diff --git a/week5/community-contributions/day3 - vectorizing_subtitles_from_llm_engineering.ipynb b/week5/community-contributions/day3 - vectorizing_subtitles_from_llm_engineering.ipynb deleted file mode 100644 index 5ce3eba..0000000 --- a/week5/community-contributions/day3 - vectorizing_subtitles_from_llm_engineering.ipynb +++ /dev/null @@ -1,4208 +0,0 @@ -{ - "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", - 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"#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
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
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
Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", - "Video: 60619275
Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", - "Video: 59472693
Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", - "Video: 59670121
Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", - "Video: 59295619
Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", - "Video: 60617163
Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", - "Video: 60616423
Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", - "Video: 59170227
Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", - "Video: 59169985
Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", - "Video: 60616927
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
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
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
Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", - "Video: 60616623
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
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
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
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
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
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
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
Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", - "Video: 59670369
Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", - "Video: 59166281
Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", - "Video: 59671567
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
Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", - "Video: 59166461
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
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
Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", - "Video: 59295579
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
Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", - "Video: 59472491
Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", - "Video: 59472425
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
Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", - "Video: 60619577
Text: And for the final piece of background information, I wanted to take another moment to talk about API...", - "Video: 59170291
Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", - "Video: 60619651
Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", - "Video: 59473191
Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", - "Video: 59170297
Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", - "Video: 59167015
Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", - "Video: 59170043
Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", - "Video: 59473147
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
Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", - "Video: 59166915
Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", - "Video: 59667365
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
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
Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", - "Video: 59471979
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
Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", - "Video: 59472505
Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", - "Video: 59669217
Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", - "Video: 59671221
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
Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", - "Video: 59473201
Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", - "Video: 60622463
Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", - "Video: 60619299
Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", - "Video: 59295607
Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", - "Video: 59297575
Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", - "Video: 59507687
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
Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", - "Video: 59673431
Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", - "Video: 59473137
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
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
Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", - "Video: 59669631
Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", - "Video: 59673663
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
Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", - "Video: 59504785
Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", - "Video: 59505337
Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", - "Video: 59271655
Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", - "Video: 59472883
Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", - "Video: 59673639
Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", - "Video: 59472463
Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", - "Video: 59297595
Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", - "Video: 60619149
Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", - "Video: 59297735
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
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
Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", - "Video: 59472067
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
Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", - "Video: 59295553
Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", - "Video: 59297773
Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", - "Video: 59295583
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
Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", - "Video: 59295429
Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", - "Video: 60595637
Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", - "Video: 59668027
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
Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", - "Video: 59295377
Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", - "Video: 59666211
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
Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", - "Video: 60616833
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
Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", - "Video: 59297561
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
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
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
Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", - "Video: 60620395
Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", - "Video: 59665127
Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", - "Video: 59668923
Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", - "Video: 59504887
Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", - "Video: 59170165
Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", - "Video: 60617251
Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", - "Video: 59166951
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
Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", - "Video: 60616663
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
Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", - "Video: 59670087
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
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
Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", - "Video: 59295435
Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", - "Video: 59297609
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
Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", - "Video: 59295549
Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", - "Video: 59665129
Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", - "Video: 59169991
Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", - "Video: 59472027
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
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
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
Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", - "Video: 59295431
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
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
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
Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", - "Video: 59166949
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
Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", - "Video: 59297585
Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", - "Video: 59170255
Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", - "Video: 60614589
Text: So we're now going to run a large language model directly on your box using a platform called llama,...", - "Video: 59297601
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
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
Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", - "Video: 59170135
Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", - "Video: 59472017
Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", - "Video: 59507017
Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", - "Video: 60619883
Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", - "Video: 59508297
Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", - "Video: 60619247
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
Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", - "Video: 59170233
Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", - "Video: 59671231
Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", - "Video: 60620397
Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", - "Video: 59170093
Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", - "Video: 59473089
Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", - "Video: 60395261
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
Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", - "Video: 59507313
Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", - "Video: 60619721
Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", - "Video: 59295451
Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", - "Video: 59166919
Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", - "Video: 59295441
Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", - "Video: 59295541
Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", - "Video: 59473101
Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", - "Video: 59507423
Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", - "Video: 59295545
Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", - "Video: 59472503
Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", - "Video: 60614591
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
Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", - "Video: 60617255
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
Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", - "Video: 59505329
Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", - "Video: 59669049
Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", - "Video: 60619439
Text: This now brings us to an extremely important property of LMS called the context window that I want t...", - "Video: 59668181
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
Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", - "Video: 59507785
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
Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", - "Video: 59166465
Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", - "Video: 59473071
Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", - "Video: 59295423
Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", - "Video: 59297723
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
Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", - "Video: 59666831
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
Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", - "Video: 60616855
Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", - "Video: 59506611
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
Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", - "Video: 59166317
Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", - "Video: 59295439
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
Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", - "Video: 59472137
Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", - "Video: 59297693
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
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
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
Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", - "Video: 59166847
Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", - "Video: 59170223
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
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
Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", - "Video: 60619281
Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", - "Video: 59472429
Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", - "Video: 59167009
Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", - "Video: 59166481
Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", - "Video: 59670933
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
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
Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", - "Video: 59297603
Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", - "Video: 60614541
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
Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", - "Video: 59667841
Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", - "Video: 59472007
Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", - "Video: 59507435
Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", - "Video: 59509185
Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", - "Video: 59473159
Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", - "Video: 60619447
Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", - "Video: 59166353
Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", - "Video: 60619123
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
Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", - "Video: 60619289
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
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
Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", - "Video: 59295601
Text: So it's time to continue our journey into the world of open source and understand which models we sh...", - "Video: 59170025
Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", - "Video: 59166443
Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", - "Video: 60620025
Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", - "Video: 59170055
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", 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" - ] - }, - "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}
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
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
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
Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", - "Video: 60619275
Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", - "Video: 59472693
Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", - "Video: 59670121
Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", - "Video: 59295619
Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", - "Video: 60617163
Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", - "Video: 60616423
Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", - "Video: 59170227
Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", - "Video: 59169985
Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", - "Video: 60616927
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
