vcRMYVFsZ6RK+BF4pKw+8Aq+9o0vmvfO+7L7zpL7TT/7GZXLK8YfJ4Qd+nFtoS4Fa5BhuoY0AIiUiJ8AttJEjpWAEBLiFNgKIlIiUALfQRoqTYhER4BbaiEBSJlIC3EIbKU6KRUSAW2gjAmlpmbLeQlsK04ECr7mlTQ6dcY5ce8k3ZN+9pkj+Kr7vXHKTPPSbK2XEsAYEXilQEXgRUKJE0gQQeEkTZ71SCCDwSqHEMUkSQOAlSZu1SiWAwCuVFMclSQCBlyRt1iqVAAKvVFJuHqdW4D321EtyzsU3Sv7a0Z7ejFRVVcrE8aPl3lsulSeefVWuvvEuaVrRLPnn5Z379RNkn6k7FxLkJRbhNzJX4IVnSIXoCSDwomdKxfAEEHjhGVIhWgIIvGh5Ui0aAgi8aDhSJVoCCLxoeVItGgIIvGg42lpFrcALChyBF5Tc+vMQeOEZUiF6Agi86JlSMTwBBF54hlSIlgACL1qeVIuGAAIvGo5UiZYAAi9anlSLhgACLxqOtlZB4NmabIi5EHgh4HFqbAQQeLGhpXAIAgi8EPA4NRYCCLxYsFI0JAEEXkiAnB4LAQReLFgpGpIAAi8kQMtPR+BZHnCQ8RB4QahxTtwEEHhxE6Z+EAIIvCDUOCdOAgi8OOlSOygBBF5QcpwXJwEEXpx0qR2UAAIvKDk3zkPguZGzrykReL5wcXBCBBB4CYFmGV8EEHi+cHFwEQJduU55MfeoLM7Nl+1Se8ge6WmS8v7y80Hg+aHFsUkRQOAlRZp1/BBA4PmhxbFJEUDgJUXazHUQeGbmFmvXCLxY8VI8IAEEXkBwnBYrAQRerHidK35X5mqZn5vTN/en0p+TaenP++KAwPOFi4MTIoDASwg0y/gigMDzhYuDEyKAwEsItKHLIPAMDS7OthF4cdKldlACCLyg5DgvTgIIvDjpulW7LdciP818S3LeX+s+I1Jj5PSKK32BQOD5wsXBCRFA4CUEmmV8EUDg+cLFwQkRQOAlBNrQZRB4hgYXZ9sIvDjpUjsoAQReUHKcFycBBF6cdN2qnZWs/CTzdenKtfcNPiG1i5xYca4vEAg8X7g4OCECCLyEQLOMLwIIPF+4ODghAgi8hEAbugwCz9Dg4mwbgRcnXWoHJYDAC0qO8+IkgMCLk657td/MvSAPZW8pSLwhqREyIz1LtkiN9QUCgecLFwcnRACBlxBolvFFAIHnCxcHJ0QAgZcQaEOXQeAZGlycbSPw4qRL7aAEEHhByXFenAQQeHHSdbN2d65LVsoSGZUaL2mp8A0BgecbGSckQACBlwBklvBNAIHnGxknJEAAgZcAZIOXQOAZHF5crSPw4iJL3TAEEHhh6HFuXAQQeHGRpW4xAh1tvbJycbvUDamS4WPq+g5H4BUjx78vBwEEXjmos2YxAgi8YoT49+UggMArB3Vz1kTgmZNVYp0i8BJDzUI+CCDwfMAKcGhrc5fc/cs35e1/tsh2Ow+VY0/cXkaOWi8FApR04hQEnhMxqxuyualT5jzZJLnMhy+8GD1psOyw14jCPyPw1MVFQx4BBB7bQCMBBJ7GVOgJgcce2BwBBB77YyMCCDw2hUYCCLz4Usn05uTyrz8j78xvKSxSM6hSdtxzuJx1wV7xLWpJZQSeJUEaNsacJ5bKqg86+3W9zzHjpLquAoFnWJautIvAcyVps+ZE4JmVlyvdIvBcSTrYnAi8YNysPguBZ3W8xg6HwIsvundeXCVXnP+c9GayfYsM2aJGrrr1QKmp8f8Mrvg61VcZgacvExc68ivwups+KGCpHjXaBTzMqJAAAk9hKLQkCDw2gUYCCDyNqejpCYGnJws1nSDw1ERBIxsQQODFtx1e+MMi+d2d/5RlLR19i4ybOEQu/vn+8S1qSWUEniVBGjZGqbfQtnf0yOJrr5DVT/+1MGHD/p+Wrb55nqTSacMmpl3TCSDwTE/Qzv4ReHbmavpUCDzTE4y3fwRevHyNrI7AMzI265tG4MUX8eqmLrnvR2/IP95bJStbu2R4Q418/dKpMn6HxvgWtaQyAs+SIA0co5SXWCx9/C+yaPYl/aYbO+uH0rDvgQZOTMsmE0DgmZyevb0j8OzN1uTJEHgmpxd/7wi8+BkbtwICz7jInGgYgRdvzCsWtsu8Z1ZK/sKcHfYbKUPH1Ma7oCXVEXiWBGnRGBu+xGLBjdfJyof+0G+64Z+bIVueMtOiiRnFBAIIPBNScq9HBJ57mZswMQLPhJTK1yMCr3zs1a6MwFMbjdONIfCcjl/t8Ag8tdE429iGAq9l/gKZf+4Zkuv88IUXqZoamfjjm6Vm7Hhn+TB4eQgg8MrDnVU3TwCBxw7RSACBpzEVPT0h8PRkoaYTBJ6aKGhkAwIIPLaDRgIIPI2puN3ThgKvoysjne/M867Cu7cAZfiRn5faidu7DYjpy0IAgVcW7CxahAACjy2ikQACT2MqenpC4OnJQk0nCDw1UdAIAo89oJwAAk95QA62N1DgOYiAkRUSQOApDIWWeAste0AlAQSeyljUNIXAUxOFnkYQeHqyoJP1BLgCj92gkQACT2MqbveEwHM7f63TI/C0JuN2X1yB53b+WqdH4GlNRkdfCDwdOajqAoGnKg6aWUsAgcdW0EgAgacxFbd7QuC5nb/W6RF4WpNxuy8Entv5a50egac1GR19IfB05KCqCwSeqjhoBoHHHlBMAIGnOBxHW0PgORq88rEReMoDcrQ9BJ6jwSsfG4GnPKAyt4fAK3MAGpdH4GlMhZ64Ao89oJEAAk9jKm73hMBzO3+t0yPwtCbjdl8IPLfz1zo9Ak9rMjr6QuDpyEFVFwg8VXHQzFoCCDy2gkYCCDyNqbjdEwLP7fy1To/A05qM230h8NzOX+v0CDytyejoC4GnIwdVXSDwVMVBMwg89oBiAgg8xeE42hoCz9HglY+NwFMekKPtIfAcDV752Ag85QGVuT0EXpkD0Lg8Ak9jKvTEFXjsAY0EEHgaU3G7JwSe2/lrnR6BpzUZt/tC4Lmdv9bpEXhak9HRFwJPRw6qukDgqYqDZtYSQOCxFTQSQOBpTMXtnhB4buevdXoEntZk3O4Lged2/lqnR+BpTUZHXwg8HTmo6gKBpyoOmkHgsQcUE0DgKQ7H0dYQeI4Gr3xsBJ7ygBxtD4HnaPDKx0bgKQ+ozO0h8MocgMblEXgaU6EnrsBjD2gkgMDTmIrbPSHw3M5f6/QIPK3JuN0XAs/t/LVOj8DTmoyOvhB4OnJQ1QUCT1UcNLOWAAKPraCRAAJPYypu94TAczt/rdMj8LQm43ZfCDy389c6PQJPazI6+kLg6chBVRcIPFVx0AwCjz2gmAACT3E4jraGwHM0eOVjI/CUB+Roewg8R4NXPjYCT3lAZW4PgVfmADQuj8DTmAo9cQUee0AjAQSexlTc7gmB53b+WqdH4GlNxu2+EHhu5691egSe1mR09IXA05GDqi4QeKrioJm1BBB4bAWNBBB4GlNxuycEntv5a50egac1Gbf7QuC5nb/W6RF4WpPR0RcCT0cOqrpA4KmKg2YQeOwBxQQQeIrDcbQ1BJ6jwSsfG4GnPCBH20PgORq88rEReMoDKnN7CLwyB6Bx+fraCqlKp6WlvUdje/TkKAGuwHM0eOVjI/CUB+Rgewg8B0M3YGQEngEhOdgiAs/B0A0YGYFnQEhlbBGBV0b4WpdG4GlNxu2+EHhu5691egSe1mTc7QuB5272midH4GlOx93eEHjuZq95cgSe5nTK3xsCr/wZqOsAgacuEhryCCDw2AYaCSDwNKbidk8IPLfz1zo9Ak9rMm73hcBzO3+t0yPwtCajoy8Eno4cVHWBwFMVB82sJYDAYytoJIDA05iK2z0h8NzOX+v0CDytybjdFwLP7fy1To/A05qMjr4QeDpyUNUFAk9VHDSDwGMPKCaAwFMcjqOtIfAcDV752Ag85QE52h4Cz9HglY+NwFMeUJnbQ+CVOQCNyyPwNKZCT1yBxx7QSACBpzEVt3tC4Lmdv9bpEXhak3G7LwSe2/lrnR6BpzUZHX0h8HTkoKoLBJ6qOGhmLQEEHltBIwEEnsZU3O4Jged2/lqnR+BpTcbtvhB4buevdXoEntZkdPSFwNORg6ouEHiq4qAZBB57QDEBBJ7icBxtDYHnaPDKx0bgKQ/I0fYQeI4Gr3xsBJ7ygMrcHgKvzAFoXB6BpzEVeuIKPPaARgIIPI2puN0TAs/t/LVOj8DTmozbfSHw3M5f6/QIPK3J6OgLgacjB1VdIPBUxUEzawkg8NgKGgkg8DSm4nZPCDy389c6PQJPazJu94XAczt/rdMj8LQmo6MvBJ6OHFR1gcBTFQfNIPDYA4oJIPAUh+Noawg8R4NXPjYCT3lAjraHwHM0eOVjI/CUB1Tm9qwTeGXmyfIQgAAEIAABCEAAAhCAAAQgAAEIQAACEFBNIJXzPqo7pDkIQAACEIAABCAAAQhAAAIQgAAEIAABCDhMAIHncPiMDgEIQAACEIAABCAAAQhAAAIQgAAEIKCfAAJPf0Z0CAEIQAACEIAABCAAAQhAAAIQgAAEIOAwAQSe4eH3ZjLyH7/4ndx618Py9P0/lWGNQwoTXXPzPXLbPY9IOp3um/Dumy6QHSeNN3xi2jeBwMrmVjnvspvlg2Wr5I+3XdbX8sLFTfLDq26RuW+9J1uNHik/+NYpMnXX7U0YiR4tIvDUc3Pka+ddI5WVFX1TfedrM+Sk4w6xaEpGMYFAfi9efdNdsmx5s0yZPFEu/95MGTm80YTW6dFSAnw/WhqsoWM9+NizctHs2+TS735VDj/w431T8N1paKAWtL2pn7357rQgXENGQOAZEtSm2jzzB9fK5O22lpt+80d58t7r+gRe/j922287Xk78/KcNn5D2TSOwpr1TTvjaxTLtk3vIE39/tZ/A+9K3rpCD95sqJx93qDzz4uuezPuV/Pnu2VK1gUgxbV76NY/An/7ynPz5yRfkJxd9w7zm6dgaAq1t7XLESefKDZefVZB3N9x6nyxYuIR9aU3CZg7C96OZudnYdf5ChJdenSvLVjTLl2d8tk/g8d1pY9rmzLSpn7357jQnQ9M7ReAZnuCb3pVMeYG368Ff7ifwzrn4Rpm2z+5y9GH7Gj4h7ZtGoL2jU5avbCn8feHs2/sE3opVq+WIE8+VZx+8QSorPrzy6fiZF8i5/3aCfGLPyaaNSb8GE7jnj4/LnDffkUvO/VeDp6B10wn89/9/Xn7/0JPy8x+fUxgl/0Pp/p//pjz/0I1SXV1l+nj0bygBvh8NDc7CtvM/4+TvHPrqrB/L9GMO6hN4fHdaGLZBI23qZ2++Ow0K0fBWEXiGB7iu/YEC7/RzZ0s2m5MF738gKe+gLx59oMw86ShLpmUMEwi8POef/QTey3PmycXX3C733XppX/uzLvqZ7D11Z5nu7U8+EEiKwC/vfEgefeIF6e7ulVUtrbL/3rvJ9848SQbV1ybVAutAQG7+zQOyYlWLfP+bJ/fROMATeL++7vsyYfxoCEGgLAT4fiwLdhbdDIGvnH1VP4HHdyfbRQOBgT97892pIRU3ekDgKc+5s6tb3lqwaKMuhzYMlnFjtuj79YFfIj//zwdkyOB6+cJnD5B3Fy2VvNDL/4B66AF7KZ+Y9kwgkL+abknTio1aHes9127dcxgHCrxnXnxNrvvl7+Uu71mM6z7n/+hXssO24+TULx5uwtj0aBCBze3RF71bcl6fu0BOm36EZHM5+c4lN8qkbbbqJ1IMGpVWDSWQf35tJpOVWWdM75vgsBnnyHWXfrNwZT0fCJSDwJ+ffJHvx3KAZ81NEhgo8PjuZLNoIDDwZ2++OzWk4kYPCDzlOS/+YLnM9l5IMfDzsd127Pd8u4FfIgOP/9nt98vSZSvlonO+rHxi2jOBwFPP/a/c98jTG7Wav8Vh7z13Kvz6QIH3ymvz5IKrb+v3TLyzL7xB9t1rihx/1DQTxqZHgwiUskfXjZMXevmXqzx8x48MmpBWTSeQ/4O2Ju/lFeefdUrfKPsde6bc+bPzZeuxW5o+Hv1bQoDvR0uCNHiMgQKP706Dw7So9WI/e/PdaVHYykZB4CkLJGg7A79E8vJkyo4T+56jc6135VP++Tob/qAQdC3Og0ApBAYKvPytiodMn+W9Lfl6qautLpT4jPcA9/xbF/ecwptoS2HKMdEQeNu7qnnI4EEyauTQQsFnvReqXHH9nf3kcjQrUQUCmyaQ/9P63/zu0cIts/nPUu+t3Z89+bvy3J9u7HtOKPwgkDQBvh+TJs56xQgMFHh8dxYjxr9PgsDAn7357kyCOmvkCSDwLNkHA79EZpxxkRzgvcTijFOPlfeXNMlpZ13pXX33r96znna1ZGLG0E5goMDL95v/TdjH95hceB7jw48/V7il9uE7rpKKirT2cejPIgKzb7pH5r2zUK658OuFZ4XmrwTN37J49unrb2W0aFxGUUog/8buw044R2Zf8G+y1+47yuXX3SEdHV1yxfdnKu2YtlwgwPejCymbNeNAgcd3p1n52drtwJ+9+e60NWl9cyHw9GVSckfNLW1y4PFnFY7v6emVqqrKwj8/dvdsaVvT4b1A4DbJvymnwXsW3inHH1b4mw8E4ibw2FMvSf4tyOI9W6ynN1PYlxO9B7Lfe8ulssi7Jfz7V/xC5r69UMZvNUounHWa7LLjhLhboj4E+hFo9yTJxT+5XZ78+6tSVVkpB+27p3z3Gyf2XRkKLggkRSD/bNArf3pn4a3de0zZTi4/b6YMbRyc1PKsA4GNCPD9yKbQQuD4mRcUngPe6/1esiKdllQ6JT/6wf/z3kb7CeG7U0tKbvWxuZ+96+tq+b2lW9uhbNMi8MqGnoUhAAEIQAACEIAABCAAAQhAAAIQgAAEIFCcAAKvOCOOgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2Qgg8MqGnoUhAAEIQAACEIAABCAAAQhAAAIQgAAEIFCcAAKvOCOOgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2Qgg8MqGnoUhAAEIQAACEIAABCAAAQhAAAIQgAAEIFCcAAKvOCOOgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2Qgg8MqGnoUhAAEIQAACEIAABCAAAQhAAAIQgAAEIFCcAAKvOCOOgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2Qgg8MqGnoUhAAEIQAACEIAABCAAAQhAAAIQgAAEIFCcAAKvOCOOgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2Qgg8MqGnoUhAAEIQAACEIAABCAAAQhAAAIQgAAEIFCcAAKvOCOOgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2Qgg8MqGnoUhAAEIQAACEIAABCAAAQhAAAIQgAAEIFCcAAKvOCOOgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2Qj8H3w/BR73hbd7AAAAAElFTkSuQmCC", |
|
"text/html": [ |
|
"<div> <div id=\"269121ac-3c48-48aa-9312-c4aba1769026\" class=\"plotly-graph-div\" style=\"height:600px; width:800px;\"></div> <script type=\"text/javascript\"> require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById(\"269121ac-3c48-48aa-9312-c4aba1769026\")) { Plotly.newPlot( \"269121ac-3c48-48aa-9312-c4aba1769026\", [{\"hoverinfo\":\"text\",\"marker\":{\"color\":[\"#d01f72\",\"#75195e\",\"#3678a7\",\"#5b3f83\",\"#74a788\",\"#571122\",\"#4099c1\",\"#659222\",\"#188ca3\",\"#6d4052\",\"#35303c\",\"#a9e927\",\"#29fa15\",\"#71c500\",\"#9b9d6e\",\"#cf7e83\",\"#badd6d\",\"#85fa26\",\"#22463b\",\"#ce865d\",\"#f59c06\",\"#011995\",\"#793548\",\"#ad8b14\",\"#d937bd\",\"#2b9f18\",\"#046e5c\",\"#75b5e3\",\"#c959de\",\"#72e048\",\"#8e8cab\",\"#20f2c3\",\"#64f999\",\"#e69670\",\"#6a0fce\",\"#d65c3a\",\"#7bee34\",\"#4f86b8\",\"#b43417\",\"#4dfb77\",\"#2ae342\",\"#c3e1f2\",\"#12897b\",\"#2b3af3\",\"#7ea8e9\",\"#6ad041\",\"#0bdacc\",\"#99fe53\",\"#4aaf9f\",\"#d156c8\",\"#505bd9\",\"#dc152c\",\"#b52bf6\",\"#9baca0\",\"#a03134\",\"#d43c00\",\"#5af098\",\"#2c168d\",\"#c6016b\",\"#f090af\",\"#482281\",\"#39821f\",\"#e0a8df\",\"#480c89\",\"#08808d\",\"#ac5faf\",\"#0faf59\",\"#79c82a\",\"#e6e164\",\"#0d2037\",\"#8afd40\",\"#2e1afc\",\"#3ec815\",\"#fbfef2\",\"#a63fa4\",\"#b27d2e\",\"#ca3592\",\"#b9fd23\",\"#ac9648\",\"#804ce2\",\"#9b5e28\",\"#a64739\",\"#c457d7\",\"#de30e4\",\"#1f6ab0\",\"#6ff3c5\",\"#6df6ca\",\"#ed694d\",\"#2fef1a\",\"#335dcf\",\"#845aa9\",\"#574e28\",\"#dc95ec\",\"#b2140a\",\"#15ae86\",\"#70d1d9\",\"#6f745a\",\"#b3dba5\",\"#108c41\",\"#268bba\",\"#913568\",\"#1a6fdf\",\"#422abb\",\"#cb725f\",\"#fe62a5\",\"#dfc6c7\",\"#b25d7b\",\"#bd53b1\",\"#796278\",\"#048452\",\"#c6eff5\",\"#d24e5d\",\"#fe8e92\",\"#22398f\",\"#3e5237\",\"#8069bc\",\"#7740be\",\"#cc8ec0\",\"#b280bb\",\"#91f4db\",\"#ac55ba\",\"#c97596\",\"#116019\",\"#43c2e8\",\"#2a2d25\",\"#fc2b74\",\"#ae7afe\",\"#92b4fa\",\"#dd8cd7\",\"#4862ce\",\"#af0f59\",\"#ad6bd0\",\"#3f0a72\",\"#e01073\",\"#144ada\",\"#5cb9ca\",\"#51d0da\",\"#d6d07a\",\"#b61e76\",\"#474ff9\",\"#68bece\",\"#d01b19\",\"#ee26df\",\"#2ebca4\",\"#539908\",\"#ec0a37\",\"#1a5613\",\"#da28db\",\"#246fa5\",\"#bbfe83\",\"#d54222\",\"#580c96\",\"#02cada\",\"#996ff1\",\"#e2a239\",\"#ae5204\",\"#4ce72d\",\"#2cde7f\",\"#b64eac\",\"#591ab9\",\"#a958c9\",\"#696eaa\",\"#4c4355\",\"#6a6c06\",\"#df5d2e\",\"#9780cf\",\"#682d42\",\"#efed10\",\"#1b312a\",\"#dbde1c\",\"#e1b5db\",\"#a95826\",\"#4e797a\",\"#10384a\",\"#9a5ba2\",\"#d34482\",\"#8a29da\",\"#fb9dce\",\"#ff2d6a\",\"#50f10d\",\"#f8d349\",\"#7b4427\",\"#11a70e\",\"#987252\",\"#c932c1\",\"#2d7f7d\",\"#c1e3c5\",\"#0c777d\",\"#0f8781\",\"#dd889c\",\"#799a24\",\"#4212f1\",\"#e6f378\",\"#805527\",\"#091a90\",\"#a9541c\",\"#fcdcad\",\"#01f59b\",\"#94a85d\",\"#426575\",\"#7f03bd\",\"#2dcfac\",\"#52b6df\",\"#73e76a\",\"#d70d97\",\"#601568\",\"#d4b1ce\",\"#7341ee\",\"#bb0ee6\",\"#f645e0\",\"#1c2c7e\",\"#7dd58b\",\"#4b9a93\",\"#9df332\",\"#612b32\",\"#b1c27d\",\"#3626a5\"],\"opacity\":0.8,\"size\":5},\"mode\":\"markers\",\"text\":[\"Video: 59506507\\u003cbr\\u003eText: Well, I realized that was a whole lot of theory, but I hope it gave you a good intuition that will\\nb...\",\"Video: 59671315\\u003cbr\\u003eText: Okay, so here we are, back in Jupyter Lab, ready for the next use of a frontier model, and see this\\n...\",\"Video: 60616895\\u003cbr\\u003eText: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...\",\"Video: 60619275\\u003cbr\\u003eText: And we will conclude our expedition into the world of frontier models through their chat interface b...\",\"Video: 59472693\\u003cbr\\u003eText: Friends.\\nI am absolutely exhausted.\\nI am exhausted and a little tiny bit traumatized.\\nAnd you are so...\",\"Video: 59670121\\u003cbr\\u003eText: So it's business time right now.\\nWe are going to build a Rag pipeline to estimate the price of produ...\",\"Video: 59295619\\u003cbr\\u003eText: Welcome back to the the moment when we bring it all together into a beautiful user interface.\\nBut fi...\",\"Video: 60617163\\u003cbr\\u003eText: And already that wraps up day two.\\nNow that you have built that solution.\\nAnd congratulations on tha...\",\"Video: 60616423\\u003cbr\\u003eText: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...\",\"Video: 59170227\\u003cbr\\u003eText: Welcome back to Google Colab.\\nHere we are ready to explore the wonderful world of Tokenizers.\\nSo, uh...\",\"Video: 59169985\\u003cbr\\u003eText: So I hope you enjoyed that whirlwind tour of Google Colab.\\nHere's just a little screenshot example o...\",\"Video: 60616927\\u003cbr\\u003eText: It's time for our first LM experiment at this point.\\nSo some of this you may know well, you may know...\",\"Video: 59673721\\u003cbr\\u003eText: And here we are in JupyterLab for the last time, and we are looking here at day five, the last day\\no...\",\"Video: 59508055\\u003cbr\\u003eText: I'm so very happy that you've reached this epic moment in the course and that you're hanging in ther...\",\"Video: 59670259\\u003cbr\\u003eText: It's remarkable.\\nBut you are now at the 95% point.\\nThere's 5% remaining of this course.\\nUh, maybe it...\",\"Video: 60616623\\u003cbr\\u003eText: So we're now going to start week one of the course when we are going to be looking at exploring fron...\",\"Video: 59472383\\u003cbr\\u003eText: And welcome back to the week six folder.\\nWe're now at day two, which is the second and final stage o...\",\"Video: 59670171\\u003cbr\\u003eText: So as the very final step on this part four of day two of week eight, we are now going to build an\\ne...\",\"Video: 59297721\\u003cbr\\u003eText: And so now the time has come to talk about the most crucial aspect of Rag, which is the idea of vect...\",\"Video: 59297599\\u003cbr\\u003eText: Well, that was a sneaky detour I took you on in the last one.\\nI hope you enjoyed it though, and I ho...\",\"Video: 59507635\\u003cbr\\u003eText: Look, I hope you're excited.\\nYou really should be.\\nYou've been through 80% of the course and it's al...\",\"Video: 59669375\\u003cbr\\u003eText: Here we are for the day.\\n2.1 notebook.\\nAnd don't let it be said that I don't ever do anything for yo...\",\"Video: 59297733\\u003cbr\\u003eText: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\\nLet me...\",\"Video: 59670369\\u003cbr\\u003eText: It is terrific that you're hanging on in there and making such great progress with this course.\\nAs w...\",\"Video: 59166281\\u003cbr\\u003eText: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...\",\"Video: 59671567\\u003cbr\\u003eText: Well, the first thing you're going to notice is that I don't have a notebook open for you.\\nAnd that'...\",\"Video: 59297593\\u003cbr\\u003eText: And welcome to continuing our journey with Hrag.\\nAnd today it's time to unveil Liang Chen.\\nSo first,...\",\"Video: 59166461\\u003cbr\\u003eText: And welcome back to the lab.\\nHere we are in Jupyter Lab and we are going to go into week two.\\nAnd we...\",\"Video: 59167007\\u003cbr\\u003eText: Well, how fabulous is that?\\nI hope that you are as wowed as I am by our new airline, I assistant and...\",\"Video: 59508121\\u003cbr\\u003eText: The moment has arrived.\\nHere we go.\\nWe're in fine tuning.\\nWe do fine tuning.\\nTrain.\\nThere is also a ...\",\"Video: 59295579\\u003cbr\\u003eText: All right.\\nAre you excited to see how this goes?\\nLet's give it a try.\\nSo in this next section, I cre...\",\"Video: 60620375\\u003cbr\\u003eText: And with that, we've reached an important milestone.\\nThe first week of our eight week journey is com...\",\"Video: 59472491\\u003cbr\\u003eText: Welcome back.\\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...\",\"Video: 59472425\\u003cbr\\u003eText: Welcome to week six, day three.\\nToday is going to be a day that you will either love or you will hat...\",\"Video: 59508057\\u003cbr\\u003eText: Actually slight change in plan.\\nI'm going to wrap up the day.\\nDay three at this point, and say that ...\",\"Video: 60619577\\u003cbr\\u003eText: And for the final piece of background information, I wanted to take another moment to talk about API...\",\"Video: 59170291\\u003cbr\\u003eText: Welcome back to Colab and welcome back to our business project.\\nSo again our assignment, we are due ...\",\"Video: 60619651\\u003cbr\\u003eText: I mentioned before an AI company called vellum.\\nWhen we were talking about the different questions, ...\",\"Video: 59473191\\u003cbr\\u003eText: And you thought we'd never get here.\\nHere we are in Jupyter Lab, running our fine tuning for a front...\",\"Video: 59170297\\u003cbr\\u003eText: And here we are in Google Colab, ready for fun with models.\\nSo first we do the usual Pip installs an...\",\"Video: 59167015\\u003cbr\\u003eText: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\\nAnd this is going to be lots of creativit...\",\"Video: 59170043\\u003cbr\\u003eText: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\\nIf you en...\",\"Video: 59473147\\u003cbr\\u003eText: Well, I'm very relieved.\\nI've got that behind me.\\nNo more human testing for me.\\nWe'll have one final...\",\"Video: 59166453\\u003cbr\\u003eText: Welcome back and welcome to our continuing JupyterLab experience.\\nUh, I'm hopefully going to keep yo...\",\"Video: 59166915\\u003cbr\\u003eText: Welcome back to the wonderful world of JupyterLab.\\nAnd here we are in week two.\\nDay three.\\nUh, bring...\",\"Video: 59667365\\u003cbr\\u003eText: Here we are back in Colab, looking at the week seven, day five of the Colab notebooks and I'm on a\\nT...\",\"Video: 60616845\\u003cbr\\u003eText: We're on the home stretch.\\nThis is the final step in the environment setup, and it's an easy one.\\nIt...\",\"Video: 59295459\\u003cbr\\u003eText: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\\nBut this time we'...\",\"Video: 59471979\\u003cbr\\u003eText: So we now turn to the parts of the problem, which is perhaps, let's say, not as glamorous as some\\nof...\",\"Video: 59503705\\u003cbr\\u003eText: And so now we talk about quantization the q and q Laura.\\nQ stands for quantized quantized.\\nLaura.\\nAn...\",\"Video: 59472505\\u003cbr\\u003eText: So the good news is that this is the very final video about data set curation.\\nYou were probably fed...\",\"Video: 59669217\\u003cbr\\u003eText: And welcome to the next part of visualizing the data.\\nAnd just very quickly to show it to you in 3D....\",\"Video: 59671221\\u003cbr\\u003eText: I gotta tell you, I don't like to toot my horn a whole lot, but I do think that I've done a great\\njo...\",\"Video: 59503703\\u003cbr\\u003eText: Well.\\nHello there everybody.\\nI am so grateful that you've made it through to the start of week seven...\",\"Video: 59473201\\u003cbr\\u003eText: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...\",\"Video: 60622463\\u003cbr\\u003eText: In this video, we're going to set up a full data science environment for Mac users.\\nIn the next vide...\",\"Video: 60619299\\u003cbr\\u003eText: Well, I hope you found that both educational and enjoyable.\\nAs we went through and learned so much a...\",\"Video: 59295607\\u003cbr\\u003eText: So to revisit then the solution that we built in the previous day and talk about the metrics.\\nAs I s...\",\"Video: 59297575\\u003cbr\\u003eText: Well, welcome to the final part on rag.\\nAnd this is the session where you go from being a rag expert...\",\"Video: 59507687\\u003cbr\\u003eText: It's time for action, everybody.\\nWe've set up our colab.\\nHere we are, week seven, day three.\\nWe've g...\",\"Video: 59671441\\u003cbr\\u003eText: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...\",\"Video: 59673431\\u003cbr\\u003eText: And here we have it.\\nThe user interface is completed.\\nThe extra notification came through on my phon...\",\"Video: 59473137\\u003cbr\\u003eText: Let's get straight to it.\\nSo the place where you can see everything that's going on and get knee dee...\",\"Video: 59166421\\u003cbr\\u003eText: Welcome back to the radio day in the lab.\\nMore to do.\\nLet's keep going.\\nWhere we left off is we had ...\",\"Video: 59295599\\u003cbr\\u003eText: Welcome to the Jupyter Lab for day four.\\nIt's going to look very familiar because it's actually I've...\",\"Video: 59669631\\u003cbr\\u003eText: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...\",\"Video: 59673663\\u003cbr\\u003eText: But wait, there's more.\\nWe need to add some more to the user interface just to make it look more coo...\",\"Video: 59506929\\u003cbr\\u003eText: And we return to the hugging face open LLM leaderboard.\\nThe first place you go when selecting your b...\",\"Video: 59504785\\u003cbr\\u003eText: So at this point we're going to talk about hyperparameters.\\nAnd we're going to introduce three of th...\",\"Video: 59505337\\u003cbr\\u003eText: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...\",\"Video: 59271655\\u003cbr\\u003eText: So here we are on Hugging Face's main landing page at Hugging Face Core.\\nA URL you know.\\nWell, since...\",\"Video: 59472883\\u003cbr\\u003eText: Okay, time to reveal the results.\\nIt has run to completion.\\nAnd here it is.\\nSo a moment to pause.\\nIt...\",\"Video: 59673639\\u003cbr\\u003eText: And welcome now to the code for our user interface, which we will find in this Python module.\\nPrice ...\",\"Video: 59472463\\u003cbr\\u003eText: So last time we looked at a humble linear regression model with feature engineering, and now we say\\n...\",\"Video: 59297595\\u003cbr\\u003eText: So by the time you're watching this, hopefully you have played yourself with vectors.\\nYou've created...\",\"Video: 60619149\\u003cbr\\u003eText: So we're going to start our exploration into the world of frontier models by playing with the famous...\",\"Video: 59297735\\u003cbr\\u003eText: And at last the time has come to see rag in action.\\nAfter all of this talk, and here we are.\\nWe're i...\",\"Video: 60616407\\u003cbr\\u003eText: And now over to my Mac people.\\nAnd I have news for you.\\nIt's exactly the same thing.\\nYou go to a fav...\",\"Video: 59170235\\u003cbr\\u003eText: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\\nOn ...\",\"Video: 59472067\\u003cbr\\u003eText: So we've covered steps 1 to 4 of the five step strategy.\\nAnd that brings us to step five, which is p...\",\"Video: 59472011\\u003cbr\\u003eText: Welcome everybody.\\nSo in the past I've said quite a few times, I am excited to start this this week ...\",\"Video: 59295553\\u003cbr\\u003eText: Welcome back.\\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...\",\"Video: 59297773\\u003cbr\\u003eText: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\\n...\",\"Video: 59295583\\u003cbr\\u003eText: And here we are back in JupyterLab.\\nIt's been a minute.\\nWe've been working in Colab for last week, a...\",\"Video: 59507329\\u003cbr\\u003eText: Okay.\\nIt's moment of truth time.\\nI have just taken our class tester.\\nYou remember this class?\\nUh, it...\",\"Video: 59295429\\u003cbr\\u003eText: Continuing our investigation of benchmarks, and this will become more real when we actually see some...\",\"Video: 60595637\\u003cbr\\u003eText: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\\nh...\",\"Video: 59668027\\u003cbr\\u003eText: And so here we are at the home page for modal.\\nAt modal.com spelt model not not model which is confu...\",\"Video: 59295527\\u003cbr\\u003eText: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\\nHe...\",\"Video: 59295377\\u003cbr\\u003eText: Just before we go on to some of the more advanced metrics, I want to mention for a second something\\n...\",\"Video: 59666211\\u003cbr\\u003eText: So before we try our new model and one more recap on the models so far and keep notes of this so we\\n...\",\"Video: 59170107\\u003cbr\\u003eText: And once again, it's that moment when you take a pause and congratulate yourself on another day of\\ns...\",\"Video: 60616833\\u003cbr\\u003eText: So I realized that day one of week one has been a pretty long day, and I assure you that the other,\\n...\",\"Video: 59472413\\u003cbr\\u003eText: Wonderful.\\nWhere we left off is we had just created the Get Features function, which builds our feat...\",\"Video: 59297561\\u003cbr\\u003eText: And would you believe at this point you're 55% of the way along the journey?\\nUh, it's been a while s...\",\"Video: 59669211\\u003cbr\\u003eText: Well, we took on a lot today and we seem to have been successful.\\nThese red icons that you see on th...\",\"Video: 59166981\\u003cbr\\u003eText: Welcome to week two, day five.\\nThe last day of week two where a lot is coming together.\\nI am so grat...\",\"Video: 60619227\\u003cbr\\u003eText: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\\nm...\",\"Video: 60620395\\u003cbr\\u003eText: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\\n...\",\"Video: 59665127\\u003cbr\\u003eText: Well hi there everybody.\\nI'm not going to give you my usual song and dance about how excited you are...\",\"Video: 59668923\\u003cbr\\u003eText: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\\nAnd ...\",\"Video: 59504887\\u003cbr\\u003eText: Well, here we are again in Google Colab.\\nIt's been a minute since we were here, and welcome back to ...\",\"Video: 59170165\\u003cbr\\u003eText: Welcome, everybody to the last day of week three.\\nWeek three.\\nDay five.\\nWe're here already wrapping ...\",\"Video: 60617251\\u003cbr\\u003eText: Congratulations are definitely in order.\\nYesterday was a mammoth first day on this course and you go...\",\"Video: 59166951\\u003cbr\\u003eText: All right, back to the lab.\\nBack to our project.\\nTime to work with tools.\\nI am in the week two folde...\",\"Video: 60619619\\u003cbr\\u003eText: Well, day four was an information dense day.\\nI do hope that you learned some something useful here, ...\",\"Video: 60616663\\u003cbr\\u003eText: Well.\\nHi there, this is time for PC people to get set up.\\nSo all you Mac people out there, you don't...\",\"Video: 59508175\\u003cbr\\u003eText: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\\n...\",\"Video: 59670087\\u003cbr\\u003eText: And welcome to part four of day two of week eight.\\nUh, there's a lot happening this week, and I have...\",\"Video: 59506713\\u003cbr\\u003eText: Hi everyone.\\nSo the reason I'm so fired up about week seven is that this is the time when we actuall...\",\"Video: 60620169\\u003cbr\\u003eText: Hopefully you found this super satisfying to be able to have this nice business result and have it c...\",\"Video: 59295435\\u003cbr\\u003eText: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...\",\"Video: 59297609\\u003cbr\\u003eText: Last week, we worked with models that were able to speed up code by a factor of 60,000 times, which\\n...\",\"Video: 59507489\\u003cbr\\u003eText: Continuing our adventure through hyperparameters for training.\\nThe next one is pretty crucial and it...\",\"Video: 59295549\\u003cbr\\u003eText: And welcome back to our challenge again.\\nAnd this time we are working with our beautiful prototype.\\n...\",\"Video: 59665129\\u003cbr\\u003eText: And now let me make this real for you by showing you some, some diagrams, particularly now looking\\na...\",\"Video: 59169991\\u003cbr\\u003eText: Okay, so that was your introduction to Hugging Face.\\nAnd now I'm going to turn to a different resour...\",\"Video: 59472027\\u003cbr\\u003eText: And now the time has come to curate our data set.\\nAnd the way we're going to do this is we're going ...\",\"Video: 59472307\\u003cbr\\u003eText: Welcome to week six.\\nDay two a day.\\nWhen we get back into the data, we look back in anger at our dat...\",\"Video: 59508289\\u003cbr\\u003eText: So here we are now, back in the Colab, in the same one that we kicked off in the previous day.\\nIt's ...\",\"Video: 59472333\\u003cbr\\u003eText: Thank you for putting up with me during my foray into traditional machine learning.\\nI think it was u...\",\"Video: 59295431\\u003cbr\\u003eText: Now I want to take a quick moment to give you a flyby of five different ways that llms are used comm...\",\"Video: 59673449\\u003cbr\\u003eText: Well, I have to tell you that I'm a little bit sad.\\nThis is the beginning of the beginning of the en...\",\"Video: 59669389\\u003cbr\\u003eText: Well.\\nHi there.\\nSo you've made it to day two of week eight, and I am super grateful that you've been...\",\"Video: 59170057\\u003cbr\\u003eText: And so at the beginning of this week, we started by talking about hugging face pipelines.\\nAnd you us...\",\"Video: 59166949\\u003cbr\\u003eText: Welcome back to making chatbots.\\nLet's keep going.\\nSo for the next part we're going to beef up the s...\",\"Video: 59473019\\u003cbr\\u003eText: Welcome back to an action packed time of of training.\\nSo now, after waiting about five minutes when ...\",\"Video: 59297585\\u003cbr\\u003eText: Before we move on, let me show you one more time this fabulous slide that describes the simple three...\",\"Video: 59170255\\u003cbr\\u003eText: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...\",\"Video: 60614589\\u003cbr\\u003eText: So we're now going to run a large language model directly on your box using a platform called llama,...\",\"Video: 59297601\\u003cbr\\u003eText: I'm not going to lie, at this point you have every reason to be impatient with me.\\nWe've been yammer...\",\"Video: 60616629\\u003cbr\\u003eText: And welcome back to team PC and Team Mac as we come back together again for a quick video.\\nIn this o...\",\"Video: 59297749\\u003cbr\\u003eText: It's always welcome back to JupyterLab, my favorite place to be.\\nAnd now we are, of course in the we...\",\"Video: 59170135\\u003cbr\\u003eText: Welcome.\\nIt's week three.\\nIt's day four.\\nWe are back on the adventure in open source land, back inve...\",\"Video: 59472017\\u003cbr\\u003eText: And this is the first time that we'll be coding against our big project of the course.\\nWelcome to Ju...\",\"Video: 59507017\\u003cbr\\u003eText: Welcome to Colab.\\nWelcome to the week seven day two Colab.\\nAnd just before we try our base model, we...\",\"Video: 60619883\\u003cbr\\u003eText: And now we've arrived at an exciting moment in our first week.\\nThe conclusion of the first week is w...\",\"Video: 59508297\\u003cbr\\u003eText: What more is there to say, really?\\nTomorrow is the day for results.\\nA day that very excited indeed a...\",\"Video: 60619247\\u003cbr\\u003eText: We're going to spend a little bit more time with GPT just to try out a few more interesting things.\\n...\",\"Video: 59504769\\u003cbr\\u003eText: Without further ado, we're going to get stuck into it.\\nTalking about Laura.\\nLow rank adaptation.\\nAnd...\",\"Video: 59170233\\u003cbr\\u003eText: Welcome back to our continued exploits with Tokenizers.\\nWhat we're now going to look at is what's ca...\",\"Video: 59671231\\u003cbr\\u003eText: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...\",\"Video: 60620397\\u003cbr\\u003eText: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...\",\"Video: 59170093\\u003cbr\\u003eText: I'm delighted to see you again.\\nAs we get started with day three of week three of our adventure and ...\",\"Video: 59473089\\u003cbr\\u003eText: Welcome back.\\nSo hopefully you are still impressed by the GPT four mini results.\\nThe frontier model ...\",\"Video: 60395261\\u003cbr\\u003eText: Let's keep going with our project to equip our LM with a tool.\\nWe just created this piece of code to...\",\"Video: 60617259\\u003cbr\\u003eText: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...\",\"Video: 59507313\\u003cbr\\u003eText: And it's this time again, when we look at the podium of how our models are performing across the boa...\",\"Video: 60619721\\u003cbr\\u003eText: Now it's time to talk for a minute about tokens.\\nTokens are the individual units which get passed in...\",\"Video: 59295451\\u003cbr\\u003eText: I know that everybody.\\nIt seems like just the other day that we were embarking on our quest together...\",\"Video: 59166919\\u003cbr\\u003eText: And with that, it concludes our session on tools.\\nAnd at this point, you are probably an expert on t...\",\"Video: 59295441\\u003cbr\\u003eText: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\\nc...\",\"Video: 59295541\\u003cbr\\u003eText: And welcome back.\\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...\",\"Video: 59473101\\u003cbr\\u003eText: Welcome back.\\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\\nAnd how do ...\",\"Video: 59507423\\u003cbr\\u003eText: So you may remember eons ago when we were building our data set.\\nAt the end of that, we uploaded our...\",\"Video: 59295545\\u003cbr\\u003eText: I really hope you've enjoyed this week.\\nWe've got tons done.\\nWe've experimented with all sorts of ne...\",\"Video: 59472503\\u003cbr\\u003eText: Welcome back to Jupyter Lab.\\nLast time, we looked at some silly models for predicting the price of p...\",\"Video: 60614591\\u003cbr\\u003eText: The mantra of this course is that the best way to learn is by doing, and we will be doing stuff toge...\",\"Video: 59473021\\u003cbr\\u003eText: Welcome to our favorite place to be to JupyterLab.\\nHere we are again now in day three.\\nIn week six.\\n...\",\"Video: 60617255\\u003cbr\\u003eText: I'm now going to talk for a bit about models.\\nA term you often hear is the term frontier models, whi...\",\"Video: 59667829\\u003cbr\\u003eText: Well.\\nHello there.\\nLook, I know what you're thinking.\\nYou're thinking I peaked too early.\\nLast week ...