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
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
Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", - "Video: 60616623
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
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
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
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
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
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
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
Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", - "Video: 59670369
Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", - "Video: 59166281
Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", - "Video: 59671567
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
Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", - "Video: 59166461
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
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
Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", - "Video: 59295579
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
Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", - "Video: 59472491
Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", - "Video: 59472425
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
Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", - "Video: 60619577
Text: And for the final piece of background information, I wanted to take another moment to talk about API...", - "Video: 59170291
Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", - "Video: 60619651
Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", - "Video: 59473191
Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", - "Video: 59170297
Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", - "Video: 59167015
Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", - "Video: 59170043
Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", - "Video: 59473147
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
Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", - "Video: 59166915
Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", - "Video: 59667365
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
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
Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", - "Video: 59471979
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
Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", - "Video: 59472505
Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", - "Video: 59669217
Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", - "Video: 59671221
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
Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", - "Video: 59473201
Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", - "Video: 60622463
Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", - "Video: 60619299
Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", - "Video: 59295607
Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", - "Video: 59297575
Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", - "Video: 59507687
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
Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", - "Video: 59673431
Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", - "Video: 59473137
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
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
Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", - "Video: 59669631
Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", - "Video: 59673663
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
Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", - "Video: 59504785
Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", - "Video: 59505337
Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", - "Video: 59271655
Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", - "Video: 59472883
Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", - "Video: 59673639
Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", - "Video: 59472463
Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", - "Video: 59297595
Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", - "Video: 60619149
Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", - "Video: 59297735
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
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
Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", - "Video: 59472067
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
Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", - "Video: 59295553
Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", - "Video: 59297773
Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", - "Video: 59295583
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
Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", - "Video: 59295429
Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", - "Video: 60595637
Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", - "Video: 59668027
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
Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", - "Video: 59295377
Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", - "Video: 59666211
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
Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", - "Video: 60616833
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
Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", - "Video: 59297561
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
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
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
Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", - "Video: 60620395
Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", - "Video: 59665127
Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", - "Video: 59668923
Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", - "Video: 59504887
Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", - "Video: 59170165
Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", - "Video: 60617251
Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", - "Video: 59166951
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
Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", - "Video: 60616663
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
Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", - "Video: 59670087
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
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
Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", - "Video: 59295435
Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", - "Video: 59297609
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
Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", - "Video: 59295549
Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", - "Video: 59665129
Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", - "Video: 59169991
Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", - "Video: 59472027
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
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
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
Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", - "Video: 59295431
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
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
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
Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", - "Video: 59166949
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
Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", - "Video: 59297585
Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", - "Video: 59170255
Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", - "Video: 60614589
Text: So we're now going to run a large language model directly on your box using a platform called llama,...", - "Video: 59297601
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
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
Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", - "Video: 59170135
Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", - "Video: 59472017
Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", - "Video: 59507017
Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", - "Video: 60619883
Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", - "Video: 59508297
Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", - "Video: 60619247
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
Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", - "Video: 59170233
Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", - "Video: 59671231
Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", - "Video: 60620397
Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", - "Video: 59170093
Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", - "Video: 59473089
Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", - "Video: 60395261
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
Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", - "Video: 59507313
Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", - "Video: 60619721
Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", - "Video: 59295451
Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", - "Video: 59166919
Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", - "Video: 59295441
Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", - "Video: 59295541
Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", - "Video: 59473101
Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", - "Video: 59507423
Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", - "Video: 59295545
Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", - "Video: 59472503
Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", - "Video: 60614591
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
Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", - "Video: 60617255
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
Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", - "Video: 59505329
Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", - "Video: 59669049
Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", - "Video: 60619439
Text: This now brings us to an extremely important property of LMS called the context window that I want t...", - "Video: 59668181
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
Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", - "Video: 59507785
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
Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", - "Video: 59166465
Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", - "Video: 59473071
Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", - "Video: 59295423
Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", - "Video: 59297723
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
Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", - "Video: 59666831
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
Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", - "Video: 60616855
Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", - "Video: 59506611
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
Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", - "Video: 59166317
Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", - "Video: 59295439
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
Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", - "Video: 59472137
Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", - "Video: 59297693
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
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
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
Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", - "Video: 59166847
Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", - "Video: 59170223
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
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
Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", - "Video: 60619281
Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", - "Video: 59472429
Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", - "Video: 59167009
Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", - "Video: 59166481
Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", - "Video: 59670933
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
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
Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", - "Video: 59297603
Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", - "Video: 60614541
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
Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", - "Video: 59667841
Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", - "Video: 59472007
Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", - "Video: 59507435
Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", - "Video: 59509185
Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", - "Video: 59473159
Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", - "Video: 60619447
Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", - "Video: 59166353
Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", - "Video: 60619123
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
Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", - "Video: 60619289
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
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
Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", - "Video: 59295601
Text: So it's time to continue our journey into the world of open source and understand which models we sh...", - "Video: 59170025
Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", - "Video: 59166443
Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", - "Video: 60620025
Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", - "Video: 59170055
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, - 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", - "text/html": [ - "
" - ] - }, - "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}
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 -} diff --git a/week5/community-contributions/day4 - chat with Eds subtitles on LLM engineering.ipynb b/week5/community-contributions/day4 - chat with Eds subtitles on LLM engineering.ipynb new file mode 100644 index 0000000..46622e0 --- /dev/null +++ b/week5/community-contributions/day4 - chat with Eds subtitles on LLM engineering.ipynb @@ -0,0 +1,4323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", + "metadata": {}, + "source": [ + "## Chat with Ed - the Expert on LLM engineering\n", + "This project will: \n", + "- use subtitle files from Ed Donners excellent LLM engineering course on Udemy.\n", + "- use Document loading using Langchain\n", + "- use Vectorization, embeddings and store vectors in a Chroma DB\n", + "- use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy.\n", + "\n", + "These subtitles can be downloaded using the following process:\n", + "- Using 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.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "802137aa-8a74-45e0-a487-d1974927d7ca", + "metadata": {}, + "outputs": [], + "source": [ + "# imports for langchain\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", + "from langchain.memory import ConversationBufferMemory\n", + "from langchain.chains import ConversationalRetrievalChain" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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": 4, + "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": 5, + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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": 7, + "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "217" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(chunks)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Video numbers found: 59507785, 59472503, 59170107, 60616493, 59504887, 59297735, 59472429, 59170291, 60595637, 59473019, 59472441, 59295423, 59170043, 59472067, 59295363, 59472425, 59297723, 59473137, 59473159, 59669375, 59472011, 59295431, 59673721, 59473101, 59167015, 59670087, 60619429, 59667365, 59673639, 59169985, 59507489, 60620143, 59505329, 59670369, 59295549, 60395261, 59668181, 59671231, 60619281, 59506713, 59472491, 59295579, 59167007, 59167009, 