\",\"Video: 59505329\\u003cbr\\u003eText: Welcome back.\\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...\",\"Video: 59669049\\u003cbr\\u003eText: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...\",\"Video: 60619439\\u003cbr\\u003eText: This now brings us to an extremely important property of LMS called the context window that I want t...\",\"Video: 59668181\\u003cbr\\u003eText: And so it gives me great pleasure to introduce to you the project that I've lined up for you this we...\",\"Video: 59472441\\u003cbr\\u003eText: Welcome back.\\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\\n...\",\"Video: 59507785\\u003cbr\\u003eText: Well, I'm sure you're fed up of me saying that the moment of truth has arrived, but it really has.\\nT...\",\"Video: 59295587\\u003cbr\\u003eText: When I left you, we had just created this simple user interface for converting from Python to C plus...\",\"Video: 59166465\\u003cbr\\u003eText: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\\nWe'd written two...\",\"Video: 59473071\\u003cbr\\u003eText: Hey, gang.\\nLook, I know what you're thinking.\\nThis week was supposed to be training week.\\nI set it a...\",\"Video: 59295423\\u003cbr\\u003eText: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...\",\"Video: 59297723\\u003cbr\\u003eText: So I know what you're thinking.\\nYou're thinking, what's going on here?\\nWe're on day five.\\nWe're on d...\",\"Video: 59166947\\u003cbr\\u003eText: Well, thank you for coming along for week two, day four.\\nWe have lots of good stuff in store today.\\n...\",\"Video: 59666831\\u003cbr\\u003eText: Take one more moment to look at this very nice diagram that lays it all out, and we will move on.\\nNo...\",\"Video: 59295493\\u003cbr\\u003eText: And welcome to week four, day three.\\nAs we are about to embark upon another business project which w...\",\"Video: 60616855\\u003cbr\\u003eText: Now I know what you're thinking.\\nWe've been building environments for so long.\\nAre we not done yet?\\n...\",\"Video: 59506611\\u003cbr\\u003eText: So in a future day, I'm going to be training, fine tuning a model and creating a fine tuned model.\\nA...\",\"Video: 60616493\\u003cbr\\u003eText: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...\",\"Video: 59166317\\u003cbr\\u003eText: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\\nUh, so today, ...\",\"Video: 59295439\\u003cbr\\u003eText: So I'm aware that there's a big risk that you are getting fed up of leaderboards, because we've done...\",\"Video: 59472421\\u003cbr\\u003eText: And welcome back to our final time in Jupyter Lab with traditional machine learning.\\nIt's almost ove...\",\"Video: 59472137\\u003cbr\\u003eText: Well, well, well, it's been a long day, but congratulations, you've made it.\\nWe've gone through and ...\",\"Video: 59297693\\u003cbr\\u003eText: So at the end of each week, it's customary for me to give you a challenge, an assignment to do on\\nyo...\",\"Video: 60620143\\u003cbr\\u003eText: So we're going to make a call to GPT four.\\nOh, that's going to ask it to look through a set of links...\",\"Video: 60619501\\u003cbr\\u003eText: I welcome to day four of our time together.\\nThis is a very important day.\\nToday we're going to be lo...\",\"Video: 59297743\\u003cbr\\u003eText: And welcome to day five.\\nFor reals.\\nWe're actually in the proper Jupyter notebook.\\nThis time we're i...\",\"Video: 59166847\\u003cbr\\u003eText: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\\nU...\",\"Video: 59170223\\u003cbr\\u003eText: Well.\\nFantastic.\\nIt's coming up to the end of the week, and that means it's coming up to a challenge...\",\"Video: 59170037\\u003cbr\\u003eText: So how does it feel to be 30% of the way down the journey to being a proficient LLM engineer?\\nTake a...\",\"Video: 59295609\\u003cbr\\u003eText: You must be feeling absolutely exhausted at this point.\\nAnd if you are, that is okay.\\nYou have done ...\",\"Video: 60619281\\u003cbr\\u003eText: Well, I'm delighted to welcome you to day three of our eight week journey together.\\nAnd today we're ...\",\"Video: 59472429\\u003cbr\\u003eText: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\\n...\",\"Video: 59167009\\u003cbr\\u003eText: Welcome back.\\nIt's time to make our full agent framework.\\nI'm super excited about this.\\nIt's pulling...\",\"Video: 59166481\\u003cbr\\u003eText: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\\nReady to go with weeks...\",\"Video: 59670933\\u003cbr\\u003eText: I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very sat...\",\"Video: 59670073\\u003cbr\\u003eText: Okay, it's time to complete the Rag workflow in our Jupyter Lab on day 2.3.\\nWe've got this function ...\",\"Video: 59673595\\u003cbr\\u003eText: That concludes a mammoth project.\\nThree weeks in the making.\\nIn the course of those three weeks, sta...\",\"Video: 59297603\\u003cbr\\u003eText: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\\nFinally,...\",\"Video: 60614541\\u003cbr\\u003eText: I am delighted to welcome you to the first day of our eight weeks together as you join me on this ad...\",\"Video: 59667357\\u003cbr\\u003eText: Let's now see our results side by side.\\nWe started our journey with a constant model that was at $1....\",\"Video: 59667841\\u003cbr\\u003eText: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\\nat t...\",\"Video: 59472007\\u003cbr\\u003eText: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...\",\"Video: 59507435\\u003cbr\\u003eText: So I'm now going to talk about five important hyperparameters for the training process.\\nAnd some of ...\",\"Video: 59509185\\u003cbr\\u003eText: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...\",\"Video: 59473159\\u003cbr\\u003eText: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\\nSo we are going to put our fr...\",\"Video: 60619447\\u003cbr\\u003eText: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...\",\"Video: 59166353\\u003cbr\\u003eText: Well, congratulations on leveling up yet again.\\nYou've got some real hard skills that you've added t...\",\"Video: 60619123\\u003cbr\\u003eText: So what we're now going to do is we're going to look at some models in practice and start to compare...\",\"Video: 59295363\\u003cbr\\u003eText: Well, another congratulations moment.\\nYou have 40% on the way to being an LM engineer at a high leve...\",\"Video: 60619289\\u003cbr\\u003eText: And now we'll go a bit faster through the other models.\\nWe'll start with Google's Gemini.\\nI have the...\",\"Video: 59472873\\u003cbr\\u003eText: So it's quite an adventure that we had at the frontier of what's capable with Llms today, solving a\\n...\",\"Video: 60619429\\u003cbr\\u003eText: Let me talk about some other phenomena that have happened over the last few years.\\nOne of them has b...\",\"Video: 59295601\\u003cbr\\u003eText: So it's time to continue our journey into the world of open source and understand which models we sh...\",\"Video: 59170025\\u003cbr\\u003eText: And a massive welcome back one more time to LM engineering.\\nWe are in week three, day two and we are...\",\"Video: 59166443\\u003cbr\\u003eText: And welcome back everybody.\\nWelcome to week two day three.\\nIt's a continuation of our enjoyment of r...\",\"Video: 60620025\\u003cbr\\u003eText: And welcome back to Jupyter Lab, one of my very favorite places to be.\\nWhen Jupyter Lab sprung up on...\",\"Video: 59170055\\u003cbr\\u003eText: Welcome to the world of Google Colab.\\nYou may already be very familiar with Google Colab, even if so...\"],\"x\":[-12.589552,3.4522862,6.075746,7.942426,-3.525712,4.1480594,4.6078315,-1.7122985,-1.6395565,-9.307264,-6.770974,1.4278501,-3.795615,-5.48206,-4.170929,0.42981502,-3.5235593,1.8772042,17.16095,15.35386,-11.031532,15.838091,14.824762,-2.4908643,-4.1442113,-6.1486583,14.927404,-2.396536,-3.8051388,-6.8470283,7.2692485,-3.5521216,-2.7953513,-3.2857506,-5.7256823,9.390827,-8.941686,8.362188,-2.4580688,-7.4087963,-0.73915297,-9.044852,4.499095,1.223194,0.6079307,-2.3045015,9.307752,4.968605,-3.0444636,-13.019468,-1.9913696,16.247093,-6.6251817,-3.236832,2.7420254,8.059585,5.8575497,1.3678622,14.408681,-7.4271216,4.6005616,-6.2227287,-8.091358,-1.0886598,3.9747384,0.32758102,-5.358367,0.61464316,-10.948633,-13.510744,-10.267108,3.5313623,-4.744116,0.98348933,15.8871355,8.520779,12.316195,13.00314,-7.271094,-12.220864,-1.1228861,8.195982,15.675435,3.5282235,2.7380142,3.0779696,-7.539173,9.471518,2.180644,1.8750061,1.8318319,-7.089598,-0.79000425,0.13995205,16.312626,-3.438324,-4.710372,6.9159217,4.997074,-11.944866,-6.278514,-7.310172,-8.248277,-0.2617442,-2.001054,-2.4265862,7.9734154,-4.359084,1.4919127,-0.38369736,2.8925261,2.770904,11.788717,-11.200065,7.0120173,-12.489671,-7.3114347,-1.5968479,-2.0740008,-7.660865,1.4215823,3.4180312,-5.9557977,-4.101128,-7.1637955,1.2174717,-8.017974,13.607655,-8.332471,12.951081,13.259139,7.851571,11.287736,-8.430205,-2.83165,-9.306727,1.3151592,-2.5466766,9.444017,-12.522999,-10.38123,-7.0192504,0.9397985,-9.068451,4.640919,-2.51455,5.657744,1.8063583,-15.553587,0.9260013,-4.1032104,4.0678425,6.9909325,4.943192,-2.3060699,1.6395743,-0.48130858,1.4182721,-0.63343734,5.6635394,-3.9217196,-6.3144593,8.239023,8.01618,-8.5425,-0.17059784,-6.761717,5.7745337,-1.1535196,-2.372529,3.1349926,14.739626,-3.0802853,-13.388992,3.012913,10.2796135,-13.004479,-0.6004416,-2.7484965,4.0349708,1.1794678,-3.6047134,2.0950997,3.1776624,5.355312,9.249312,-5.047935,-2.5895002,-6.023992,0.42378932,6.4555655,11.28314,-6.1557565,2.6091251,-6.8104343,4.435232,-6.023258,16.286194,-0.5731437,2.0213904,8.013111,-1.5368563,-10.384564,-8.238789,-0.057244953,-15.348441,-1.7015631,6.999166,2.5275056,8.751711,1.0946581,-8.001234,2.8864157,-7.969383,-0.49457392,5.2979984,-7.2938204],\"y\":[-6.5300555,14.089418,12.162957,-0.80311126,-1.6755519,-13.505905,6.5699277,1.4233526,3.7408068,-3.009902,-1.6519994,9.911368,14.304171,-9.145412,6.8292613,4.1779256,-13.0463,-11.951641,5.743851,10.09115,-8.627289,2.584683,7.23334,6.759529,4.1768756,12.57557,9.190438,13.93031,5.511717,-11.910828,5.8589373,3.6352885,-13.270146,-2.0432546,-9.36256,-4.0989513,2.833454,-4.2829947,-6.8667107,-5.736574,11.985562,0.33564866,-7.6441755,11.567259,10.677815,-9.594754,14.068278,-2.490878,-4.379241,-6.612759,-12.312431,2.8374946,10.107471,0.86265963,-7.4858155,13.485198,0.44996768,0.12787041,9.892149,-7.652323,13.810954,13.420327,-9.137389,14.72838,7.676501,-12.387229,14.694999,-5.226657,-8.565104,-5.734247,0.18139325,-9.293782,14.728803,-13.8647995,6.203831,0.3127214,8.967697,-1.4659885,-1.9273498,3.1576743,-2.5850005,5.1483097,5.489101,8.593102,-9.933031,-4.0722184,-10.497164,-10.699288,1.5761652,-3.9312649,-7.012359,1.2585955,3.0229156,-16.250467,6.635525,6.7354093,8.468663,-1.6286882,10.374195,-10.649093,-4.4278836,-6.3712683,2.0350108,4.080304,11.325701,2.5883422,13.5416975,-11.214315,-11.917827,-2.9803138,10.627456,2.9241033,7.383127,-11.1040945,5.136991,-10.0327215,-16.114536,-12.599517,-2.9481568,-11.174494,-8.890177,-1.0979837,6.8131933,1.463663,0.48312876,12.914509,-12.583761,8.630306,-4.9693522,-1.45638,8.836656,14.184286,9.729582,0.11569965,-12.046801,-3.562859,8.306646,-0.12532109,1.8029642,-7.7512345,-2.2794526,11.317182,8.203367,-1.5793608,-7.4279957,10.902695,9.414275,-6.943587,-9.714286,-2.107979,8.535427,-2.2587268,4.7189612,-9.422279,-9.95208,1.4961839,-15.637048,5.8088226,-10.609174,-0.896489,2.6177058,-6.1964593,-10.441606,-5.6452084,6.2846713,-16.04126,-9.215314,5.74158,14.189653,-6.3043413,-2.132232,5.4891644,9.997777,-10.8906975,3.0431316,-3.8775747,-7.930391,6.087151,13.401266,-3.5819368,-10.400259,-3.2769384,5.1977687,11.534197,1.2013716,9.190291,4.5081005,4.3416286,2.9420025,-1.041198,-0.5506536,6.695455,9.870885,9.233299,5.8174458,-13.40291,5.86892,5.1465583,5.9692817,-8.088498,-9.699435,-4.3566523,-11.696756,-11.084373,-9.227834,-9.344566,8.588015,0.74937767,-1.7350386,0.8596554,-8.018119,9.262971,0.69532895,-0.87655604,9.858918,12.275479,-16.078566],\"type\":\"scatter\"}], {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"margin\":{\"r\":20,\"b\":10,\"l\":10,\"t\":40},\"title\":{\"text\":\"2D Chroma Vector Store Visualization\"},\"scene\":{\"xaxis\":{\"title\":{\"text\":\"x\"}},\"yaxis\":{\"title\":{\"text\":\"y\"}}},\"width\":800,\"height\":600}, {\"responsive\": true} ).then(function(){\n", |
|
" \n", |
|
"var gd = document.getElementById('269121ac-3c48-48aa-9312-c4aba1769026');\n", |
|
"var x = new MutationObserver(function (mutations, observer) {{\n", |
|
" var display = window.getComputedStyle(gd).display;\n", |
|
" if (!display || display === 'none') {{\n", |
|
" console.log([gd, 'removed!']);\n", |
|
" Plotly.purge(gd);\n", |
|
" observer.disconnect();\n", |
|
" }}\n", |
|
"}});\n", |
|
"\n", |
|
"// Listen for the removal of the full notebook cells\n", |
|
"var notebookContainer = gd.closest('#notebook-container');\n", |
|
"if (notebookContainer) {{\n", |
|
" x.observe(notebookContainer, {childList: true});\n", |
|
"}}\n", |
|
"\n", |
|
"// Listen for the clearing of the current output cell\n", |
|
"var outputEl = gd.closest('.output');\n", |
|
"if (outputEl) {{\n", |
|
" x.observe(outputEl, {childList: true});\n", |
|
"}}\n", |
|
"\n", |
|
" }) }; }); </script> </div>" |
|
] |
|
}, |
|
"metadata": {}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"# We humans find it easier to visalize things in 2D!\n", |
|
"# Reduce the dimensionality of the vectors to 2D using t-SNE\n", |
|
"# (t-distributed stochastic neighbor embedding)\n", |
|
"\n", |
|
"tsne = TSNE(n_components=2, random_state=42)\n", |
|
"reduced_vectors = tsne.fit_transform(vectors)\n", |
|
"\n", |
|
"# Create the 2D scatter plot\n", |
|
"fig = go.Figure(data=[go.Scatter(\n", |
|
" x=reduced_vectors[:, 0],\n", |
|
" y=reduced_vectors[:, 1],\n", |
|
" mode='markers',\n", |
|
" marker=dict(size=5, color=colors, opacity=0.8),\n", |
|
" text=[f\"Video: {t}<br>Text: {d[:100]}...\" for t, d in zip(video_numbers , documents)],\n", |
|
" hoverinfo='text'\n", |
|
")])\n", |
|
"\n", |
|
"fig.update_layout(\n", |
|
" title='2D Chroma Vector Store Visualization',\n", |
|
" scene=dict(xaxis_title='x',yaxis_title='y'),\n", |
|
" width=800,\n", |
|
" height=600,\n", |
|
" margin=dict(r=20, b=10, l=10, t=40)\n", |
|
")\n", |
|
"\n", |
|
"fig.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 78, |
|
"id": "50207703-afdc-4251-96c3-5e3d6f14d9b7", |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"application/vnd.plotly.v1+json": { |
|
"config": { |
|
"plotlyServerURL": "https://plot.ly" |
|
}, |
|
"data": [ |
|
{ |
|
"hoverinfo": "text", |
|
"marker": { |
|
"color": [ |
|
"#d01f72", |
|
"#75195e", |
|
"#3678a7", |
|
"#5b3f83", |
|
"#74a788", |
|
"#571122", |
|
"#4099c1", |
|
"#659222", |
|
"#188ca3", |
|
"#6d4052", |
|
"#35303c", |
|
"#a9e927", |
|
"#29fa15", |
|
"#71c500", |
|
"#9b9d6e", |
|
"#cf7e83", |
|
"#badd6d", |
|
"#85fa26", |
|
"#22463b", |
|
"#ce865d", |
|
"#f59c06", |
|
"#011995", |
|
"#793548", |
|
"#ad8b14", |
|
"#d937bd", |
|
"#2b9f18", |
|
"#046e5c", |
|
"#75b5e3", |
|
"#c959de", |
|
"#72e048", |
|
"#8e8cab", |
|
"#20f2c3", |
|
"#64f999", |
|
"#e69670", |
|
"#6a0fce", |
|
"#d65c3a", |
|
"#7bee34", |
|
"#4f86b8", |
|
"#b43417", |
|
"#4dfb77", |
|
"#2ae342", |
|
"#c3e1f2", |
|
"#12897b", |
|
"#2b3af3", |
|
"#7ea8e9", |
|
"#6ad041", |
|
"#0bdacc", |
|
"#99fe53", |
|
"#4aaf9f", |
|
"#d156c8", |
|
"#505bd9", |
|
"#dc152c", |
|
"#b52bf6", |
|
"#9baca0", |
|
"#a03134", |
|
"#d43c00", |
|
"#5af098", |
|
"#2c168d", |
|
"#c6016b", |
|
"#f090af", |
|
"#482281", |
|
"#39821f", |
|
"#e0a8df", |
|
"#480c89", |
|
"#08808d", |
|
"#ac5faf", |
|
"#0faf59", |
|
"#79c82a", |
|
"#e6e164", |
|
"#0d2037", |
|
"#8afd40", |
|
"#2e1afc", |
|
"#3ec815", |
|
"#fbfef2", |
|
"#a63fa4", |
|
"#b27d2e", |
|
"#ca3592", |
|
"#b9fd23", |
|
"#ac9648", |
|
"#804ce2", |
|
"#9b5e28", |
|
"#a64739", |
|
"#c457d7", |
|
"#de30e4", |
|
"#1f6ab0", |
|
"#6ff3c5", |
|
"#6df6ca", |
|
"#ed694d", |
|
"#2fef1a", |
|
"#335dcf", |
|
"#845aa9", |
|
"#574e28", |
|
"#dc95ec", |
|
"#b2140a", |
|
"#15ae86", |
|
"#70d1d9", |
|
"#6f745a", |
|
"#b3dba5", |
|
"#108c41", |
|
"#268bba", |
|
"#913568", |
|
"#1a6fdf", |
|
"#422abb", |
|
"#cb725f", |
|
"#fe62a5", |
|
"#dfc6c7", |
|
"#b25d7b", |
|
"#bd53b1", |
|
"#796278", |
|
"#048452", |
|
"#c6eff5", |
|
"#d24e5d", |
|
"#fe8e92", |
|
"#22398f", |
|
"#3e5237", |
|
"#8069bc", |
|
"#7740be", |
|
"#cc8ec0", |
|
"#b280bb", |
|
"#91f4db", |
|
"#ac55ba", |
|
"#c97596", |
|
"#116019", |
|
"#43c2e8", |
|
"#2a2d25", |
|
"#fc2b74", |
|
"#ae7afe", |
|
"#92b4fa", |
|
"#dd8cd7", |
|
"#4862ce", |
|
"#af0f59", |
|
"#ad6bd0", |
|
"#3f0a72", |
|
"#e01073", |
|
"#144ada", |
|
"#5cb9ca", |
|
"#51d0da", |
|
"#d6d07a", |
|
"#b61e76", |
|
"#474ff9", |
|
"#68bece", |
|
"#d01b19", |
|
"#ee26df", |
|
"#2ebca4", |
|
"#539908", |
|
"#ec0a37", |
|
"#1a5613", |
|
"#da28db", |
|
"#246fa5", |
|
"#bbfe83", |
|
"#d54222", |
|
"#580c96", |
|
"#02cada", |
|
"#996ff1", |
|
"#e2a239", |
|
"#ae5204", |
|
"#4ce72d", |
|
"#2cde7f", |
|
"#b64eac", |
|
"#591ab9", |
|
"#a958c9", |
|
"#696eaa", |
|
"#4c4355", |
|
"#6a6c06", |
|
"#df5d2e", |
|
"#9780cf", |
|
"#682d42", |
|
"#efed10", |
|
"#1b312a", |
|
"#dbde1c", |
|
"#e1b5db", |
|
"#a95826", |
|
"#4e797a", |
|
"#10384a", |
|
"#9a5ba2", |
|
"#d34482", |
|
"#8a29da", |
|
"#fb9dce", |
|
"#ff2d6a", |
|
"#50f10d", |
|
"#f8d349", |
|
"#7b4427", |
|
"#11a70e", |
|
"#987252", |
|
"#c932c1", |
|
"#2d7f7d", |
|
"#c1e3c5", |
|
"#0c777d", |
|
"#0f8781", |
|
"#dd889c", |
|
"#799a24", |
|
"#4212f1", |
|
"#e6f378", |
|
"#805527", |
|
"#091a90", |
|
"#a9541c", |
|
"#fcdcad", |
|
"#01f59b", |
|
"#94a85d", |
|
"#426575", |
|
"#7f03bd", |
|
"#2dcfac", |
|
"#52b6df", |
|
"#73e76a", |
|
"#d70d97", |
|
"#601568", |
|
"#d4b1ce", |
|
"#7341ee", |
|
"#bb0ee6", |
|
"#f645e0", |
|
"#1c2c7e", |
|
"#7dd58b", |
|
"#4b9a93", |
|
"#9df332", |
|
"#612b32", |
|
"#b1c27d", |
|
"#3626a5" |
|
], |
|
"opacity": 0.8, |
|
"size": 5 |
|
}, |
|
"mode": "markers", |
|
"text": [ |
|
"Video: 59506507<br>Text: Well, I realized that was a whole lot of theory, but I hope it gave you a good intuition that will\nb...", |
|
"Video: 59671315<br>Text: Okay, so here we are, back in Jupyter Lab, ready for the next use of a frontier model, and see this\n...", |
|
"Video: 60616895<br>Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", |
|
"Video: 60619275<br>Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", |
|
"Video: 59472693<br>Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", |
|
"Video: 59670121<br>Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", |
|
"Video: 59295619<br>Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", |
|
"Video: 60617163<br>Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", |
|
"Video: 60616423<br>Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", |
|
"Video: 59170227<br>Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", |
|
"Video: 59169985<br>Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", |
|
"Video: 60616927<br>Text: It's time for our first LM experiment at this point.\nSo some of this you may know well, you may know...", |
|
"Video: 59673721<br>Text: And here we are in JupyterLab for the last time, and we are looking here at day five, the last day\no...", |
|
"Video: 59508055<br>Text: I'm so very happy that you've reached this epic moment in the course and that you're hanging in ther...", |
|
"Video: 59670259<br>Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", |
|
"Video: 60616623<br>Text: So we're now going to start week one of the course when we are going to be looking at exploring fron...", |
|
"Video: 59472383<br>Text: And welcome back to the week six folder.\nWe're now at day two, which is the second and final stage o...", |
|
"Video: 59670171<br>Text: So as the very final step on this part four of day two of week eight, we are now going to build an\ne...", |
|
"Video: 59297721<br>Text: And so now the time has come to talk about the most crucial aspect of Rag, which is the idea of vect...", |
|
"Video: 59297599<br>Text: Well, that was a sneaky detour I took you on in the last one.\nI hope you enjoyed it though, and I ho...", |
|
"Video: 59507635<br>Text: Look, I hope you're excited.\nYou really should be.\nYou've been through 80% of the course and it's al...", |
|
"Video: 59669375<br>Text: Here we are for the day.\n2.1 notebook.\nAnd don't let it be said that I don't ever do anything for yo...", |
|
"Video: 59297733<br>Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", |
|
"Video: 59670369<br>Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", |
|
"Video: 59166281<br>Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", |
|
"Video: 59671567<br>Text: Well, the first thing you're going to notice is that I don't have a notebook open for you.\nAnd that'...", |
|
"Video: 59297593<br>Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", |
|
"Video: 59166461<br>Text: And welcome back to the lab.\nHere we are in Jupyter Lab and we are going to go into week two.\nAnd we...", |
|
"Video: 59167007<br>Text: Well, how fabulous is that?\nI hope that you are as wowed as I am by our new airline, I assistant and...", |
|
"Video: 59508121<br>Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", |
|
"Video: 59295579<br>Text: All right.\nAre you excited to see how this goes?\nLet's give it a try.\nSo in this next section, I cre...", |
|
"Video: 60620375<br>Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", |
|
"Video: 59472491<br>Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", |
|
"Video: 59472425<br>Text: Welcome to week six, day three.\nToday is going to be a day that you will either love or you will hat...", |
|
"Video: 59508057<br>Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", |
|
"Video: 60619577<br>Text: And for the final piece of background information, I wanted to take another moment to talk about API...", |
|
"Video: 59170291<br>Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", |
|
"Video: 60619651<br>Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", |
|
"Video: 59473191<br>Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", |
|
"Video: 59170297<br>Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", |
|
"Video: 59167015<br>Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", |
|
"Video: 59170043<br>Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", |
|
"Video: 59473147<br>Text: Well, I'm very relieved.\nI've got that behind me.\nNo more human testing for me.\nWe'll have one final...", |
|
"Video: 59166453<br>Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", |
|
"Video: 59166915<br>Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", |
|
"Video: 59667365<br>Text: Here we are back in Colab, looking at the week seven, day five of the Colab notebooks and I'm on a\nT...", |
|
"Video: 60616845<br>Text: We're on the home stretch.\nThis is the final step in the environment setup, and it's an easy one.\nIt...", |
|
"Video: 59295459<br>Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", |
|
"Video: 59471979<br>Text: So we now turn to the parts of the problem, which is perhaps, let's say, not as glamorous as some\nof...", |
|
"Video: 59503705<br>Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", |
|
"Video: 59472505<br>Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", |
|
"Video: 59669217<br>Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", |
|
"Video: 59671221<br>Text: I gotta tell you, I don't like to toot my horn a whole lot, but I do think that I've done a great\njo...", |
|
"Video: 59503703<br>Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", |
|
"Video: 59473201<br>Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", |
|
"Video: 60622463<br>Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", |
|
"Video: 60619299<br>Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", |
|
"Video: 59295607<br>Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", |
|
"Video: 59297575<br>Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", |
|
"Video: 59507687<br>Text: It's time for action, everybody.\nWe've set up our colab.\nHere we are, week seven, day three.\nWe've g...", |
|
"Video: 59671441<br>Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", |
|
"Video: 59673431<br>Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", |
|
"Video: 59473137<br>Text: Let's get straight to it.\nSo the place where you can see everything that's going on and get knee dee...", |
|
"Video: 59166421<br>Text: Welcome back to the radio day in the lab.\nMore to do.\nLet's keep going.\nWhere we left off is we had ...", |
|
"Video: 59295599<br>Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", |
|
"Video: 59669631<br>Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", |
|
"Video: 59673663<br>Text: But wait, there's more.\nWe need to add some more to the user interface just to make it look more coo...", |
|
"Video: 59506929<br>Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", |
|
"Video: 59504785<br>Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", |
|
"Video: 59505337<br>Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", |
|
"Video: 59271655<br>Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", |
|
"Video: 59472883<br>Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", |
|
"Video: 59673639<br>Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", |
|
"Video: 59472463<br>Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", |
|
"Video: 59297595<br>Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", |
|
"Video: 60619149<br>Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", |
|
"Video: 59297735<br>Text: And at last the time has come to see rag in action.\nAfter all of this talk, and here we are.\nWe're i...", |
|
"Video: 60616407<br>Text: And now over to my Mac people.\nAnd I have news for you.\nIt's exactly the same thing.\nYou go to a fav...", |
|
"Video: 59170235<br>Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", |
|
"Video: 59472067<br>Text: So we've covered steps 1 to 4 of the five step strategy.\nAnd that brings us to step five, which is p...", |
|
"Video: 59472011<br>Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", |
|
"Video: 59295553<br>Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", |
|
"Video: 59297773<br>Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", |
|
"Video: 59295583<br>Text: And here we are back in JupyterLab.\nIt's been a minute.\nWe've been working in Colab for last week, a...", |
|
"Video: 59507329<br>Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", |
|
"Video: 59295429<br>Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", |
|
"Video: 60595637<br>Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", |
|
"Video: 59668027<br>Text: And so here we are at the home page for modal.\nAt modal.com spelt model not not model which is confu...", |
|
"Video: 59295527<br>Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", |
|
"Video: 59295377<br>Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", |
|
"Video: 59666211<br>Text: So before we try our new model and one more recap on the models so far and keep notes of this so we\n...", |
|
"Video: 59170107<br>Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", |
|
"Video: 60616833<br>Text: So I realized that day one of week one has been a pretty long day, and I assure you that the other,\n...", |
|
"Video: 59472413<br>Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", |
|
"Video: 59297561<br>Text: And would you believe at this point you're 55% of the way along the journey?\nUh, it's been a while s...", |
|
"Video: 59669211<br>Text: Well, we took on a lot today and we seem to have been successful.\nThese red icons that you see on th...", |
|
"Video: 59166981<br>Text: Welcome to week two, day five.\nThe last day of week two where a lot is coming together.\nI am so grat...", |
|
"Video: 60619227<br>Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", |
|
"Video: 60620395<br>Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", |
|
"Video: 59665127<br>Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", |
|
"Video: 59668923<br>Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", |
|
"Video: 59504887<br>Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", |
|
"Video: 59170165<br>Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", |
|
"Video: 60617251<br>Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", |
|
"Video: 59166951<br>Text: All right, back to the lab.\nBack to our project.\nTime to work with tools.\nI am in the week two folde...", |
|
"Video: 60619619<br>Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", |
|
"Video: 60616663<br>Text: Well.\nHi there, this is time for PC people to get set up.\nSo all you Mac people out there, you don't...", |
|
"Video: 59508175<br>Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", |
|
"Video: 59670087<br>Text: And welcome to part four of day two of week eight.\nUh, there's a lot happening this week, and I have...", |
|
"Video: 59506713<br>Text: Hi everyone.\nSo the reason I'm so fired up about week seven is that this is the time when we actuall...", |
|
"Video: 60620169<br>Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", |
|
"Video: 59295435<br>Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", |
|
"Video: 59297609<br>Text: Last week, we worked with models that were able to speed up code by a factor of 60,000 times, which\n...", |
|
"Video: 59507489<br>Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", |
|
"Video: 59295549<br>Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", |
|
"Video: 59665129<br>Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", |
|
"Video: 59169991<br>Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", |
|
"Video: 59472027<br>Text: And now the time has come to curate our data set.\nAnd the way we're going to do this is we're going ...", |
|
"Video: 59472307<br>Text: Welcome to week six.\nDay two a day.\nWhen we get back into the data, we look back in anger at our dat...", |
|
"Video: 59508289<br>Text: So here we are now, back in the Colab, in the same one that we kicked off in the previous day.\nIt's ...", |
|
"Video: 59472333<br>Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", |
|
"Video: 59295431<br>Text: Now I want to take a quick moment to give you a flyby of five different ways that llms are used comm...", |
|
"Video: 59673449<br>Text: Well, I have to tell you that I'm a little bit sad.\nThis is the beginning of the beginning of the en...", |
|
"Video: 59669389<br>Text: Well.\nHi there.\nSo you've made it to day two of week eight, and I am super grateful that you've been...", |
|
"Video: 59170057<br>Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", |
|
"Video: 59166949<br>Text: Welcome back to making chatbots.\nLet's keep going.\nSo for the next part we're going to beef up the s...", |
|
"Video: 59473019<br>Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", |
|
"Video: 59297585<br>Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", |
|
"Video: 59170255<br>Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", |
|
"Video: 60614589<br>Text: So we're now going to run a large language model directly on your box using a platform called llama,...", |
|
"Video: 59297601<br>Text: I'm not going to lie, at this point you have every reason to be impatient with me.\nWe've been yammer...", |
|
"Video: 60616629<br>Text: And welcome back to team PC and Team Mac as we come back together again for a quick video.\nIn this o...", |
|
"Video: 59297749<br>Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", |
|
"Video: 59170135<br>Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", |
|
"Video: 59472017<br>Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", |
|
"Video: 59507017<br>Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", |
|
"Video: 60619883<br>Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", |
|
"Video: 59508297<br>Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", |
|
"Video: 60619247<br>Text: We're going to spend a little bit more time with GPT just to try out a few more interesting things.\n...", |
|
"Video: 59504769<br>Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", |
|
"Video: 59170233<br>Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", |
|
"Video: 59671231<br>Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", |
|
"Video: 60620397<br>Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", |
|
"Video: 59170093<br>Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", |
|
"Video: 59473089<br>Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", |
|
"Video: 60395261<br>Text: Let's keep going with our project to equip our LM with a tool.\nWe just created this piece of code to...", |
|
"Video: 60617259<br>Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", |
|
"Video: 59507313<br>Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", |
|
"Video: 60619721<br>Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", |
|
"Video: 59295451<br>Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", |
|
"Video: 59166919<br>Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", |
|
"Video: 59295441<br>Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", |
|