59666211, 59673431, 59671567, 59170055, 59472017, 59473021, 59297599, 59472027, 59166947, 59473201, 60619123, 59472873, 59295601, 60614591, 60614541, 59472007, 59507313, 60619721, 59297595, 59472693, 59295527, 60619501, 59166981, 59166421, 59507423, 59170165, 59166951, 59170227, 59673663, 59670121, 59166453, 60616845, 59471979, 59670171, 59503705, 59668923, 60617163, 60616629, 59297693, 59166915, 60617259, 59166847, 59295459, 60619439, 59297593, 59295619, 59472883, 59295439, 59670933, 60619651, 59670073, 59166465, 59295429, 59669631, 59170233, 59472333, 59507635, 60619227, 59667829, 59166353, 60614589, 59295599, 59507687, 59671441, 59170057, 59670259, 59170235, 59472307, 59472421, 59667841, 59667357, 59166949, 59170297, 59504785, 59170093, 59166443, 59673595, 59669211, 60620025, 59297773, 60619883, 60616423, 59295493, 59166461, 60616855, 59297601, 59295435, 59673449, 59503703, 59472505, 59295377, 59166281, 59507435, 59297575, 59504769, 59170037, 60622463, 59508289, 60616663, 60616895, 60620375, 60619247, 59665129, 59170135, 59297743, 59169991, 59506929, 60616407, 59508297, 59297603, 60616927, 60617255, 59295441, 59668027, 59297609, 60620169, 59472383, 59297585, 60616623, 60617251, 59666831, 59295553, 59473191, 59473089, 59669217, 59508175, 60616833, 59297749, 59295609, 59295545, 59669389, 59170025, 60619619, 60620397, 59166481, 59295541, 59297561, 59166919, 59507329, 59506611, 59170223, 60619447, 59166317, 59473071, 60619299, 59507017, 59509185, 59170255, 60619577, 59671221, 60619289, 59508121, 59295583, 60619149, 59665127, 59473147, 59295451, 59271655, 59472137, 59295607, 59669049, 59295587, 59472463, 59506507, 59472413, 59297721, 59508057, 59508055, 59671315, 59297733, 60619275, 60620395, 59505337\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": 9, + "id": "78998399-ac17-4e28-b15f-0b5f51e6ee23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vectorstore created with 217 documents\n" + ] + } + ], + "source": [ + "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", + "# Chroma is a popular open source Vector Database based on SQLLite\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\")\n", + "\n", + "# Delete if already exists\n", + "\n", + "if os.path.exists(db_name):\n", + " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", + "\n", + "# Create 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": 10, + "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": "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": 11, + "id": "bf021654-a60b-4905-bdb5-d4517bd0c297", + "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]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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(video_numbers)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "text", + "marker": { + "color": [ + "#f8d349", + "#d6d07a", + "#a958c9", + "#7341ee", + "#268bba", + "#4862ce", + "#dd8cd7", + "#6a6c06", + "#8a29da", + "#0d2037", + "#805527", + 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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
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
Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", + "Video: 60619275
Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", + "Video: 59472693
Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", + "Video: 59670121
Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", + "Video: 59295619
Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", + "Video: 60617163
Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", + "Video: 60616423
Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", + "Video: 59170227
Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", + "Video: 59169985
Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", + "Video: 60616927
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
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
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
Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", + "Video: 60616623
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
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
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
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
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
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
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
Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", + "Video: 59670369
Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", + "Video: 59166281
Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", + "Video: 59671567
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
Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", + "Video: 59166461
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
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
Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", + "Video: 59295579
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
Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", + "Video: 59472491
Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", + "Video: 59472425
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
Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", + "Video: 60619577
Text: And for the final piece of background information, I wanted to take another moment to talk about API...", + "Video: 59170291
Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", + "Video: 60619651
Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", + "Video: 59473191
Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", + "Video: 59170297
Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", + "Video: 59167015
Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", + "Video: 59170043
Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", + "Video: 59473147
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
Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", + "Video: 59166915
Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", + "Video: 59667365
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
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
Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", + "Video: 59471979
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
Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", + "Video: 59472505
Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", + "Video: 59669217
Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", + "Video: 59671221
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
Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", + "Video: 59473201
Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", + "Video: 60622463
Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", + "Video: 60619299
Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", + "Video: 59295607
Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", + "Video: 59297575
Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", + "Video: 59507687
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
Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", + "Video: 59673431
Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", + "Video: 59473137
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
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
Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", + "Video: 59669631
Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", + "Video: 59673663
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
Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", + "Video: 59504785
Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", + "Video: 59505337
Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", + "Video: 59271655
Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", + "Video: 59472883
Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", + "Video: 59673639
Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", + "Video: 59472463
Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", + "Video: 59297595
Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", + "Video: 60619149
Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", + "Video: 59297735
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