"Video: 59295541<br>Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", |
|
"Video: 59473101<br>Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", |
|
"Video: 59507423<br>Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", |
|
"Video: 59295545<br>Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", |
|
"Video: 59472503<br>Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", |
|
"Video: 60614591<br>Text: The mantra of this course is that the best way to learn is by doing, and we will be doing stuff toge...", |
|
"Video: 59473021<br>Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", |
|
"Video: 60617255<br>Text: I'm now going to talk for a bit about models.\nA term you often hear is the term frontier models, whi...", |
|
"Video: 59667829<br>Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", |
|
"Video: 59505329<br>Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", |
|
"Video: 59669049<br>Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", |
|
"Video: 60619439<br>Text: This now brings us to an extremely important property of LMS called the context window that I want t...", |
|
"Video: 59668181<br>Text: And so it gives me great pleasure to introduce to you the project that I've lined up for you this we...", |
|
"Video: 59472441<br>Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", |
|
"Video: 59507785<br>Text: Well, I'm sure you're fed up of me saying that the moment of truth has arrived, but it really has.\nT...", |
|
"Video: 59295587<br>Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", |
|
"Video: 59166465<br>Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", |
|
"Video: 59473071<br>Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", |
|
"Video: 59295423<br>Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", |
|
"Video: 59297723<br>Text: So I know what you're thinking.\nYou're thinking, what's going on here?\nWe're on day five.\nWe're on d...", |
|
"Video: 59166947<br>Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", |
|
"Video: 59666831<br>Text: Take one more moment to look at this very nice diagram that lays it all out, and we will move on.\nNo...", |
|
"Video: 59295493<br>Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", |
|
"Video: 60616855<br>Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", |
|
"Video: 59506611<br>Text: So in a future day, I'm going to be training, fine tuning a model and creating a fine tuned model.\nA...", |
|
"Video: 60616493<br>Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", |
|
"Video: 59166317<br>Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", |
|
"Video: 59295439<br>Text: So I'm aware that there's a big risk that you are getting fed up of leaderboards, because we've done...", |
|
"Video: 59472421<br>Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", |
|
"Video: 59472137<br>Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", |
|
"Video: 59297693<br>Text: So at the end of each week, it's customary for me to give you a challenge, an assignment to do on\nyo...", |
|
"Video: 60620143<br>Text: So we're going to make a call to GPT four.\nOh, that's going to ask it to look through a set of links...", |
|
"Video: 60619501<br>Text: I welcome to day four of our time together.\nThis is a very important day.\nToday we're going to be lo...", |
|
"Video: 59297743<br>Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", |
|
"Video: 59166847<br>Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", |
|
"Video: 59170223<br>Text: Well.\nFantastic.\nIt's coming up to the end of the week, and that means it's coming up to a challenge...", |
|
"Video: 59170037<br>Text: So how does it feel to be 30% of the way down the journey to being a proficient LLM engineer?\nTake a...", |
|
"Video: 59295609<br>Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", |
|
"Video: 60619281<br>Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", |
|
"Video: 59472429<br>Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", |
|
"Video: 59167009<br>Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", |
|
"Video: 59166481<br>Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", |
|
"Video: 59670933<br>Text: I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very sat...", |
|
"Video: 59670073<br>Text: Okay, it's time to complete the Rag workflow in our Jupyter Lab on day 2.3.\nWe've got this function ...", |
|
"Video: 59673595<br>Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", |
|
"Video: 59297603<br>Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", |
|
"Video: 60614541<br>Text: I am delighted to welcome you to the first day of our eight weeks together as you join me on this ad...", |
|
"Video: 59667357<br>Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", |
|
"Video: 59667841<br>Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", |
|
"Video: 59472007<br>Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", |
|
"Video: 59507435<br>Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", |
|
"Video: 59509185<br>Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", |
|
"Video: 59473159<br>Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", |
|
"Video: 60619447<br>Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", |
|
"Video: 59166353<br>Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", |
|
"Video: 60619123<br>Text: So what we're now going to do is we're going to look at some models in practice and start to compare...", |
|
"Video: 59295363<br>Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", |
|
"Video: 60619289<br>Text: And now we'll go a bit faster through the other models.\nWe'll start with Google's Gemini.\nI have the...", |
|
"Video: 59472873<br>Text: So it's quite an adventure that we had at the frontier of what's capable with Llms today, solving a\n...", |
|
"Video: 60619429<br>Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", |
|
"Video: 59295601<br>Text: So it's time to continue our journey into the world of open source and understand which models we sh...", |
|
"Video: 59170025<br>Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", |
|
"Video: 59166443<br>Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", |
|
"Video: 60620025<br>Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", |
|
"Video: 59170055<br>Text: Welcome to the world of Google Colab.\nYou may already be very familiar with Google Colab, even if so..." |
|
], |
|
"type": "scatter3d", |
|
"x": [ |
|
1.7087736, |
|
-23.05743, |
|
-28.06106, |
|
53.49951, |
|
-25.6022, |
|
-38.486794, |
|
19.744888, |
|
40.88814, |
|
16.016825, |
|
-7.811325, |
|
-42.27272, |
|
12.64262, |
|
-0.9483807, |
|
6.6331143, |
|
30.734392, |
|
-11.433903, |
|
4.802981, |
|
-38.28026, |
|
-65.32751, |
|
-70.74078, |
|
17.332169, |
|
-52.152122, |
|
-59.27239, |
|
33.891144, |
|
31.012686, |
|
-3.4545732, |
|
-64.21017, |
|
7.6461124, |
|
20.45596, |
|
17.49392, |
|
58.194645, |
|
23.335354, |
|
-1.4417802, |
|
-31.790943, |
|
16.829693, |
|
9.377639, |
|
-35.48943, |
|
21.703556, |
|
-32.651184, |
|
34.197903, |
|
8.165246, |
|
-16.031826, |
|
-15.671898, |
|
24.559917, |
|
12.978084, |
|
-2.6771584, |
|
-40.945656, |
|
36.925316, |
|
25.997892, |
|
-6.9610124, |
|
-10.469476, |
|
-64.6909, |
|
1.2878436, |
|
11.471338, |
|
-9.606986, |
|
-36.224865, |
|
52.36488, |
|
18.624384, |
|
-75.35086, |
|
7.6970425, |
|
-28.021814, |
|
4.318845, |
|
21.450777, |
|
6.252253, |
|
8.603412, |
|
-42.26961, |
|
-4.845308, |
|
18.71574, |
|
26.781273, |
|
-0.79253143, |
|
-14.978188, |
|
18.245869, |
|
-12.82021, |
|
-46.9319, |
|
-74.53214, |
|
65.01824, |
|
-55.562347, |
|
-16.433084, |
|
-34.34535, |
|
37.855953, |
|
23.34828, |
|
58.13494, |
|
-62.33339, |
|
23.672892, |
|
3.081372, |
|
54.94866, |
|
26.811195, |
|
-17.7189, |
|
8.902483, |
|
43.160995, |
|
-9.250307, |
|
-15.630141, |
|
17.22394, |
|
-55.25729, |
|
-83.02552, |
|
30.956335, |
|
9.167712, |
|
44.13426, |
|
-11.525453, |
|
17.694338, |
|
-5.0039816, |
|
9.241007, |
|
-22.265665, |
|
1.0213552, |
|
-1.9952722, |
|
31.26171, |
|
-44.436382, |
|
8.186024, |
|
-38.107677, |
|
20.091564, |
|
-47.018497, |
|
3.9196463, |
|
-46.056137, |
|
29.834492, |
|
51.38648, |
|
9.7722, |
|
-39.721962, |
|
-11.258467, |
|
38.1706, |
|
25.899416, |
|
-22.391533, |
|
64.70646, |
|
8.984558, |
|
-8.005773, |
|
-22.550919, |
|
29.339518, |
|
38.68547, |
|
-58.67559, |
|
-38.17486, |
|
-21.293037, |
|
-59.715446, |
|
-32.520184, |
|
-55.803455, |
|
-9.595691, |
|
4.706987, |
|
1.6881931, |
|
-37.37762, |
|
26.374004, |
|
76.44756, |
|
1.4116235, |
|
-8.510549, |
|
-13.362774, |
|
-50.184566, |
|
-10.902527, |
|
-4.4753523, |
|
-6.0763307, |
|
-7.691084, |
|
0.769521, |
|
23.278786, |
|
37.985294, |
|
11.939553, |
|
39.230835, |
|
59.653934, |
|
43.122715, |
|
-8.87973, |
|
4.4371753, |
|
-48.39784, |
|
-10.907453, |
|
-26.853792, |
|
35.47057, |
|
6.833131, |
|
59.59064, |
|
19.819576, |
|
38.58615, |
|
-16.264578, |
|
-53.5018, |
|
10.727256, |
|
42.230526, |
|
17.628677, |
|
-24.103918, |
|
52.2294, |
|
-77.18268, |
|
3.6058846, |
|
19.204115, |
|
-4.63787, |
|
-4.450328, |
|
-2.8182654, |
|
-46.7583, |
|
17.780125, |
|
57.05202, |
|
-20.470179, |
|
-23.03723, |
|
-15.013553, |
|
-61.297047, |
|
53.65074, |
|
-63.843815, |
|
36.721672, |
|
1.2968292, |
|
-1.146437, |
|
30.53313, |
|
47.650024, |
|
-35.42971, |
|
13.790592, |
|
-29.44714, |
|
-7.0857954, |
|
-31.83992, |
|
4.395385, |
|
-71.52093, |
|
-38.636032, |
|
-10.17397, |
|
13.551749, |
|
26.199244, |
|
32.304344, |
|
37.940987, |
|
-19.058989, |
|
35.280716, |
|
28.176294, |
|
63.618996, |
|
50.98304, |
|
70.33112, |
|
-23.338556, |
|
55.944035, |
|
21.928713, |
|
-24.126383, |
|
20.637466, |
|
-27.234331, |
|
-34.206 |
|
], |
|
"y": [ |
|
-4.252548, |
|
-43.394333, |
|
-15.430764, |
|
3.913298, |
|
4.4845552, |
|
-33.13936, |
|
-22.152138, |
|
26.412823, |
|
30.148039, |
|
59.570904, |
|
48.391426, |
|
-12.528983, |
|
-30.37183, |
|
60.731644, |
|
27.484875, |
|
14.465288, |
|
-69.40243, |
|
0.97867054, |
|
-5.723522, |
|
2.7253983, |
|
7.151598, |
|
-40.23502, |
|
-24.437897, |
|
44.777378, |
|
2.2172062, |
|
-15.407989, |
|
-9.227094, |
|
-35.85389, |
|
19.15898, |
|
37.333565, |
|
-32.59874, |
|
12.68407, |
|
-73.58059, |
|
-6.1550703, |
|
54.37412, |
|
-45.11716, |
|
24.515192, |
|
-40.404133, |
|
7.894027, |
|
40.451534, |
|
-45.810196, |
|
32.84157, |
|
-29.400854, |
|
-36.06799, |
|
-26.307, |
|
-34.34112, |
|
16.958231, |
|
-4.426608, |
|
-51.174706, |
|
-10.152972, |
|
-61.325924, |
|
-40.93275, |
|
1.7414787, |
|
25.57757, |
|
-6.57861, |
|
23.91178, |
|
-12.384486, |
|
-16.934166, |
|
-12.787716, |
|
46.80957, |
|
-40.928993, |
|
-23.344496, |
|
57.658195, |
|
-52.681698, |
|
-29.147705, |
|
-26.069113, |
|
-37.631737, |
|
6.913289, |
|
1.0152185, |
|
-22.000841, |
|
40.077843, |
|
68.951485, |
|
-34.20306, |
|
-10.534028, |
|
-17.659843, |
|
-15.2762165, |
|
-31.812723, |
|
41.836502, |
|
44.55901, |
|
42.523884, |
|
21.308317, |
|
-16.114529, |
|
-24.19872, |
|
-28.394356, |
|
72.398735, |
|
-11.762284, |
|
39.155792, |
|
62.174786, |
|
0.33776075, |
|
-9.822582, |
|
7.8490186, |
|
38.088703, |
|
3.910647, |
|
-21.867012, |
|
-9.80895, |
|
34.229267, |
|
6.49524, |
|
-20.215645, |
|
-23.823503, |
|
13.815909, |
|
-10.719444, |
|
29.71537, |
|
27.11394, |
|
8.893772, |
|
-42.861084, |
|
11.520209, |
|
31.976051, |
|
-61.493744, |
|
-10.253941, |
|
20.047174, |
|
7.7775283, |
|
16.061588, |
|
-27.2339, |
|
9.338753, |
|
-28.769846, |
|
4.966599, |
|
66.91598, |
|
-58.99846, |
|
28.998318, |
|
35.759415, |
|
-14.775799, |
|
15.561535, |
|
23.844439, |
|
13.185903, |
|
50.08121, |
|
-53.279182, |
|
52.965717, |
|
-9.916494, |
|
30.322853, |
|
38.71735, |
|
-20.32845, |
|
33.65194, |
|
-45.906616, |
|
37.194542, |
|
-58.197693, |
|
68.45129, |
|
19.058973, |
|
30.657938, |
|
-11.88528, |
|
-3.2491598, |
|
67.76955, |
|
-1.6988521, |
|
19.30631, |
|
52.74911, |
|
-24.831163, |
|
-32.65019, |
|
-10.958649, |
|
0.82742673, |
|
-21.389397, |
|
14.101158, |
|
2.1391983, |
|
6.576113, |
|
-32.843567, |
|
60.250656, |
|
-43.65164, |
|
-10.533554, |
|
-25.26452, |
|
50.190014, |
|
-22.556831, |
|
-1.8844366, |
|
17.620245, |
|
25.576294, |
|
53.109592, |
|
-41.58676, |
|
8.641028, |
|
-33.405598, |
|
39.503387, |
|
-40.254204, |
|
-46.44093, |
|
13.338996, |
|
9.385, |
|
-31.900993, |
|
-10.131737, |
|
-5.4291334, |
|
9.878982, |
|
-47.344704, |
|
9.33157, |
|
51.674915, |
|
-31.377905, |
|
-4.2005377, |
|
-12.071655, |
|
-3.6661708, |
|
37.244083, |
|
3.2858796, |
|
-25.261751, |
|
-48.280323, |
|
11.631785, |
|
32.637978, |
|
45.047813, |
|
-23.116121, |
|
-6.183171, |
|
-23.86113, |
|
0.017425848, |
|
8.19719, |
|
22.400421, |
|
-40.894783, |
|
29.179394, |
|
-18.357765, |
|
43.00136, |
|
-4.4837027, |
|
41.68122, |
|
-36.107216, |
|
21.893982, |
|
34.812412, |
|
-32.88127, |
|
-17.11192, |
|
-10.238356, |
|
-1.9124643, |
|
21.319334, |
|
-2.981173, |
|
-1.3924571, |
|
-41.355488, |
|
-4.402796, |
|
45.275204, |
|
-19.099257, |
|
-28.038015, |
|
63.64564 |
|
], |
|
"z": [ |
|
-80.01053, |
|
6.952257, |
|
39.770596, |
|
16.702005, |
|
5.445383, |
|
-15.32626, |
|
-0.5249115, |
|
29.32656, |
|
36.423714, |
|
-14.892507, |
|
-8.28791, |
|
23.206917, |
|
57.858578, |
|
-50.514557, |
|
64.206955, |
|
18.315903, |
|
-0.5376158, |
|
-15.617648, |
|
-0.26207563, |
|
-29.67441, |
|
-71.75039, |
|
8.197639, |
|
-14.2429905, |
|
43.300938, |
|
38.940685, |
|
68.43502, |
|
-23.888317, |
|
48.1142, |
|
49.111935, |
|
-61.746227, |
|
50.52906, |
|
32.868515, |
|
-13.877641, |
|
7.967563, |
|
-50.249985, |
|
-49.565216, |
|
-2.7943447, |
|
-46.210426, |
|
-54.42825, |
|
-20.080788, |
|
15.767397, |
|
-16.436441, |
|
-31.49947, |
|
27.740046, |
|
31.81843, |
|
-10.746491, |
|
68.19509, |
|
-17.48625, |
|
-12.825234, |
|
-71.884796, |
|
-16.59812, |
|
12.649029, |
|
70.53656, |
|
19.578947, |
|
-38.99613, |
|
53.66051, |
|
-4.5801187, |
|
-19.734165, |
|
-33.125164, |
|
-28.618763, |
|
23.614397, |
|
77.43594, |
|
-32.899113, |
|
46.40308, |
|
8.167681, |
|
-2.8620894, |
|
74.148186, |
|
-26.347952, |
|
-74.90554, |
|
-77.46363, |
|
-28.790958, |
|
20.622337, |
|
64.1211, |
|
9.823497, |
|
-7.1779866, |
|
10.737146, |
|
-29.82501, |
|
-67.2842, |
|
-18.324295, |
|
4.6903768, |
|
-0.48786125, |
|
44.55313, |
|
-0.092902616, |
|
13.796377, |
|
27.559351, |
|
-36.15748, |
|
-36.40333, |
|
-38.448048, |
|
-5.13925, |
|
-45.601143, |
|
-32.268417, |
|
10.468017, |
|
16.537827, |
|
40.53047, |
|
-1.0723572, |
|
52.555515, |
|
53.437595, |
|
12.123496, |
|
38.82272, |
|
-48.952595, |
|
12.853546, |
|
-30.875723, |
|
1.1754398, |
|
18.969849, |
|
28.55506, |
|
22.07326, |
|
57.09251, |
|
-26.802582, |
|
-10.858276, |
|
-13.749718, |
|
36.731487, |
|
0.24828485, |
|
-43.257736, |
|
-55.938084, |
|
39.296425, |
|
-53.445366, |
|
-17.457575, |
|
-2.9673405, |
|
0.0901862, |
|
-49.77096, |
|
-25.705078, |
|
-13.610352, |
|
67.12242, |
|
35.858795, |
|
9.70463, |
|
20.889431, |
|
-43.310135, |
|
-42.36413, |
|
-33.766987, |
|
-57.633263, |
|
-35.28791, |
|
64.09733, |
|
-30.020395, |
|
-4.3398356, |
|
-11.234243, |
|
-16.331018, |
|
22.371712, |
|
17.535992, |
|
25.282942, |
|
-62.60095, |
|
0.4806009, |
|
70.375824, |
|
20.449402, |
|
-2.0014663, |
|
-32.865536, |
|
23.095472, |
|
27.847054, |
|
-30.648935, |
|
-56.0284, |
|
-7.3562527, |
|
41.76956, |
|
-24.99873, |
|
31.66762, |
|
-29.629705, |
|
-15.31758, |
|
-9.096767, |
|
26.896555, |
|
44.854942, |
|
7.493553, |
|
4.068373, |
|
34.71581, |
|
-46.640224, |
|
-8.490605, |
|
-48.098576, |
|
50.40989, |
|
35.75695, |
|
-43.580498, |
|
43.48538, |
|
43.563065, |
|
-47.500908, |
|
-17.975456, |
|
-4.8319664, |
|
43.81085, |
|
-46.34204, |
|
-4.954837, |
|
-51.740547, |
|
-68.37514, |
|
29.03883, |
|
56.43028, |
|
-23.674625, |
|
-9.0214, |
|
20.377613, |
|
37.052822, |
|
35.568115, |
|
-8.854601, |
|
-12.067132, |
|
49.907764, |
|
36.929363, |
|
16.495632, |
|
-27.568445, |
|
7.129844, |
|
-50.75493, |
|
69.71066, |
|
32.083324, |
|
56.811436, |
|
-21.762302, |
|
54.430317, |
|
8.924631, |
|
33.06066, |
|
-21.813917, |
|
-3.1104941, |
|
-21.70847, |
|
-63.661057, |
|
-45.242672, |
|
-2.3619707, |
|
-58.077934, |
|
52.1452, |
|
-1.9540714, |
|
-23.772692, |
|
14.296897, |
|
-31.250578, |
|
-1.1164938, |
|
-8.393525, |
|
-8.107033, |
|
41.62983, |
|
34.141304, |
|
-26.842785 |
|
] |
|
} |
|
], |
|
"layout": { |
|
"height": 700, |
|
"margin": { |
|
"b": 10, |
|
"l": 10, |
|
"r": 20, |
|
"t": 40 |
|
}, |
|
"scene": { |
|
"xaxis": { |
|
"title": { |
|
"text": "x" |
|
} |
|
}, |
|
"yaxis": { |
|
"title": { |
|
"text": "y" |
|
} |
|
}, |
|
"zaxis": { |
|
"title": { |
|
"text": "z" |
|
} |
|
} |
|
}, |
|
"template": { |
|
"data": { |
|
"bar": [ |
|
{ |
|
"error_x": { |
|
"color": "#2a3f5f" |
|
}, |
|
"error_y": { |
|
"color": "#2a3f5f" |
|
}, |
|
"marker": { |
|
"line": { |
|
"color": "#E5ECF6", |
|
"width": 0.5 |
|
}, |
|
"pattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
} |
|
}, |
|
"type": "bar" |
|
} |
|
], |
|
"barpolar": [ |
|
{ |
|
"marker": { |
|
"line": { |
|
"color": "#E5ECF6", |
|
"width": 0.5 |
|
}, |
|
"pattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
} |
|
}, |
|
"type": "barpolar" |
|
} |
|
], |
|
"carpet": [ |
|
{ |
|
"aaxis": { |
|
"endlinecolor": "#2a3f5f", |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"minorgridcolor": "white", |
|
"startlinecolor": "#2a3f5f" |
|
}, |
|
"baxis": { |
|
"endlinecolor": "#2a3f5f", |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"minorgridcolor": "white", |
|
"startlinecolor": "#2a3f5f" |
|
}, |
|
"type": "carpet" |
|
} |
|
], |
|
"choropleth": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"type": "choropleth" |
|
} |
|
], |
|
"contour": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "contour" |
|
} |
|
], |
|
"contourcarpet": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"type": "contourcarpet" |
|
} |
|
], |
|
"heatmap": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "heatmap" |
|
} |
|
], |
|
"heatmapgl": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "heatmapgl" |
|
} |
|
], |
|
"histogram": [ |
|
{ |
|
"marker": { |
|
"pattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
} |
|
}, |
|
"type": "histogram" |
|
} |
|
], |
|
"histogram2d": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "histogram2d" |
|
} |
|
], |
|
"histogram2dcontour": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "histogram2dcontour" |
|
} |
|
], |
|
"mesh3d": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"type": "mesh3d" |
|
} |
|
], |
|
"parcoords": [ |
|
{ |
|
"line": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "parcoords" |
|
} |
|
], |
|
"pie": [ |
|
{ |
|
"automargin": true, |
|
"type": "pie" |
|
} |
|
], |
|
"scatter": [ |
|
{ |
|
"fillpattern": { |
|
"fillmode": "overlay", |
|
"size": 10, |
|
"solidity": 0.2 |
|
}, |
|
"type": "scatter" |
|
} |
|
], |
|
"scatter3d": [ |
|
{ |
|
"line": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatter3d" |
|
} |
|
], |
|
"scattercarpet": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattercarpet" |
|
} |
|
], |
|
"scattergeo": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattergeo" |
|
} |
|
], |
|
"scattergl": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattergl" |
|
} |
|
], |
|
"scattermapbox": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scattermapbox" |
|
} |
|
], |
|
"scatterpolar": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatterpolar" |
|
} |
|
], |
|
"scatterpolargl": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatterpolargl" |
|
} |
|
], |
|
"scatterternary": [ |
|
{ |
|
"marker": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"type": "scatterternary" |
|
} |
|
], |
|
"surface": [ |
|
{ |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
}, |
|
"colorscale": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"type": "surface" |
|
} |
|
], |
|
"table": [ |
|
{ |
|
"cells": { |
|
"fill": { |
|
"color": "#EBF0F8" |
|
}, |
|
"line": { |
|
"color": "white" |
|
} |
|
}, |
|
"header": { |
|
"fill": { |
|
"color": "#C8D4E3" |
|
}, |
|
"line": { |
|
"color": "white" |
|
} |
|
}, |
|
"type": "table" |
|
} |
|
] |
|
}, |
|
"layout": { |
|
"annotationdefaults": { |
|
"arrowcolor": "#2a3f5f", |
|
"arrowhead": 0, |
|
"arrowwidth": 1 |
|
}, |
|
"autotypenumbers": "strict", |
|
"coloraxis": { |
|
"colorbar": { |
|
"outlinewidth": 0, |
|
"ticks": "" |
|
} |
|
}, |
|
"colorscale": { |
|
"diverging": [ |
|
[ |
|
0, |
|
"#8e0152" |
|
], |
|
[ |
|
0.1, |
|
"#c51b7d" |
|
], |
|
[ |
|
0.2, |
|
"#de77ae" |
|
], |
|
[ |
|
0.3, |
|
"#f1b6da" |
|
], |
|
[ |
|
0.4, |
|
"#fde0ef" |
|
], |
|
[ |
|
0.5, |
|
"#f7f7f7" |
|
], |
|
[ |
|
0.6, |
|
"#e6f5d0" |
|
], |
|
[ |
|
0.7, |
|
"#b8e186" |
|
], |
|
[ |
|
0.8, |
|
"#7fbc41" |
|
], |
|
[ |
|
0.9, |
|
"#4d9221" |
|
], |
|
[ |
|
1, |
|
"#276419" |
|
] |
|
], |
|
"sequential": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
], |
|
"sequentialminus": [ |
|
[ |
|
0, |
|
"#0d0887" |
|
], |
|
[ |
|
0.1111111111111111, |
|
"#46039f" |
|
], |
|
[ |
|
0.2222222222222222, |
|
"#7201a8" |
|
], |
|
[ |
|
0.3333333333333333, |
|
"#9c179e" |
|
], |
|
[ |
|
0.4444444444444444, |
|
"#bd3786" |
|
], |
|
[ |
|
0.5555555555555556, |
|
"#d8576b" |
|
], |
|
[ |
|
0.6666666666666666, |
|
"#ed7953" |
|
], |
|
[ |
|
0.7777777777777778, |
|
"#fb9f3a" |
|
], |
|
[ |
|
0.8888888888888888, |
|
"#fdca26" |
|
], |
|
[ |
|
1, |
|
"#f0f921" |
|
] |
|
] |
|
}, |
|
"colorway": [ |
|
"#636efa", |
|
"#EF553B", |
|
"#00cc96", |
|
"#ab63fa", |
|
"#FFA15A", |
|
"#19d3f3", |
|
"#FF6692", |
|
"#B6E880", |
|
"#FF97FF", |
|
"#FECB52" |
|
], |
|
"font": { |
|
"color": "#2a3f5f" |
|
}, |
|
"geo": { |
|
"bgcolor": "white", |
|
"lakecolor": "white", |
|
"landcolor": "#E5ECF6", |
|
"showlakes": true, |
|
"showland": true, |
|
"subunitcolor": "white" |
|
}, |
|
"hoverlabel": { |
|
"align": "left" |
|
}, |
|
"hovermode": "closest", |
|
"mapbox": { |
|
"style": "light" |
|
}, |
|
"paper_bgcolor": "white", |
|
"plot_bgcolor": "#E5ECF6", |
|
"polar": { |
|
"angularaxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
}, |
|
"bgcolor": "#E5ECF6", |
|
"radialaxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
} |
|
}, |
|
"scene": { |
|
"xaxis": { |
|
"backgroundcolor": "#E5ECF6", |
|
"gridcolor": "white", |
|
"gridwidth": 2, |
|
"linecolor": "white", |
|
"showbackground": true, |
|
"ticks": "", |
|
"zerolinecolor": "white" |
|
}, |
|
"yaxis": { |
|
"backgroundcolor": "#E5ECF6", |
|
"gridcolor": "white", |
|
"gridwidth": 2, |
|
"linecolor": "white", |
|
"showbackground": true, |
|
"ticks": "", |
|
"zerolinecolor": "white" |
|
}, |
|
"zaxis": { |
|
"backgroundcolor": "#E5ECF6", |
|
"gridcolor": "white", |
|
"gridwidth": 2, |
|
"linecolor": "white", |
|
"showbackground": true, |
|
"ticks": "", |
|
"zerolinecolor": "white" |
|
} |
|
}, |
|
"shapedefaults": { |
|
"line": { |
|
"color": "#2a3f5f" |
|
} |
|
}, |
|
"ternary": { |
|
"aaxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
}, |
|
"baxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
}, |
|
"bgcolor": "#E5ECF6", |
|
"caxis": { |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "" |
|
} |
|
}, |
|
"title": { |
|
"x": 0.05 |
|
}, |
|
"xaxis": { |
|
"automargin": true, |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "", |
|
"title": { |
|
"standoff": 15 |
|
}, |
|
"zerolinecolor": "white", |
|
"zerolinewidth": 2 |
|
}, |
|
"yaxis": { |
|
"automargin": true, |
|
"gridcolor": "white", |
|
"linecolor": "white", |
|
"ticks": "", |
|
"title": { |
|
"standoff": 15 |
|
}, |
|
"zerolinecolor": "white", |
|
"zerolinewidth": 2 |
|
} |
|
} |
|
}, |
|
"title": { |
|
"text": "3D Chroma Vector Store Visualization" |
|
}, |
|
"width": 900 |
|
} |
|
}, |
|
"image/png": "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", |
|
"text/html": [ |
|
"<div> <div id=\"4b1616ca-306c-4be2-80b1-9433324cfbaf\" class=\"plotly-graph-div\" style=\"height:700px; width:900px;\"></div> <script type=\"text/javascript\"> require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById(\"4b1616ca-306c-4be2-80b1-9433324cfbaf\")) { Plotly.newPlot( \"4b1616ca-306c-4be2-80b1-9433324cfbaf\", [{\"hoverinfo\":\"text\",\"marker\":{\"color\":[\"#d01f72\",\"#75195e\",\"#3678a7\",\"#5b3f83\",\"#74a788\",\"#571122\",\"#4099c1\",\"#659222\",\"#188ca3\",\"#6d4052\",\"#35303c\",\"#a9e927\",\"#29fa15\",\"#71c500\",\"#9b9d6e\",\"#cf7e83\",\"#badd6d\",\"#85fa26\",\"#22463b\",\"#ce865d\",\"#f59c06\",\"#011995\",\"#793548\",\"#ad8b14\",\"#d937bd\",\"#2b9f18\",\"#046e5c\",\"#75b5e3\",\"#c959de\",\"#72e048\",\"#8e8cab\",\"#20f2c3\",\"#64f999\",\"#e69670\",\"#6a0fce\",\"#d65c3a\",\"#7bee34\",\"#4f86b8\",\"#b43417\",\"#4dfb77\",\"#2ae342\",\"#c3e1f2\",\"#12897b\",\"#2b3af3\",\"#7ea8e9\",\"#6ad041\",\"#0bdacc\",\"#99fe53\",\"#4aaf9f\",\"#d156c8\",\"#505bd9\",\"#dc152c\",\"#b52bf6\",\"#9baca0\",\"#a03134\",\"#d43c00\",\"#5af098\",\"#2c168d\",\"#c6016b\",\"#f090af\",\"#482281\",\"#39821f\",\"#e0a8df\",\"#480c89\",\"#08808d\",\"#ac5faf\",\"#0faf59\",\"#79c82a\",\"#e6e164\",\"#0d2037\",\"#8afd40\",\"#2e1afc\",\"#3ec815\",\"#fbfef2\",\"#a63fa4\",\"#b27d2e\",\"#ca3592\",\"#b9fd23\",\"#ac9648\",\"#804ce2\",\"#9b5e28\",\"#a64739\",\"#c457d7\",\"#de30e4\",\"#1f6ab0\",\"#6ff3c5\",\"#6df6ca\",\"#ed694d\",\"#2fef1a\",\"#335dcf\",\"#845aa9\",\"#574e28\",\"#dc95ec\",\"#b2140a\",\"#15ae86\",\"#70d1d9\",\"#6f745a\",\"#b3dba5\",\"#108c41\",\"#268bba\",\"#913568\",\"#1a6fdf\",\"#422abb\",\"#cb725f\",\"#fe62a5\",\"#dfc6c7\",\"#b25d7b\",\"#bd53b1\",\"#796278\",\"#048452\",\"#c6eff5\",\"#d24e5d\",\"#fe8e92\",\"#22398f\",\"#3e5237\",\"#8069bc\",\"#7740be\",\"#cc8ec0\",\"#b280bb\",\"#91f4db\",\"#ac55ba\",\"#c97596\",\"#116019\",\"#43c2e8\",\"#2a2d25\",\"#fc2b74\",\"#ae7afe\",\"#92b4fa\",\"#dd8cd7\",\"#4862ce\",\"#af0f59\",\"#ad6bd0\",\"#3f0a72\",\"#e01073\",\"#144ada\",\"#5cb9ca\",\"#51d0da\",\"#d6d07a\",\"#b61e76\",\"#474ff9\",\"#68bece\",\"#d01b19\",\"#ee26df\",\"#2ebca4\",\"#539908\",\"#ec0a37\",\"#1a5613\",\"#da28db\",\"#246fa5\",\"#bbfe83\",\"#d54222\",\"#580c96\",\"#02cada\",\"#996ff1\",\"#e2a239\",\"#ae5204\",\"#4ce72d\",\"#2cde7f\",\"#b64eac\",\"#591ab9\",\"#a958c9\",\"#696eaa\",\"#4c4355\",\"#6a6c06\",\"#df5d2e\",\"#9780cf\",\"#682d42\",\"#efed10\",\"#1b312a\",\"#dbde1c\",\"#e1b5db\",\"#a95826\",\"#4e797a\",\"#10384a\",\"#9a5ba2\",\"#d34482\",\"#8a29da\",\"#fb9dce\",\"#ff2d6a\",\"#50f10d\",\"#f8d349\",\"#7b4427\",\"#11a70e\",\"#987252\",\"#c932c1\",\"#2d7f7d\",\"#c1e3c5\",\"#0c777d\",\"#0f8781\",\"#dd889c\",\"#799a24\",\"#4212f1\",\"#e6f378\",\"#805527\",\"#091a90\",\"#a9541c\",\"#fcdcad\",\"#01f59b\",\"#94a85d\",\"#426575\",\"#7f03bd\",\"#2dcfac\",\"#52b6df\",\"#73e76a\",\"#d70d97\",\"#601568\",\"#d4b1ce\",\"#7341ee\",\"#bb0ee6\",\"#f645e0\",\"#1c2c7e\",\"#7dd58b\",\"#4b9a93\",\"#9df332\",\"#612b32\",\"#b1c27d\",\"#3626a5\"],\"opacity\":0.8,\"size\":5},\"mode\":\"markers\",\"text\":[\"Video: 59506507\\u003cbr\\u003eText: Well, I realized that was a whole lot of theory, but I hope it gave you a good intuition that will\\nb...\",\"Video: 59671315\\u003cbr\\u003eText: Okay, so here we are, back in Jupyter Lab, ready for the next use of a frontier model, and see this\\n...\",\"Video: 60616895\\u003cbr\\u003eText: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...\",\"Video: 60619275\\u003cbr\\u003eText: And we will conclude our expedition into the world of frontier models through their chat interface b...\",\"Video: 59472693\\u003cbr\\u003eText: Friends.\\nI am absolutely exhausted.\\nI am exhausted and a little tiny bit traumatized.\\nAnd you are so...\",\"Video: 59670121\\u003cbr\\u003eText: So it's business time right now.\\nWe are going to build a Rag pipeline to estimate the price of produ...\",\"Video: 59295619\\u003cbr\\u003eText: Welcome back to the the moment when we bring it all together into a beautiful user interface.\\nBut fi...\",\"Video: 60617163\\u003cbr\\u003eText: And already that wraps up day two.\\nNow that you have built that solution.\\nAnd congratulations on tha...\",\"Video: 60616423\\u003cbr\\u003eText: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...\",\"Video: 59170227\\u003cbr\\u003eText: Welcome back to Google Colab.\\nHere we are ready to explore the wonderful world of Tokenizers.\\nSo, uh...\",\"Video: 59169985\\u003cbr\\u003eText: So I hope you enjoyed that whirlwind tour of Google Colab.\\nHere's just a little screenshot example o...\",\"Video: 60616927\\u003cbr\\u003eText: It's time for our first LM experiment at this point.\\nSo some of this you may know well, you may know...\",\"Video: 59673721\\u003cbr\\u003eText: And here we are in JupyterLab for the last time, and we are looking here at day five, the last day\\no...\",\"Video: 59508055\\u003cbr\\u003eText: I'm so very happy that you've reached this epic moment in the course and that you're hanging in ther...\",\"Video: 59670259\\u003cbr\\u003eText: It's remarkable.\\nBut you are now at the 95% point.\\nThere's 5% remaining of this course.\\nUh, maybe it...\",\"Video: 60616623\\u003cbr\\u003eText: So we're now going to start week one of the course when we are going to be looking at exploring fron...\",\"Video: 59472383\\u003cbr\\u003eText: And welcome back to the week six folder.\\nWe're now at day two, which is the second and final stage o...\",\"Video: 59670171\\u003cbr\\u003eText: So as the very final step on this part four of day two of week eight, we are now going to build an\\ne...\",\"Video: 59297721\\u003cbr\\u003eText: And so now the time has come to talk about the most crucial aspect of Rag, which is the idea of vect...\",\"Video: 59297599\\u003cbr\\u003eText: Well, that was a sneaky detour I took you on in the last one.\\nI hope you enjoyed it though, and I ho...\",\"Video: 59507635\\u003cbr\\u003eText: Look, I hope you're excited.\\nYou really should be.\\nYou've been through 80% of the course and it's al...\",\"Video: 59669375\\u003cbr\\u003eText: Here we are for the day.\\n2.1 notebook.\\nAnd don't let it be said that I don't ever do anything for yo...\",\"Video: 59297733\\u003cbr\\u003eText: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\\nLet me...\",\"Video: 59670369\\u003cbr\\u003eText: It is terrific that you're hanging on in there and making such great progress with this course.\\nAs w...\",\"Video: 59166281\\u003cbr\\u003eText: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...\",\"Video: 59671567\\u003cbr\\u003eText: Well, the first thing you're going to notice is that I don't have a notebook open for you.\\nAnd that'...\",\"Video: 59297593\\u003cbr\\u003eText: And welcome to continuing our journey with Hrag.\\nAnd today it's time to unveil Liang Chen.\\nSo first,...\",\"Video: 59166461\\u003cbr\\u003eText: And welcome back to the lab.\\nHere we are in Jupyter Lab and we are going to go into week two.\\nAnd we...\",\"Video: 59167007\\u003cbr\\u003eText: Well, how fabulous is that?\\nI hope that you are as wowed as I am by our new airline, I assistant and...\",\"Video: 59508121\\u003cbr\\u003eText: The moment has arrived.\\nHere we go.\\nWe're in fine tuning.\\nWe do fine tuning.\\nTrain.\\nThere is also a ...\",\"Video: 59295579\\u003cbr\\u003eText: All right.\\nAre you excited to see how this goes?\\nLet's give it a try.\\nSo in this next section, I cre...\",\"Video: 60620375\\u003cbr\\u003eText: And with that, we've reached an important milestone.\\nThe first week of our eight week journey is com...\",\"Video: 59472491\\u003cbr\\u003eText: Welcome back.\\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...\",\"Video: 59472425\\u003cbr\\u003eText: Welcome to week six, day three.\\nToday is going to be a day that you will either love or you will hat...\",\"Video: 59508057\\u003cbr\\u003eText: Actually slight change in plan.\\nI'm going to wrap up the day.\\nDay three at this point, and say that ...\",\"Video: 60619577\\u003cbr\\u003eText: And for the final piece of background information, I wanted to take another moment to talk about API...\",\"Video: 59170291\\u003cbr\\u003eText: Welcome back to Colab and welcome back to our business project.\\nSo again our assignment, we are due ...\",\"Video: 60619651\\u003cbr\\u003eText: I mentioned before an AI company called vellum.\\nWhen we were talking about the different questions, ...\",\"Video: 59473191\\u003cbr\\u003eText: And you thought we'd never get here.\\nHere we are in Jupyter Lab, running our fine tuning for a front...\",\"Video: 59170297\\u003cbr\\u003eText: And here we are in Google Colab, ready for fun with models.\\nSo first we do the usual Pip installs an...\",\"Video: 59167015\\u003cbr\\u003eText: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\\nAnd this is going to be lots of creativit...\",\"Video: 59170043\\u003cbr\\u003eText: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\\nIf you en...\",\"Video: 59473147\\u003cbr\\u003eText: Well, I'm very relieved.\\nI've got that behind me.\\nNo more human testing for me.\\nWe'll have one final...\",\"Video: 59166453\\u003cbr\\u003eText: Welcome back and welcome to our continuing JupyterLab experience.\\nUh, I'm hopefully going to keep yo...\",\"Video: 59166915\\u003cbr\\u003eText: Welcome back to the wonderful world of JupyterLab.\\nAnd here we are in week two.\\nDay three.\\nUh, bring...