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
Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", + "Video: 59472067
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
Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", + "Video: 59295553
Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", + "Video: 59297773
Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", + "Video: 59295583
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
Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", + "Video: 59295429
Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", + "Video: 60595637
Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", + "Video: 59668027
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
Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", + "Video: 59295377
Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", + "Video: 59666211
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
Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", + "Video: 60616833
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
Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", + "Video: 59297561
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
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
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
Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", + "Video: 60620395
Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", + "Video: 59665127
Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", + "Video: 59668923
Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", + "Video: 59504887
Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", + "Video: 59170165
Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", + "Video: 60617251
Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", + "Video: 59166951
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
Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", + "Video: 60616663
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
Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", + "Video: 59670087
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
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
Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", + "Video: 59295435
Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", + "Video: 59297609
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
Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", + "Video: 59295549
Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", + "Video: 59665129
Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", + "Video: 59169991
Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", + "Video: 59472027
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
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
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
Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", + "Video: 59295431
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
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
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
Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", + "Video: 59166949
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
Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", + "Video: 59297585
Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", + "Video: 59170255
Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", + "Video: 60614589
Text: So we're now going to run a large language model directly on your box using a platform called llama,...", + "Video: 59297601
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
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
Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", + "Video: 59170135
Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", + "Video: 59472017
Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", + "Video: 59507017
Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", + "Video: 60619883
Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", + "Video: 59508297
Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", + "Video: 60619247
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
Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", + "Video: 59170233
Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", + "Video: 59671231
Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", + "Video: 60620397
Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", + "Video: 59170093
Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", + "Video: 59473089
Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", + "Video: 60395261
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
Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", + "Video: 59507313
Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", + "Video: 60619721
Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", + "Video: 59295451
Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", + "Video: 59166919
Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", + "Video: 59295441
Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", + "Video: 59295541
Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", + "Video: 59473101
Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", + "Video: 59507423
Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", + "Video: 59295545
Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", + "Video: 59472503
Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", + "Video: 60614591
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
Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", + "Video: 60617255
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
Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", + "Video: 59505329
Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", + "Video: 59669049
Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", + "Video: 60619439
Text: This now brings us to an extremely important property of LMS called the context window that I want t...", + "Video: 59668181
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
Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", + "Video: 59507785
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
Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", + "Video: 59166465
Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", + "Video: 59473071
Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", + "Video: 59295423
Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", + "Video: 59297723
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
Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", + "Video: 59666831
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
Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", + "Video: 60616855
Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", + "Video: 59506611
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
Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", + "Video: 59166317
Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", + "Video: 59295439
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
Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", + "Video: 59472137
Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", + "Video: 59297693
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
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
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
Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", + "Video: 59166847
Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", + "Video: 59170223
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
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
Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", + "Video: 60619281
Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", + "Video: 59472429
Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", + "Video: 59167009
Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", + "Video: 59166481
Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", + "Video: 59670933
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
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
Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", + "Video: 59297603
Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", + "Video: 60614541
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
Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", + "Video: 59667841
Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", + "Video: 59472007
Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", + "Video: 59507435
Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", + "Video: 59509185
Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", + "Video: 59473159
Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", + "Video: 60619447
Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", + "Video: 59166353
Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", + "Video: 60619123
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
Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", + "Video: 60619289
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
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
Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", + "Video: 59295601
Text: So it's time to continue our journey into the world of open source and understand which models we sh...", + "Video: 59170025
Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", + "Video: 59166443
Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", + "Video: 60620025
Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", + "Video: 59170055
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/tHhSR0DVccR3HMHR1Tm+qKTL+jse5YozdWy45aeO0vbs3tWVIepI7e3/fnaFD1S0FnjZslH1fOmtj+SD527NqlZtNVJC+JQLbpWfJs+QNx5btqOttcBL3+P2ySv/at5lqcZSu/TUTkV1V9vRzj18beWTnos6rhtrJRkbG5tcAapEb3ufarOd85iF2oWmZJaSj+ohlfRR6XS/1nfgqXvd1L176oVdVUu1E3P0Tpu5x7XVLrxsBF62aygbgTfJ+TmhpKI6Aqte/G35qaO9jzq7Y9WxbjXPysoK907Iw076Z9YCT+2gVOtRPbzS8h5IFUftulXHv9XuuvQOvJYiWrVB4Gn/FKIjBCAAAQhAAAIQgAAEIAABCPxKAIHngFB3eB3jCC91Z9UFpx7R5v1lKxJ46tikujdMPaCgjsi29TU4xx23cYSMutRfPXTR1q4uddTvKeeOOHV8Vd0tpivwvv5hkhx07EXS3g68jddf0xU7bcm/bAReWoq9/NBVGY9/pO+j62gHnuJz4J8vdOZYLvded6Z7/9imG67lHstMf5vsfqx7NFTdSdb6U/e7qXsBH3aOrF52439cYTigX682ubcWeNmyyUXgKSGkdgC29XjHG2M+FXW0WD1wscXG6ywn8NL5tbcD7wLnXrzfOzvd2spH7fD7/bEXursit3HuYGv9qZ2kLR+RaAvQ1bc9Io89/7a89/TNcuG198nnjsh+1XmROX1MVPVpLfBajqOOqL7s7G5Tr+vusOVIufLcY52HU5bdc9j6EQslzdRjJOoRi2zXUEcCT90reJBzX6Wa578cxq3vHlQ763Z07lFUD0ukP7VDUh2JzWUHnhpn9E5t78Ab9/l3rlBG4PHvVQhAAAIQgAAEIAABCEAAAhDwi0DkBZ563GH0YWfJLs6LlS3vZGsNvD2Bp44UnnvlPfLca+/LPdeeIZttNKLdWqkXUdUdXSf9aT/nwv+9M9qp46fHn3WdTJw0XV749xXu5fq6Ak8JRXU3mtqR1PIOPLXraVfniKS60P8IRxLqCrz0wwnjXrzdFY3qU8dg1c7APr2cI7TObrOOPnVn4CU3/Fuuv+hEV3A9eMs57kMc6e+Y065xH3to/aqviqNePS117sVL3/XWUsSo/mo8dRebOj6aFkUfv3SHuzssWzbZCryxH4+XY8+4tvmoa+t533zvU3LHv59rPlqtduCp10rT9wR++tUEOfyv/3TvsNt1u02bu6cfX1Dt1lpj5TYFnpLCWzqPJuy3x9Zy9kmZO8/UUetVVxqQIeLaqonavaZ2Zaodl+qY5zF/2NO957Dl11LgKVn5uXNsuvXRbPUS8reOUFSvC6udeadccIt7n2F6V6i6r297Z7efenxECbxs19CKBJ66i/GEs6+TSVNny2N3XijdunbOyFsdCd5w56PlkN/t1Hyfo2qgfr4qvq0F3u/32t79uaG+1nfgqfsC5zm/Vqgj8ukv/RDHsFWHuPfpIfA6+lnPj0MAAhCAAAQgAAEIQAACEICALoHICzz1h3klstSdcOnHEtIw1f1s6661qvs/lcRQsuqME9IPOyTchxyecHYbfTthsrsL6siDdlthHdTRUHWk8q2xn8nmG68tu28/Srp3rZQpM2bLI8+85byoWS23XHZy8yMXugJPJaGOpd7735fkLEfsbDNqPZnjSKPLb3rIvT/uWUdCdHNeZNUVeOmHF5ToUXfW/Thphlx568OurHntvU/kmXsvlUGOQFP3v7X3VdfUyXb7n+wev+zmMGgpRlSf9Cu0+++xrfuyq2qnZNnVtz8ipx77e/e1W/WpI5/qGOe5Jx/uvhSrjr2q47jpRz7UK6FKAilBpUTY6qsMyopNtgJPSaKTzrlJ3hv3pagXSrdyHhZRNVUPcowZ95V7fHNXRw5fe8GyRybUS73qddt/OUeYezlHatVxZrUzbcbsua5AXt1h+PX3P7uvuqp81VFn9bWXjxJh6hjx352jtuq4tNrh9vjz77j3vqmXftdec2iHvzYogaju3FMC6k1Hvra+T7ClwEtLUyWA93EeSVFSVEnn8x3Gu22/qfMa8GGiRLG60/CQfZeJMyVN1fp498Mv3Z2SSuBlu4Yudo6ojv34K3n7iRuafx6qOxzVbk216+9+56Xl6y880Xl9OfPeSbVe1JFltdNuhpPPLf/8m3s8+8EnX3fvanzixXfdo+zqaK06Lr/jgX93c1PCWz3MMnbc+IxXaNXrx0oqqz5qh2yTw/ke5+eXevX4gRvVHYJrIPA6XGk0gAAEIAABCEAAAhCAAAQgAAFdApEXeGpX0Jx5i9rkp45pvvifK5rFgbq4P/2pI4Zql5y6/F/9gT796mVHhVDCR71I+9TL7zniaaqonUlqR9nmzvHKPzl3bKmdY+kvH4Gn4tz36MuOzHnXvfNLiRa1O1DdqTZkYF83hK7AU33Vy5rq4Y4ljnQc4Tx6cOaJ/yedHOF5zOnXiDrWqCRNW495tOSjjlmq45Zq91hayLX8cXUn2633P+MKUnXkWO3eUg8nqCOl6U89RnCd8wjEm2M/FSUFV3F2nandjTs7L9WqT0nRY52cFOv1115N1GMf2bDJVuCpGErMqnvfVF2V1F20pMq5z67MuSNwoIx2Hj74/d7bNR/t/Orbn1yJq3YXHnnQ7u4dgoqXklFvvf+5K4GUeNpl202cV3/3dx9UUd+K8lGv5aoXcN1XXZ06D19tJTnWYZDtYynpF5J33Hqk3HTJX5dbwq2P0Ko873bqP9ERt2ru6viqkpQnHLFP8yurSpyqnYcLnFeUlRD80yF7OA+e/CQTncdP1KMp2a6h+x97tV2Bt6ezc3bytNlt/pRTr/+q47zqxy+45j5Hik5yBbx6mfkk5zXlK295WB53araHc0+gkoFqR6h6rTiZTLoyXwlYJVtfe+QaV7KqT4nXOxxZ+oMzB3WMW637v/xxX9ls5LJdt+zA6+hXP34cAhCAAAQgAAEIQAACEIAABHQJRF7g6YKjHwQgAAEIQAACEIAABCAAAQhAAAIQgAAECkEAgVcIysSAAAQgAAEIQAACEIAABCAAAQhAAAIQgIAmAQSeJji6QQACEIAABCAAAQhAAAIQgAAEIAABCECgEAQQeIWgTAwIQAACEIAABCAAAQhAAAIQgAAEIAABCGgSQOBpgqMbBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQKQQCBVwjKxIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgCYBBJ4mOLpBAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAQBBB4haBMDAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIaBJA4GmCoxsEIAABCEAAAhCAAAQgAAEIQAACEIAABApBAIFXCMrEgAAEIAABCEAAAhCAAAQgAAEIQAACEICAJgEEniY4ukEAAhCAAAQgAAEIQAACEIAABCAAAQhAoBAEEHiFoEwMCEAAAhCAAAQgAAEIQAACEIAABCAAAQhoEkDgaYKjGwQgAAEIQAACEIAABCAAAQhAAAIQgAAECkEAgVcIysSAAAQgAAEIQAACEIAABCAAAQhAAAIQgIAmAQSeJji6QQACEIAABCAAAQhAAAIQgAAEIAABCECgEAQQeIWgTAwIQAACEIAABCAAAQhAAAIQgAAEIAABCGgSQOBpgqMbBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQKQQCBVwjKxIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgCYBBJ4mOLpBAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAQBBB4haBMDAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIaBIwLvBmzq/VTD263Xp2KZWa+oTUNSSiC4GZB4ZAaXFcunQqlvlLGgKTM4lGm0DvbmWyuLpRGpuS0QbB7ANBoG/3cikuismcRXXSlEgFImeSjDaBfs6anbukTpL8EhvthRCQ2XcuL3Z/jVW/L+CDQBAIDOxVITgWeyql6uHlh8DzkmaBxkLgFQg0YTwhgMDzBCODFJAAAq+AsAmVNwEEXt4IGaDABBB4BQZOuLwIIPDywkdnAwQQeAagryAkAs+uehjJBoFnBDtBNQkg8DTB0c0YAQSeMfQE1iCAwNOARhejBBB4RvETPEcCCLwcgdHcOAEEnvESZCSAwLOrHkayQeAZwU5QTQIIPE1wdDNGAIFnDD2BNQgg8DSg0cUoAQSeUfwEz5EAAi9HYDQ3TgCBZ7wECDy7SmA+GwSe+RqQQfYEEHjZs6KlHQQQeHbUgSyyI4DAy44TrewhgMCzpxZk0jEBBF7HjGhhFwEEnn318DIj7sDzkmaBxkLgFQg0YTwhgMDzBCODFJAAAq+AsAmVNwEEXt4IGaDABBB4BQZOuLwIIPDywkdnAwQQeAagryAkR2jtqoeRbBB4RrATVJMAAk8THN2MEUDgGUNPYA0CCDwNaHQxSgCBZxQ/wXMkgMDLERjNjRNA4BkvQUYCCDy76mEkGwSeEewE1SSAwNMERzdjBBB4xtATWIMAAk8DGl2MEkDgGcVP8BwJIPByBEZz4wQQeMZLgMCzqwTms0Hgma8BGWRPAIGXPSta2kEAgWdHHcgiOwIIvOw40coeAgg8e2pBJh0TQOB1zIgWdhFA4NlXDy8z4g48L2kWaCwEXoFAE8YTAgg8TzAySAEJIPAKCJtQeRNA4OWNkAEKTACBV2DghMuLAAIvL3x0NkAAgWcA+gpCcoTWrnoYyQaBZwQ7QTUJIPA0wdHNGAEEnjH0BNYggMDTgEYXowQQeEbxEzxHAgi8HIHR3DgBBJ7xEmQkgMCzqx5GskHgGcFOUE0CCDxNcHQzRgCBZww9gTUIIPA0oNHFKAEEnlH8BM+RAAIvR2A0N04AgWe8BAg8u0pgPhsEnvkakEH2BBB42bOipR0EEHh21IEssiOAwMuOE63sIYDAs6cWZNIxAQRex4xoYRcBBJ599fAyI+7A85JmgcZC4BUINGE8IYDA8wQjgxSQAAKvgLAJlTcBBF7eCBmgwAQQeAUGTri8CCDw8sJHZwMEEHgGoK8gJEdo7aqHkWwQeEawE1STAAJPExzdjBFA4BlDT2ANAgg8DWh0MUoAgWcUP8FzJIDAyxEYzY0TQOAZL0FGAgg8u+phJBsEnhHsBNUkgMDTBEc3YwQQeMbQE1iDAAJPAxpdjBJA4BnFT/AcCSDwcgRGc+MEEHjGS4DAs6sE5rNB4JmvARlkTwCBlz0rWtpBAIFnRx3IIjsCCLzsONHKHgIIPHtqQSYdE0DgdcyIFnYRQODZVw8vM+IOPC9pFmgsBF6BQBPGEwIIPE8wMkgBCSDwCgibUHkTQODljZABCkwAgVdg4ITLiwACLy98dDZAAIFnAPoKQnKE1q56GMkGgWcEO0E1CSDwNMHRzRgBBJ4x9ATWIIDA04BGF6MEEHhG8RM8RwIIvByB0dw4AQSe8RJkJIDAs6seRrJB4BnBTlBNAgg8TXB0M0YAgWcMPYE1CCDwNKDRxSgBBJ5R/ATPkQACL0dgNDdOAIFnvAQIPLtKYD4bBJ75GpBB9gQQeNmzoqUdBBB4dtSBLLIjgMDLjhOt7CGAwLOnFmTSMQEEXseMaGEXAQSeffXwMiPuwPOSZoHGQuAVCDRhPCGAwPMEI4MUkAACr4CwCZU3AQRe3ggZoMAEEHgFBk64vAgg8PLCR2cDBBB4BqCvICRHaO2qh5FsEHhGsBNUkwACTxMc3YwRQOAZQ09gDQIIPA1odDFKAIFnFD/BcySAwMsRGM2NE0DgGS9BRgIIPLvqYSQbBJ4R7ATVJIDA0wRHN2MEEHjG0BNYgwACTwMaXYwSQOAZxU/wHAkg8HIERnPjBBB4xkuAwLOrBOazQeCZrwEZZE8AgZc9K1raQQCBZ0cdyCI7Agi87DjRyh4CCDx7akEmHRNA4HXMiBZ2EUDg2VcPLzPiDjwvaRZoLARegUATxhMCCDxPMDJIAQkg8AoIm1B5E0Dg5WE4Q4IAACAASURBVI2QAQpMAIFXYOCEy4sAAi8vfHQ2QACBZwD6CkJyhNauehjJBoFnBDtBNQkg8DTB0c0YAQSeMfQE1iCAwNOARhejBBB4RvETPEcCCLwcgdHcOAEEnvESZCSAwLOrHkayQeAZwU5QTQIIPE1wdDNGAIFnDD2BNQgg8DSg0SWDQFNTk7w95i355odvZNhqw2Sn7XaR0pIS3ygh8HxDy8A+EEDg+QCVIX0lgMDzFW/OgyPwckYWvg4IvPDVNMwzQuCFubrhnBsCL5x1DeusEHhhrWzh5nX/f+9zBV7622LUVnLsEcf6lgACzze0DOwDAQSeD1AZ0lcCCDxf8eY8uNUCb8GipXLWZXfK7LkL5bn7L2ue3MHHXyzfT5wiEou5/6xrZSd57+mb3P8+c35tzhCi3gGBF/UVEKz5I/CCVS+yFUHgsQqCRACBF6Rq2ZnrX886URYvWdycXGlpqdx53V0Sj8d9SRiB5wtWBvWJAALPJ7AM6xsBBJ5vaLUGtlbgVdfUySGOqNt28w3k3Y++zBB4ex52ltx48Umy+iqDlps0Ai/3dYDAy50ZPcwRQOCZY09kPQIIPD1u9DJDAIFnhnuYop5/+XkyZdrk5in17dNPrr7oGt+miMDzDS0D+0AAgecDVIb0lQACz1e8OQ9urcCrqa