\",\"Video: 59667365\\u003cbr\\u003eText: Here we are back in Colab, looking at the week seven, day five of the Colab notebooks and I'm on a\\nT...\",\"Video: 60616845\\u003cbr\\u003eText: We're on the home stretch.\\nThis is the final step in the environment setup, and it's an easy one.\\nIt...\",\"Video: 59295459\\u003cbr\\u003eText: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\\nBut this time we'...\",\"Video: 59471979\\u003cbr\\u003eText: So we now turn to the parts of the problem, which is perhaps, let's say, not as glamorous as some\\nof...\",\"Video: 59503705\\u003cbr\\u003eText: And so now we talk about quantization the q and q Laura.\\nQ stands for quantized quantized.\\nLaura.\\nAn...\",\"Video: 59472505\\u003cbr\\u003eText: So the good news is that this is the very final video about data set curation.\\nYou were probably fed...\",\"Video: 59669217\\u003cbr\\u003eText: And welcome to the next part of visualizing the data.\\nAnd just very quickly to show it to you in 3D....\",\"Video: 59671221\\u003cbr\\u003eText: I gotta tell you, I don't like to toot my horn a whole lot, but I do think that I've done a great\\njo...\",\"Video: 59503703\\u003cbr\\u003eText: Well.\\nHello there everybody.\\nI am so grateful that you've made it through to the start of week seven...\",\"Video: 59473201\\u003cbr\\u003eText: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...\",\"Video: 60622463\\u003cbr\\u003eText: In this video, we're going to set up a full data science environment for Mac users.\\nIn the next vide...\",\"Video: 60619299\\u003cbr\\u003eText: Well, I hope you found that both educational and enjoyable.\\nAs we went through and learned so much a...\",\"Video: 59295607\\u003cbr\\u003eText: So to revisit then the solution that we built in the previous day and talk about the metrics.\\nAs I s...\",\"Video: 59297575\\u003cbr\\u003eText: Well, welcome to the final part on rag.\\nAnd this is the session where you go from being a rag expert...\",\"Video: 59507687\\u003cbr\\u003eText: It's time for action, everybody.\\nWe've set up our colab.\\nHere we are, week seven, day three.\\nWe've g...\",\"Video: 59671441\\u003cbr\\u003eText: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...\",\"Video: 59673431\\u003cbr\\u003eText: And here we have it.\\nThe user interface is completed.\\nThe extra notification came through on my phon...\",\"Video: 59473137\\u003cbr\\u003eText: Let's get straight to it.\\nSo the place where you can see everything that's going on and get knee dee...\",\"Video: 59166421\\u003cbr\\u003eText: Welcome back to the radio day in the lab.\\nMore to do.\\nLet's keep going.\\nWhere we left off is we had ...\",\"Video: 59295599\\u003cbr\\u003eText: Welcome to the Jupyter Lab for day four.\\nIt's going to look very familiar because it's actually I've...\",\"Video: 59669631\\u003cbr\\u003eText: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...\",\"Video: 59673663\\u003cbr\\u003eText: But wait, there's more.\\nWe need to add some more to the user interface just to make it look more coo...\",\"Video: 59506929\\u003cbr\\u003eText: And we return to the hugging face open LLM leaderboard.\\nThe first place you go when selecting your b...\",\"Video: 59504785\\u003cbr\\u003eText: So at this point we're going to talk about hyperparameters.\\nAnd we're going to introduce three of th...\",\"Video: 59505337\\u003cbr\\u003eText: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...\",\"Video: 59271655\\u003cbr\\u003eText: So here we are on Hugging Face's main landing page at Hugging Face Core.\\nA URL you know.\\nWell, since...\",\"Video: 59472883\\u003cbr\\u003eText: Okay, time to reveal the results.\\nIt has run to completion.\\nAnd here it is.\\nSo a moment to pause.\\nIt...\",\"Video: 59673639\\u003cbr\\u003eText: And welcome now to the code for our user interface, which we will find in this Python module.\\nPrice ...\",\"Video: 59472463\\u003cbr\\u003eText: So last time we looked at a humble linear regression model with feature engineering, and now we say\\n...\",\"Video: 59297595\\u003cbr\\u003eText: So by the time you're watching this, hopefully you have played yourself with vectors.\\nYou've created...\",\"Video: 60619149\\u003cbr\\u003eText: So we're going to start our exploration into the world of frontier models by playing with the famous...\",\"Video: 59297735\\u003cbr\\u003eText: And at last the time has come to see rag in action.\\nAfter all of this talk, and here we are.\\nWe're i...\",\"Video: 60616407\\u003cbr\\u003eText: And now over to my Mac people.\\nAnd I have news for you.\\nIt's exactly the same thing.\\nYou go to a fav...\",\"Video: 59170235\\u003cbr\\u003eText: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\\nOn ...\",\"Video: 59472067\\u003cbr\\u003eText: So we've covered steps 1 to 4 of the five step strategy.\\nAnd that brings us to step five, which is p...\",\"Video: 59472011\\u003cbr\\u003eText: Welcome everybody.\\nSo in the past I've said quite a few times, I am excited to start this this week ...\",\"Video: 59295553\\u003cbr\\u003eText: Welcome back.\\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...\",\"Video: 59297773\\u003cbr\\u003eText: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\\n...\",\"Video: 59295583\\u003cbr\\u003eText: And here we are back in JupyterLab.\\nIt's been a minute.\\nWe've been working in Colab for last week, a...\",\"Video: 59507329\\u003cbr\\u003eText: Okay.\\nIt's moment of truth time.\\nI have just taken our class tester.\\nYou remember this class?\\nUh, it...\",\"Video: 59295429\\u003cbr\\u003eText: Continuing our investigation of benchmarks, and this will become more real when we actually see some...\",\"Video: 60595637\\u003cbr\\u003eText: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\\nh...\",\"Video: 59668027\\u003cbr\\u003eText: And so here we are at the home page for modal.\\nAt modal.com spelt model not not model which is confu...\",\"Video: 59295527\\u003cbr\\u003eText: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\\nHe...\",\"Video: 59295377\\u003cbr\\u003eText: Just before we go on to some of the more advanced metrics, I want to mention for a second something\\n...\",\"Video: 59666211\\u003cbr\\u003eText: So before we try our new model and one more recap on the models so far and keep notes of this so we\\n...\",\"Video: 59170107\\u003cbr\\u003eText: And once again, it's that moment when you take a pause and congratulate yourself on another day of\\ns...\",\"Video: 60616833\\u003cbr\\u003eText: So I realized that day one of week one has been a pretty long day, and I assure you that the other,\\n...\",\"Video: 59472413\\u003cbr\\u003eText: Wonderful.\\nWhere we left off is we had just created the Get Features function, which builds our feat...\",\"Video: 59297561\\u003cbr\\u003eText: And would you believe at this point you're 55% of the way along the journey?\\nUh, it's been a while s...\",\"Video: 59669211\\u003cbr\\u003eText: Well, we took on a lot today and we seem to have been successful.\\nThese red icons that you see on th...\",\"Video: 59166981\\u003cbr\\u003eText: Welcome to week two, day five.\\nThe last day of week two where a lot is coming together.\\nI am so grat...\",\"Video: 60619227\\u003cbr\\u003eText: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\\nm...\",\"Video: 60620395\\u003cbr\\u003eText: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\\n...\",\"Video: 59665127\\u003cbr\\u003eText: Well hi there everybody.\\nI'm not going to give you my usual song and dance about how excited you are...\",\"Video: 59668923\\u003cbr\\u003eText: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\\nAnd ...\",\"Video: 59504887\\u003cbr\\u003eText: Well, here we are again in Google Colab.\\nIt's been a minute since we were here, and welcome back to ...\",\"Video: 59170165\\u003cbr\\u003eText: Welcome, everybody to the last day of week three.\\nWeek three.\\nDay five.\\nWe're here already wrapping ...\",\"Video: 60617251\\u003cbr\\u003eText: Congratulations are definitely in order.\\nYesterday was a mammoth first day on this course and you go...\",\"Video: 59166951\\u003cbr\\u003eText: All right, back to the lab.\\nBack to our project.\\nTime to work with tools.\\nI am in the week two folde...\",\"Video: 60619619\\u003cbr\\u003eText: Well, day four was an information dense day.\\nI do hope that you learned some something useful here, ...\",\"Video: 60616663\\u003cbr\\u003eText: Well.\\nHi there, this is time for PC people to get set up.\\nSo all you Mac people out there, you don't...\",\"Video: 59508175\\u003cbr\\u003eText: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\\n...\",\"Video: 59670087\\u003cbr\\u003eText: And welcome to part four of day two of week eight.\\nUh, there's a lot happening this week, and I have...\",\"Video: 59506713\\u003cbr\\u003eText: Hi everyone.\\nSo the reason I'm so fired up about week seven is that this is the time when we actuall...\",\"Video: 60620169\\u003cbr\\u003eText: Hopefully you found this super satisfying to be able to have this nice business result and have it c...\",\"Video: 59295435\\u003cbr\\u003eText: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...\",\"Video: 59297609\\u003cbr\\u003eText: Last week, we worked with models that were able to speed up code by a factor of 60,000 times, which\\n...\",\"Video: 59507489\\u003cbr\\u003eText: Continuing our adventure through hyperparameters for training.\\nThe next one is pretty crucial and it...\",\"Video: 59295549\\u003cbr\\u003eText: And welcome back to our challenge again.\\nAnd this time we are working with our beautiful prototype.\\n...\",\"Video: 59665129\\u003cbr\\u003eText: And now let me make this real for you by showing you some, some diagrams, particularly now looking\\na...\",\"Video: 59169991\\u003cbr\\u003eText: Okay, so that was your introduction to Hugging Face.\\nAnd now I'm going to turn to a different resour...\",\"Video: 59472027\\u003cbr\\u003eText: And now the time has come to curate our data set.\\nAnd the way we're going to do this is we're going ...\",\"Video: 59472307\\u003cbr\\u003eText: Welcome to week six.\\nDay two a day.\\nWhen we get back into the data, we look back in anger at our dat...\",\"Video: 59508289\\u003cbr\\u003eText: So here we are now, back in the Colab, in the same one that we kicked off in the previous day.\\nIt's ...\",\"Video: 59472333\\u003cbr\\u003eText: Thank you for putting up with me during my foray into traditional machine learning.\\nI think it was u...\",\"Video: 59295431\\u003cbr\\u003eText: Now I want to take a quick moment to give you a flyby of five different ways that llms are used comm...\",\"Video: 59673449\\u003cbr\\u003eText: Well, I have to tell you that I'm a little bit sad.\\nThis is the beginning of the beginning of the en...\",\"Video: 59669389\\u003cbr\\u003eText: Well.\\nHi there.\\nSo you've made it to day two of week eight, and I am super grateful that you've been...\",\"Video: 59170057\\u003cbr\\u003eText: And so at the beginning of this week, we started by talking about hugging face pipelines.\\nAnd you us...\",\"Video: 59166949\\u003cbr\\u003eText: Welcome back to making chatbots.\\nLet's keep going.\\nSo for the next part we're going to beef up the s...\",\"Video: 59473019\\u003cbr\\u003eText: Welcome back to an action packed time of of training.\\nSo now, after waiting about five minutes when ...\",\"Video: 59297585\\u003cbr\\u003eText: Before we move on, let me show you one more time this fabulous slide that describes the simple three...\",\"Video: 59170255\\u003cbr\\u003eText: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...\",\"Video: 60614589\\u003cbr\\u003eText: So we're now going to run a large language model directly on your box using a platform called llama,...\",\"Video: 59297601\\u003cbr\\u003eText: I'm not going to lie, at this point you have every reason to be impatient with me.\\nWe've been yammer...\",\"Video: 60616629\\u003cbr\\u003eText: And welcome back to team PC and Team Mac as we come back together again for a quick video.\\nIn this o...\",\"Video: 59297749\\u003cbr\\u003eText: It's always welcome back to JupyterLab, my favorite place to be.\\nAnd now we are, of course in the we...\",\"Video: 59170135\\u003cbr\\u003eText: Welcome.\\nIt's week three.\\nIt's day four.\\nWe are back on the adventure in open source land, back inve...\",\"Video: 59472017\\u003cbr\\u003eText: And this is the first time that we'll be coding against our big project of the course.\\nWelcome to Ju...\",\"Video: 59507017\\u003cbr\\u003eText: Welcome to Colab.\\nWelcome to the week seven day two Colab.\\nAnd just before we try our base model, we...\",\"Video: 60619883\\u003cbr\\u003eText: And now we've arrived at an exciting moment in our first week.\\nThe conclusion of the first week is w...\",\"Video: 59508297\\u003cbr\\u003eText: What more is there to say, really?\\nTomorrow is the day for results.\\nA day that very excited indeed a...\",\"Video: 60619247\\u003cbr\\u003eText: We're going to spend a little bit more time with GPT just to try out a few more interesting things.\\n...\",\"Video: 59504769\\u003cbr\\u003eText: Without further ado, we're going to get stuck into it.\\nTalking about Laura.\\nLow rank adaptation.\\nAnd...\",\"Video: 59170233\\u003cbr\\u003eText: Welcome back to our continued exploits with Tokenizers.\\nWhat we're now going to look at is what's ca...\",\"Video: 59671231\\u003cbr\\u003eText: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...\",\"Video: 60620397\\u003cbr\\u003eText: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...\",\"Video: 59170093\\u003cbr\\u003eText: I'm delighted to see you again.\\nAs we get started with day three of week three of our adventure and ...\",\"Video: 59473089\\u003cbr\\u003eText: Welcome back.\\nSo hopefully you are still impressed by the GPT four mini results.\\nThe frontier model ...\",\"Video: 60395261\\u003cbr\\u003eText: Let's keep going with our project to equip our LM with a tool.\\nWe just created this piece of code to...\",\"Video: 60617259\\u003cbr\\u003eText: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...\",\"Video: 59507313\\u003cbr\\u003eText: And it's this time again, when we look at the podium of how our models are performing across the boa...\",\"Video: 60619721\\u003cbr\\u003eText: Now it's time to talk for a minute about tokens.\\nTokens are the individual units which get passed in...\",\"Video: 59295451\\u003cbr\\u003eText: I know that everybody.\\nIt seems like just the other day that we were embarking on our quest together...\",\"Video: 59166919\\u003cbr\\u003eText: And with that, it concludes our session on tools.\\nAnd at this point, you are probably an expert on t...\",\"Video: 59295441\\u003cbr\\u003eText: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\\nc...\",\"Video: 59295541\\u003cbr\\u003eText: And welcome back.\\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...\",\"Video: 59473101\\u003cbr\\u003eText: Welcome back.\\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\\nAnd how do ...\",\"Video: 59507423\\u003cbr\\u003eText: So you may remember eons ago when we were building our data set.\\nAt the end of that, we uploaded our...\",\"Video: 59295545\\u003cbr\\u003eText: I really hope you've enjoyed this week.\\nWe've got tons done.\\nWe've experimented with all sorts of ne...\",\"Video: 59472503\\u003cbr\\u003eText: Welcome back to Jupyter Lab.\\nLast time, we looked at some silly models for predicting the price of p...\",\"Video: 60614591\\u003cbr\\u003eText: The mantra of this course is that the best way to learn is by doing, and we will be doing stuff toge...\",\"Video: 59473021\\u003cbr\\u003eText: Welcome to our favorite place to be to JupyterLab.\\nHere we are again now in day three.\\nIn week six.\\n...\",\"Video: 60617255\\u003cbr\\u003eText: I'm now going to talk for a bit about models.\\nA term you often hear is the term frontier models, whi...\",\"Video: 59667829\\u003cbr\\u003eText: Well.\\nHello there.\\nLook, I know what you're thinking.\\nYou're thinking I peaked too early.\\nLast week ...\",\"Video: 59505329\\u003cbr\\u003eText: Welcome back.\\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...\",\"Video: 59669049\\u003cbr\\u003eText: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...\",\"Video: 60619439\\u003cbr\\u003eText: This now brings us to an extremely important property of LMS called the context window that I want t...\",\"Video: 59668181\\u003cbr\\u003eText: And so it gives me great pleasure to introduce to you the project that I've lined up for you this we...\",\"Video: 59472441\\u003cbr\\u003eText: Welcome back.\\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\\n...\",\"Video: 59507785\\u003cbr\\u003eText: Well, I'm sure you're fed up of me saying that the moment of truth has arrived, but it really has.\\nT...\",\"Video: 59295587\\u003cbr\\u003eText: When I left you, we had just created this simple user interface for converting from Python to C plus...\",\"Video: 59166465\\u003cbr\\u003eText: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\\nWe'd written two...\",\"Video: 59473071\\u003cbr\\u003eText: Hey, gang.\\nLook, I know what you're thinking.\\nThis week was supposed to be training week.\\nI set it a...\",\"Video: 59295423\\u003cbr\\u003eText: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...\",\"Video: 59297723\\u003cbr\\u003eText: So I know what you're thinking.\\nYou're thinking, what's going on here?\\nWe're on day five.\\nWe're on d...\",\"Video: 59166947\\u003cbr\\u003eText: Well, thank you for coming along for week two, day four.\\nWe have lots of good stuff in store today.\\n...\",\"Video: 59666831\\u003cbr\\u003eText: Take one more moment to look at this very nice diagram that lays it all out, and we will move on.\\nNo...\",\"Video: 59295493\\u003cbr\\u003eText: And welcome to week four, day three.\\nAs we are about to embark upon another business project which w...\",\"Video: 60616855\\u003cbr\\u003eText: Now I know what you're thinking.\\nWe've been building environments for so long.\\nAre we not done yet?\\n...\",\"Video: 59506611\\u003cbr\\u003eText: So in a future day, I'm going to be training, fine tuning a model and creating a fine tuned model.\\nA...\",\"Video: 60616493\\u003cbr\\u003eText: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...\",\"Video: 59166317\\u003cbr\\u003eText: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\\nUh, so today, ...\",\"Video: 59295439\\u003cbr\\u003eText: So I'm aware that there's a big risk that you are getting fed up of leaderboards, because we've done...\",\"Video: 59472421\\u003cbr\\u003eText: And welcome back to our final time in Jupyter Lab with traditional machine learning.\\nIt's almost ove...\",\"Video: 59472137\\u003cbr\\u003eText: Well, well, well, it's been a long day, but congratulations, you've made it.\\nWe've gone through and ...\",\"Video: 59297693\\u003cbr\\u003eText: So at the end of each week, it's customary for me to give you a challenge, an assignment to do on\\nyo...\",\"Video: 60620143\\u003cbr\\u003eText: So we're going to make a call to GPT four.\\nOh, that's going to ask it to look through a set of links...\",\"Video: 60619501\\u003cbr\\u003eText: I welcome to day four of our time together.\\nThis is a very important day.\\nToday we're going to be lo...\",\"Video: 59297743\\u003cbr\\u003eText: And welcome to day five.\\nFor reals.\\nWe're actually in the proper Jupyter notebook.\\nThis time we're i...\",\"Video: 59166847\\u003cbr\\u003eText: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\\nU...\",\"Video: 59170223\\u003cbr\\u003eText: Well.\\nFantastic.\\nIt's coming up to the end of the week, and that means it's coming up to a challenge...\",\"Video: 59170037\\u003cbr\\u003eText: So how does it feel to be 30% of the way down the journey to being a proficient LLM engineer?\\nTake a...\",\"Video: 59295609\\u003cbr\\u003eText: You must be feeling absolutely exhausted at this point.\\nAnd if you are, that is okay.\\nYou have done ...\",\"Video: 60619281\\u003cbr\\u003eText: Well, I'm delighted to welcome you to day three of our eight week journey together.\\nAnd today we're ...\",\"Video: 59472429\\u003cbr\\u003eText: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\\n...\",\"Video: 59167009\\u003cbr\\u003eText: Welcome back.\\nIt's time to make our full agent framework.\\nI'm super excited about this.\\nIt's pulling...\",\"Video: 59166481\\u003cbr\\u003eText: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\\nReady to go with weeks...\",\"Video: 59670933\\u003cbr\\u003eText: I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very sat...\",\"Video: 59670073\\u003cbr\\u003eText: Okay, it's time to complete the Rag workflow in our Jupyter Lab on day 2.3.\\nWe've got this function ...\",\"Video: 59673595\\u003cbr\\u003eText: That concludes a mammoth project.\\nThree weeks in the making.\\nIn the course of those three weeks, sta...\",\"Video: 59297603\\u003cbr\\u003eText: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\\nFinally,...\",\"Video: 60614541\\u003cbr\\u003eText: I am delighted to welcome you to the first day of our eight weeks together as you join me on this ad...\",\"Video: 59667357\\u003cbr\\u003eText: Let's now see our results side by side.\\nWe started our journey with a constant model that was at $1....\",\"Video: 59667841\\u003cbr\\u003eText: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\\nat t...\",\"Video: 59472007\\u003cbr\\u003eText: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...\",\"Video: 59507435\\u003cbr\\u003eText: So I'm now going to talk about five important hyperparameters for the training process.\\nAnd some of ...\",\"Video: 59509185\\u003cbr\\u003eText: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...\",\"Video: 59473159\\u003cbr\\u003eText: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\\nSo we are going to put our fr...\",\"Video: 60619447\\u003cbr\\u003eText: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...\",\"Video: 59166353\\u003cbr\\u003eText: Well, congratulations on leveling up yet again.\\nYou've got some real hard skills that you've added t...\",\"Video: 60619123\\u003cbr\\u003eText: So what we're now going to do is we're going to look at some models in practice and start to compare...\",\"Video: 59295363\\u003cbr\\u003eText: Well, another congratulations moment.\\nYou have 40% on the way to being an LM engineer at a high leve...\",\"Video: 60619289\\u003cbr\\u003eText: And now we'll go a bit faster through the other models.\\nWe'll start with Google's Gemini.\\nI have the...\",\"Video: 59472873\\u003cbr\\u003eText: So it's quite an adventure that we had at the frontier of what's capable with Llms today, solving a\\n...\",\"Video: 60619429\\u003cbr\\u003eText: Let me talk about some other phenomena that have happened over the last few years.\\nOne of them has b...\",\"Video: 59295601\\u003cbr\\u003eText: So it's time to continue our journey into the world of open source and understand which models we sh...\",\"Video: 59170025\\u003cbr\\u003eText: And a massive welcome back one more time to LM engineering.\\nWe are in week three, day two and we are...\",\"Video: 59166443\\u003cbr\\u003eText: And welcome back everybody.\\nWelcome to week two day three.\\nIt's a continuation of our enjoyment of r...\",\"Video: 60620025\\u003cbr\\u003eText: And welcome back to Jupyter Lab, one of my very favorite places to be.\\nWhen Jupyter Lab sprung up on...\",\"Video: 59170055\\u003cbr\\u003eText: Welcome to the world of Google Colab.\\nYou may already be very familiar with Google Colab, even if so...\"],\"x\":[1.7087736,-23.05743,-28.06106,53.49951,-25.6022,-38.486794,19.744888,40.88814,16.016825,-7.811325,-42.27272,12.64262,-0.9483807,6.6331143,30.734392,-11.433903,4.802981,-38.28026,-65.32751,-70.74078,17.332169,-52.152122,-59.27239,33.891144,31.012686,-3.4545732,-64.21017,7.6461124,20.45596,17.49392,58.194645,23.335354,-1.4417802,-31.790943,16.829693,9.377639,-35.48943,21.703556,-32.651184,34.197903,8.165246,-16.031826,-15.671898,24.559917,12.978084,-2.6771584,-40.945656,36.925316,25.997892,-6.9610124,-10.469476,-64.6909,1.2878436,11.471338,-9.606986,-36.224865,52.36488,18.624384,-75.35086,7.6970425,-28.021814,4.318845,21.450777,6.252253,8.603412,-42.26961,-4.845308,18.71574,26.781273,-0.79253143,-14.978188,18.245869,-12.82021,-46.9319,-74.53214,65.01824,-55.562347,-16.433084,-34.34535,37.855953,23.34828,58.13494,-62.33339,23.672892,3.081372,54.94866,26.811195,-17.7189,8.902483,43.160995,-9.250307,-15.630141,17.22394,-55.25729,-83.02552,30.956335,9.167712,44.13426,-11.525453,17.694338,-5.0039816,9.241007,-22.265665,1.0213552,-1.9952722,31.26171,-44.436382,8.186024,-38.107677,20.091564,-47.018497,3.9196463,-46.056137,29.834492,51.38648,9.7722,-39.721962,-11.258467,38.1706,25.899416,-22.391533,64.70646,8.984558,-8.005773,-22.550919,29.339518,38.68547,-58.67559,-38.17486,-21.293037,-59.715446,-32.520184,-55.803455,-9.595691,4.706987,1.6881931,-37.37762,26.374004,76.44756,1.4116235,-8.510549,-13.362774,-50.184566,-10.902527,-4.4753523,-6.0763307,-7.691084,0.769521,23.278786,37.985294,11.939553,39.230835,59.653934,43.122715,-8.87973,4.4371753,-48.39784,-10.907453,-26.853792,35.47057,6.833131,59.59064,19.819576,38.58615,-16.264578,-53.5018,10.727256,42.230526,17.628677,-24.103918,52.2294,-77.18268,3.6058846,19.204115,-4.63787,-4.450328,-2.8182654,-46.7583,17.780125,57.05202,-20.470179,-23.03723,-15.013553,-61.297047,53.65074,-63.843815,36.721672,1.2968292,-1.146437,30.53313,47.650024,-35.42971,13.790592,-29.44714,-7.0857954,-31.83992,4.395385,-71.52093,-38.636032,-10.17397,13.551749,26.199244,32.304344,37.940987,-19.058989,35.280716,28.176294,63.618996,50.98304,70.33112,-23.338556,55.944035,21.928713,-24.126383,20.637466,-27.234331,-34.206],\"y\":[-4.252548,-43.394333,-15.430764,3.913298,4.4845552,-33.13936,-22.152138,26.412823,30.148039,59.570904,48.391426,-12.528983,-30.37183,60.731644,27.484875,14.465288,-69.40243,0.97867054,-5.723522,2.7253983,7.151598,-40.23502,-24.437897,44.777378,2.2172062,-15.407989,-9.227094,-35.85389,19.15898,37.333565,-32.59874,12.68407,-73.58059,-6.1550703,54.37412,-45.11716,24.515192,-40.404133,7.894027,40.451534,-45.810196,32.84157,-29.400854,-36.06799,-26.307,-34.34112,16.958231,-4.426608,-51.174706,-10.152972,-61.325924,-40.93275,1.7414787,25.57757,-6.57861,23.91178,-12.384486,-16.934166,-12.787716,46.80957,-40.928993,-23.344496,57.658195,-52.681698,-29.147705,-26.069113,-37.631737,6.913289,1.0152185,-22.000841,40.077843,68.951485,-34.20306,-10.534028,-17.659843,-15.2762165,-31.812723,41.836502,44.55901,42.523884,21.308317,-16.114529,-24.19872,-28.394356,72.398735,-11.762284,39.155792,62.174786,0.33776075,-9.822582,7.8490186,38.088703,3.910647,-21.867012,-9.80895,34.229267,6.49524,-20.215645,-23.823503,13.815909,-10.719444,29.71537,27.11394,8.893772,-42.861084,11.520209,31.976051,-61.493744,-10.253941,20.047174,7.7775283,16.061588,-27.2339,9.338753,-28.769846,4.966599,66.91598,-58.99846,28.998318,35.759415,-14.775799,15.561535,23.844439,13.185903,50.08121,-53.279182,52.965717,-9.916494,30.322853,38.71735,-20.32845,33.65194,-45.906616,37.194542,-58.197693,68.45129,19.058973,30.657938,-11.88528,-3.2491598,67.76955,-1.6988521,19.30631,52.74911,-24.831163,-32.65019,-10.958649,0.82742673,-21.389397,14.101158,2.1391983,6.576113,-32.843567,60.250656,-43.65164,-10.533554,-25.26452,50.190014,-22.556831,-1.8844366,17.620245,25.576294,53.109592,-41.58676,8.641028,-33.405598,39.503387,-40.254204,-46.44093,13.338996,9.385,-31.900993,-10.131737,-5.4291334,9.878982,-47.344704,9.33157,51.674915,-31.377905,-4.2005377,-12.071655,-3.6661708,37.244083,3.2858796,-25.261751,-48.280323,11.631785,32.637978,45.047813,-23.116121,-6.183171,-23.86113,0.017425848,8.19719,22.400421,-40.894783,29.179394,-18.357765,43.00136,-4.4837027,41.68122,-36.107216,21.893982,34.812412,-32.88127,-17.11192,-10.238356,-1.9124643,21.319334,-2.981173,-1.3924571,-41.355488,-4.402796,45.275204,-19.099257,-28.038015,63.64564],\"z\":[-80.01053,6.952257,39.770596,16.702005,5.445383,-15.32626,-0.5249115,29.32656,36.423714,-14.892507,-8.28791,23.206917,57.858578,-50.514557,64.206955,18.315903,-0.5376158,-15.617648,-0.26207563,-29.67441,-71.75039,8.197639,-14.2429905,43.300938,38.940685,68.43502,-23.888317,48.1142,49.111935,-61.746227,50.52906,32.868515,-13.877641,7.967563,-50.249985,-49.565216,-2.7943447,-46.210426,-54.42825,-20.080788,15.767397,-16.436441,-31.49947,27.740046,31.81843,-10.746491,68.19509,-17.48625,-12.825234,-71.884796,-16.59812,12.649029,70.53656,19.578947,-38.99613,53.66051,-4.5801187,-19.734165,-33.125164,-28.618763,23.614397,77.43594,-32.899113,46.40308,8.167681,-2.8620894,74.148186,-26.347952,-74.90554,-77.46363,-28.790958,20.622337,64.1211,9.823497,-7.1779866,10.737146,-29.82501,-67.2842,-18.324295,4.6903768,-0.48786125,44.55313,-0.092902616,13.796377,27.559351,-36.15748,-36.40333,-38.448048,-5.13925,-45.601143,-32.268417,10.468017,16.537827,40.53047,-1.0723572,52.555515,53.437595,12.123496,38.82272,-48.952595,12.853546,-30.875723,1.1754398,18.969849,28.55506,22.07326,57.09251,-26.802582,-10.858276,-13.749718,36.731487,0.24828485,-43.257736,-55.938084,39.296425,-53.445366,-17.457575,-2.9673405,0.0901862,-49.77096,-25.705078,-13.610352,67.12242,35.858795,9.70463,20.889431,-43.310135,-42.36413,-33.766987,-57.633263,-35.28791,64.09733,-30.020395,-4.3398356,-11.234243,-16.331018,22.371712,17.535992,25.282942,-62.60095,0.4806009,70.375824,20.449402,-2.0014663,-32.865536,23.095472,27.847054,-30.648935,-56.0284,-7.3562527,41.76956,-24.99873,31.66762,-29.629705,-15.31758,-9.096767,26.896555,44.854942,7.493553,4.068373,34.71581,-46.640224,-8.490605,-48.098576,50.40989,35.75695,-43.580498,43.48538,43.563065,-47.500908,-17.975456,-4.8319664,43.81085,-46.34204,-4.954837,-51.740547,-68.37514,29.03883,56.43028,-23.674625,-9.0214,20.377613,37.052822,35.568115,-8.854601,-12.067132,49.907764,36.929363,16.495632,-27.568445,7.129844,-50.75493,69.71066,32.083324,56.811436,-21.762302,54.430317,8.924631,33.06066,-21.813917,-3.1104941,-21.70847,-63.661057,-45.242672,-2.3619707,-58.077934,52.1452,-1.9540714,-23.772692,14.296897,-31.250578,-1.1164938,-8.393525,-8.107033,41.62983,34.141304,-26.842785],\"type\":\"scatter3d\"}], {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"margin\":{\"r\":20,\"b\":10,\"l\":10,\"t\":40},\"title\":{\"text\":\"3D Chroma Vector Store Visualization\"},\"scene\":{\"xaxis\":{\"title\":{\"text\":\"x\"}},\"yaxis\":{\"title\":{\"text\":\"y\"}},\"zaxis\":{\"title\":{\"text\":\"z\"}}},\"width\":900,\"height\":700}, {\"responsive\": true} ).then(function(){\n", |
|
" \n", |
|
"var gd = document.getElementById('4b1616ca-306c-4be2-80b1-9433324cfbaf');\n", |
|
"var x = new MutationObserver(function (mutations, observer) {{\n", |
|
" var display = window.getComputedStyle(gd).display;\n", |
|
" if (!display || display === 'none') {{\n", |
|
" console.log([gd, 'removed!']);\n", |
|
" Plotly.purge(gd);\n", |
|
" observer.disconnect();\n", |
|
" }}\n", |
|
"}});\n", |
|
"\n", |
|
"// Listen for the removal of the full notebook cells\n", |
|
"var notebookContainer = gd.closest('#notebook-container');\n", |
|
"if (notebookContainer) {{\n", |
|
" x.observe(notebookContainer, {childList: true});\n", |
|
"}}\n", |
|
"\n", |
|
"// Listen for the clearing of the current output cell\n", |
|
"var outputEl = gd.closest('.output');\n", |
|
"if (outputEl) {{\n", |
|
" x.observe(outputEl, {childList: true});\n", |
|
"}}\n", |
|
"\n", |
|
" }) }; }); </script> </div>" |
|
] |
|
}, |
|
"metadata": {}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"# Let's try 3D!\n", |
|
"\n", |
|
"tsne = TSNE(n_components=3, random_state=42)\n", |
|
"reduced_vectors = tsne.fit_transform(vectors)\n", |
|
"\n", |
|
"# Create the 3D scatter plot\n", |
|
"fig = go.Figure(data=[go.Scatter3d(\n", |
|
" x=reduced_vectors[:, 0],\n", |
|
" y=reduced_vectors[:, 1],\n", |
|
" z=reduced_vectors[:, 2],\n", |
|
" mode='markers',\n", |
|
" marker=dict(size=5, color=colors, opacity=0.8),\n", |
|
" text=[f\"Video: {t}<br>Text: {d[:100]}...\" for t, d in zip(video_numbers, documents)],\n", |
|
" hoverinfo='text'\n", |
|
")])\n", |
|
"\n", |
|
"fig.update_layout(\n", |
|
" title='3D Chroma Vector Store Visualization',\n", |
|
" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", |
|
" width=900,\n", |
|
" height=700,\n", |
|
" margin=dict(r=20, b=10, l=10, t=40)\n", |
|
")\n", |
|
"\n", |
|
"fig.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "9b3ada26-b4b7-42fc-b943-933c14adf89b", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "1440654f-590f-4781-bd1c-abfc6ca6edf1", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [] |
|
} |
|
], |
|
"metadata": { |
|
"kernelspec": { |
|
"display_name": "Python 3 (ipykernel)", |
|
"language": "python", |
|
"name": "python3" |
|
}, |
|
"language_info": { |
|
"codemirror_mode": { |
|
"name": "ipython", |
|
"version": 3 |
|
}, |
|
"file_extension": ".py", |
|
"mimetype": "text/x-python", |
|
"name": "python", |
|
"nbconvert_exporter": "python", |
|
"pygments_lexer": "ipython3", |
|
"version": "3.11.11" |
|
} |
|
}, |
|
"nbformat": 4, |
|
"nbformat_minor": 5 |
|
}
|
|
|