2TeQsWu/+58NoHMgTetvudLI/eeYH079MTgZdzyZfvgMDzACJDFIwAAq9gqAnkEQEEnkcgGaYgBBB4BcEc6iA/TPxebr33Vlm8eJFUVnaR4//0F1ln+Nq+zRmB5xtaBvaBAALPB6gM6SsBBJ6veHMe3FqBl57JZ+MnLCfwNtzlGNlm1Hry+dcTpXfPbvK3Yw6QbTZb3+3CDryc14Ag8HJnRg9zBBB45tgTWY8AAk+PG73MEEDgmeEetqiNTY0yc9YM6d9voJQ5R2j9/BB4ftJlbK8JIPC8Jsp4fhNA4PlNOLfxAyfwksmUnHfVPbLb9qNk841HyLsffCln/fNOef7fl7s78hZVNeZGgNbSuaJIGhpT0tiUhAYErCdQXBST8tK4VNUmrM+VBCGgCHTpVCw19QlJJFIAgYD1BLo66zUej8mSmiZRv+fig4DtBLp2KpGldY2S4rextpeK/BwCZSVx99fYWuf3BXwQCAKB7pUlOBaLCqXq4eUXSzmflwO2tQOv9fh/POUK2X/PbWX0Tps7f0hq8jJ8JMYqKy6SpmRSEvxGPRL1Dvok487jNSXFcalv5Dc+Qa9lVPIvKyly/4Ik6e2/HqOCj3kWmEBFabH7RlhtQ5PYsGS/SU6XdxLfS5NjZ0YVrSqbFa1eYCKEs51ARWmR1DUkxNM/gNg+afILLIHiIkfgOb/GNrBxIrA1jFrincrUX0TjWGypu6qHl5/vAq+mtl4mTpou649YrTnvQ0+8TA47YBfZdbtNOEKrUU2O0GpAo4sxAhyhNYaewJoEOEKrCY5uRgjYdIR2riyRh4vGZHDYK7GxrCr9jLAhqJ0EOEJrZ13Iqm0CHKFlZQSNAEdo7apY4I7QLlpcJTsffJrceMmJssXG68iYcV/J6ZfcIS/+5wrp1aMrAk9jfSHwNKDRxRgBBJ4x9ATWJIDA0wRHtzYJJKpqJVHbKEVdO0mRx38LqwLaJPA+i/0sY+LfZXDYKLmabJUazuqAQDMBBB6LIUgEEHhBqha5KgIIPLvWgbUC740xn8ppF98u6vxGY1NCSkqKZZUh/eXpey+Vdz/8Uq65/RGZM3+RDOrfW874yyGy2cgRLlkesch9gSHwcmdGD3MEEHjm2BNZjwACT48bvZYnUDt9njTNr1r2A84ZrIqV+0px1wpPUdkk8ObKYmcH3tiM+e2Z3EhWT/X3dM4MFmwCCLxg1y9q2SPwolbx4M8XgWdXDa0VeLqYEHi5k0Pg5c6MHuYIIPDMsSeyHgEEnh43emUSSDY2SfW30zP+YVGXCum0qrfHSW0SeGqyP8RmyMfxH6VBErJhaqiMTK7K0oBABgEEHgsiSAQQeEGqFrkqAgg8u9YBAs+uehjJBoFnBDtBNQkg8DTB0c0YAQSeMfShCpxyHu6p+nZa5AReqIrIZHwhgMDzBSuD+kQAgecTWIb1jQACzze0WgMj8LSwhasTAi9c9Qz7bBB4Ya9w+OaHwAtfTU3NqH7mAmmYu2RZ+AgcoTXFmbjBIoDAC1a9op4tAi/qKyB480fg2VUzBJ5d9TCSDQLPCHaCahJA4GmCo5sxAgg8Y+hDGbipqk6StQ2ReMQilAVkUp4TQOB5jpQBfSSAwPMRLkP7QgCB5wtW7UEReNrowtMRgReeWkZhJgi8KFQ5XHNE4IWrnmGfjW134IWdN/PLnwACL3+GjFA4Agi8wrEmkjcEEHjecPRqFASeVyQDPA4CL8DFi2DqCLwIFj3gU0bgBbyAEUsfgeddwZuSCXn528/ki5mTZUT/wbLPOptKcbzIuwAGRnpy9hh5ed7/pLKoTA4esINs1n0tA1lkhkTgGS8BCeRAAIGXAyyaWkEAgWdFGZqTQODZVQ8j2SDwjGAnqCYBBJ4mOLoZI4DAM4aewBoEEHga0NrpcvOYl+TV7z9v/tHt11hXTt1ub+8CFHik9xd+LTdMfioj6g1rnSCDynt7nslnUxrkgx/rpUfnuOy5XoV07xRvNwYCz3P8DOgjAQSej3AZ2hcCCDxfsGoPisDTRheejgi88NQyCjNB4EWhyuGaIwIvXPUM+2wQeN5V+NCHbpRFNVXNA5YWl8gTR57mvD/SvozyLrr3I/1r2ovy+rxPMwY+esiesmvvjTwN9tFP9XLb279x69u1SC7fv5uUFMXajIPA8xQ/g/lMAIHnM2CG95wAAs9zpHkNiMDLC184OiPwwlHHqMwCgReVSodnngi88NQyCjNB4HlX5eOfuFOmLZzXPOCArj3kroNO8C5AgUdqvQOvyBGR1w4/zvMdeLe+VSXjfq7PmN15e3WVNfqVIPAKXHPCeU8Agec9U0b0lwACz1++uY6OwMuVWAjbI/BCWNQQTwmBF+LihnRqCLyQFjak00LgeVfYr2dNkSvfelYW1iyVLuWd5OwdfyfrDRzqXQADI7W8A+8g5w68zX24A++hj6rl1a/rmmcXczbeXXdQd+lV2fb9gezAM7AQCKlNAIGnjY6Ohggg8AyBbycsAs+uehjJBoFnBDtBNQkg8DTB0c0YAQSeMfQE1iCAwNOAtoIujYmETF04VwZ37yVlzhFavo4JLK5JyrWvLZXJ85qkKB6T/TeukNHOPXjtfQi8jpnSwh4CCDx7akEm2RFA4GXHqVCtEHiFIm1xHASexcUhteUIIPBYFEEjgMALWsWinS8CL9r1t2X2qZTI9IUJ5/GKmLN7ccV3BiLwbKkaeWRDAIGXDSXa2EQAgWdTNUQQeHbVw0g2CDwj2AmqSQCBpwmObsYIIPA6Rt9Uk5BEfVLKerBDqWNa/rZA4PnLl9G9J4DA854pI/pHAIHnH1tG9ocAAs8frrqjIvB0yYWoHwIvRMWMwFQQeBEocsimiMBbcUFnjF0gC75Z6jbqPKhcVt61jxSVBPOVzjAsXQReGKoYrTkg8KJV76DPFoEX9ApGL38Enl01R+DZVQ8j2SDwjGAnqCYBBJ4mOLoZI4DAax999cw6+fn5XzIaDNyip/Rat4uxekU9MAIv6isgePNH4AWvZlHOGIEX5eoHc+4IPLvqhsCzqx5GskHgGcFOUE0CCDxNcHQzRgCB1z76uZ8vltkfL8po0H21zjJkp97G6hX1wAi8qK+A4M0fgRe8mkU5YwRelKsfzLkj8OyqGwLPrnoYyQaBZwQ7QTUJIPA0wdHNGAEEXvvom+oSMuGRme79d+4XE1l9vwFS0bvUWL2iHhiBF/UVELz5I/CCV7MoZ4zAi3L1gzl3BJ5ddUPg2VUPI9kg8IxgJ6gmAQSeJji6GSOAwFsx+vpFjTJ3/BJJNaSk54hK6Tyg3FitCCyCwGMVBI0AAi9oFYt2vgi8aNc/iLNH4NlVNQSeXfUwkg0Czwh2gmoSQOBpgqObMQIIPGPoCaxBAIGnAY0uRgkg8IziJ3iOBBB4OQKjuXECCDzjJchIAIFnVz2MZIPAM4KdoJoEEHia4OhmjECUBF4qmZC6pVOktKKfFJV2NsacwPoEEHj67OhphgACzwx3ouoRQODpcaOXOQIIPHPs24qMwLOrHkayQeAZwU5QTQIIPE1wdDNGICoCr77mF5n84TnSUD1LJF4sg9c7SXqstJMx7gTWI4DA0+NGL3MEEHjm2BM5dwIIvNyZ0cMsAQSeWf6toyPw7KqHkWwQeEawE1STAAJPExzdjBGIisCb+unVsnjGO79xdiTeiN0ekaLiCmPsCZw7AQRe7szoYZYAAs8sf6LnRgCBlxsvWpsngMAzX4OWGSDw7KqHkWwQeEawE1STAAJPExzdjBGIisCb8PaxUr90egbnVbe6Rjr3XMsYewLnTgCBlzszepglgMAzy5/ouRFA4OXGi9bmCSDwzNcAgWdXDYxng8AzXgISyIEAAi8HWDS1gkBUBN78n5+XmV/f0cy8oseaspoj8GKxuBV1IInsCCDwsuNEK3sIIPDsqQWZdEwAgdcxI1rYRQCBZ189vMwolnI+LwfMdayZ82tz7RL59gi8yC+BQAFA4AWqXCTrEIiKwFPFXjjtDVky6yMprRwkfVf/PQ9ZBPBnAAIvgEWLeMoIvIgvgIBNH4EXsIKRriDw7FoEHKG1qx5GskHgGcFOUE0CCDxNcHQzRiBKAs8YZAJ7RgCB5xlKBioQAQRegUATxhMCCDxPMDJIAQkg8AoIO4tQCLwsIIW9CQIv7BUO1/wQeOGqZxRmg8CLQpXDM0cEXnhqGZWZIPCiUulwzBOBF446RmkWCDy7qo3As6seRrJB4BnBTlBNAgg8TXB0M0YAgWcMPYE1CCDwNKDRJSsCiZpGmf/SZKmbslhKB1RKr92HSkmP8qz6rqgRAi9vhAxQQAIIvALCJpQnBBB4nmD0bBAEnmcogzsQAi+4tYti5gi8KFY92HNG4AW7flHLHoEXtYoXbr6/PD5BaicsbA5Y2r+zDDxqnbwTQODljZABCkgAgVdA2ITyhAACzxOMng2CwPMMZXAHQuAFt3ZRzByBF8WqB3vOCLxg1y9q2SPwolbxws136rWfSLIu8VvAmMhKp20i8dL8XqpG4BWuhkTKnwACL3+GjFBYAgi8wvLuKBoCryNCEfhxBF4EihyiKSLwQlTMiEwFgReRQodkmgi8kBTSwmmwA8/CopBSwQkg8AqOnIB5EkDg5QnQ4+4IPI+BBnE4BF4QqxbdnBF40a19UGeOwAtq5aKZNwIvmnUvxKxb3oFXNriL9NxlZe7AKwR4YlhFAIFnVTlIJgsCCLwsIBWwCQKvgLBtDYXAs7Uy5NUWAQQe6yJoBBB4QatYtPNF4EW7/kGcPUdog1i16OaMwItu7YM6cwSeXZVD4NlVDyPZIPCMYCeoJgEEniY4uhkjgMAzhp7AGgQQeBrQ6GKUAALPKH7fgzfMWiQLX/hSUo0J6bbzCKlYo7/vMf0MgMDzky5j+0EAgecHVf0xEXj67ELTE4EXmlJGYiIIvEiUOVSTROCFqpyhnwwCL/QlDt0EEXihK2nzhBrnLZWpZz8pyZr6Zf+sOC5DLthXylftE9hJI/ACW7rIJo7As6v0CDy76mEkGwSeEewE1SSAwNMERzdjBBB4xtATWIMAAk8DGl2MEkDgGcXva/BFb3wrc+8bkxGjx+gNpPcho3yN6+fgCDw/6TK2HwQQeH5Q1R8TgafPLjQ9EXihKWUkJoLAi0SZQzVJBF6oyhn6ySDwQl/i0E0QgRe6kjZPqHr8dJl5xYsZE+x71DbSbYe1AjtpBF5gSxfZxBF4dpUegWdXPYxkg8Azgp2gmgQQeJrg6GaMAALPGHoCaxBA4GlAo4tRAgg8o/h9Dz7n/rGy+PVv3DidR64s/f+6s8RLinyP61cABJ5fZBnXLwIIPL/I6o2LwNPjFqpeCLxQlTP0k0Hghb7EoZsgAi90JQ31hBB4oS5vKCeHwAtlWTMmlVhUI8m6Rinp3y3wk0XgBb6EkZsAAs+ukiPw7KqHkWwQeEawE1STAAJPExzdjBFA4BlDT2ANAgg8DWh08YVAfO4SScVjkurVZYXjI/B8wc+gPhFA4PkElmF9I4DA8w2t1sAIPC1s4eqEwAtXPcM+GwRe2Cscvvkh8MJX0zDPCIEX5uoGZG6NCely1xtS+vkkN+G6rdeS6sO3bTd5BF5A6kqaLgEEHgshaAQQeHZVDIFnVz2MZIPAM4KdoJoEEHia4OhmjAACzxh6AmsQQOBpQKOLpwTK3v9eKu9/J2PMJaeMlsYRg9uMg8DzFD+D+UwAgeczYIb3nAACz3OkeQ2IwMsLXzg6I/DCUceozAKBF5VKh2eeCLzw1DIKM0HgRaHKds+x84PvSfm732YkWbPfplK7+0gEnt2lI7ssCCDwsoBEE6sIIPCsKocg8Oyqh5FsEHhGsBNUkwACTxMc3YwRQOAZQ09gDQIIPA1odPGUQNHMhdLtkscl1pR0x01VlMnCCw5o9y48duB5ip/BfCaAwPMZMMN7TgCB5znSvAZE4OWFLxydEXjhqGNUZoHAi0qlwzNPBF54ahmFmSDwolBl++dYNHmOVLz1jfuIRd1O60picK92k0bg2V9PMvyNAAKP1RA0Agg8uyqGwLOrHkayQeAZwU5QTQIIPE1wdDNGAIFnDD2BNQgEVeAl5s6Tpp9+kvjAgVKy0hCNmdMlqAQQeEGtXDTzRuBFs+5BnjUCz67qIfDsqoeRbBB4RrATVJMAAk8THN2MEUDgGUNPYA0CQRR4DV+Nl9pnXxBJLjtyWbrNVlKxffuvlmpgoYvFBBB4FheH1JYjgMBjUQSNAALProoh8Oyqh5FsEHhGsBNUkwACTxMc3YwRQOAZQ09gDQKtBV4qlZSpU3+Q2ppqWXnlNaWiUxeNUf3tUnXH3ZL45ZffgpSUSNezTpNYPO5vYEa3ggACz4oykESWBBB4WYKimTUEEHjWlMJNBIFnVz2MZIPAM4KdoJoEEHia4OhmjAACzxh6AmsQaCnwGpsS8uYbj8jMGT+5I5WWlssuux0uPXv20xjZvy5Vt94hiXnzmwPEHIHXBYHnH3DLRu7Xo1zmLqqTZMqyxEgHAm0QQOCxLIJGAIFnV8UQeHbVw0g2CDwj2AmqSQCBpwmObsYIIPCMoSewBoGWAm/mrGnyykv3ZYwybM2NZLPN99AY2b8ujd98KzVPPuM8V7rM4HCE1j/WNo6MwLOxKuTUHgEEHmsjaAQQeHZVDIFnVz2MZIPAM4KdoJoEEHia4OhmjAACzxh6AmsQCKLAU9NMzJkrTT//zCMWGjUPehcEXtArGK38EXjRqncYZovAs6uKCDy76mEkGwSeEewE1SSAwNMERzdjBBB4xtATWINA6yO0b735qMyY/qM7kjpCu+tuR0iPnn01RqYLBPwhgMDzhyuj+kMAgecPV0b1jwACzz+2OiMj8HSohawPAi9kBQ35dBB4IS9wCKeHwAthUUM8pSA+YhHicjC1LAgg8LKARBNrCCDwrCkFiWRJAIGXJagCNUPgFQi0zWEQeDZXh9xaE0DgsSaCRgCBF7SKhS/f72a/JR9MekDqE1Wy4aB9ZfNVDmt3kq0FXvhoMKOwEUDgha2i4Z4PAi/c9Q3j7BB4dlUVgWdXPYxkg8Azgp2gmgQQeJrg6GaMAALPGHoCOwQWVE+Vez46IoPF3uteIGv23a5NPgg8lk3QCCDw7KhYShISj38lqfhckeRKzn/WdBKL2ZGcRVkg8CwqBqlkRQCBlxWmgjVC4BUMtb2BEHj21obMlieAwGNVBI0AAi9oFQtXvl/MeE5e//76jEmtP2gv2WX43xF44Sp1ZGeDwLOk9EVvSCw+6bdkkutLKrGpJcnZkwYCz55akEl2BBB42XEqVCsEXqFIWxwHgWdxcUhtOQIIPBZF0Agg8IJWsXDlyw48++qZSqXksynvyvgZ42Rg96Gy7bB9pKyk3L5EA5IRAs+GQjVJrOR+J5FUi2S6S6rxQBuSsyoHBJ5V5SCZLAgg8LKAVMAmCLwCwrY1FALP1sqQV1sEEHisi6ARQOAFrWLhy/e3O/CqnTvw9uEOPMMlfuf7p+Xl8Q81Z7FKnxFy3HYXG84quOEReHbULlbysJNIdYtk+jsCby87krMoCwSeRcUglawIIPCywlSwRgi8gqG2NxACz97akNnyBBB4rIqgEUDgBa1i0c6XO/D8r/8Nr50msxZPzgh07l53SZfyHv4HD2GEtMCrTdRIQ7JeyosqpSRWEsKZWj6l2HRnF96bzia8BmcfXqVI087Of+9tedKFTw+BV3jmRMyPAAIvP35e90bgeU00gOMh8AJYtAinjMCLcPEDOnUEXkALF9G0EXj+F/7+9y+X72Z+2hyo1Dk+e95e90hpUZn/wUMYQQm87+dOlqrE4mWzi8Wkd9EgqSjqHMLZ2j2llDhHaWOLHHHX00k0bneyhrJD4BkCT1htAgg8bXS+dETg+YI1WIMi8IJVr6hni8CL+goI3vwReMGrWZQzRuD5X/1flkyXB96/QuZXzZYSR9odsPHxssFKW/kfOKQRenYrli/nfJMxu/J4Z+lTMjikM2ZaQSaAwAty9aKZOwLPrroj8Oyqh5FsEHhGsBNUkwACTxMc3YwRQOAZQ09gDQIIPA1oGl0SyYTMXjxFelcOcB6wqNAYgS5pAr0cgffFXEfgtXg/AYHH+rCVAALP1sqQV3sEEHh2rQ0Enl31MJINAs8IdoJqEkDgaYKjmzECCDxj6AmsQQCBpwGNLkYJqCO0E+ZNlSVNC5flwRFao/Ug+IoJIPBYIUEjgMCzq2IIPLvqYSQbBJ4R7ATVJIDA0wRHN2MEEHjG0BNYgwACTwMaXYwSSD9iUeM8YtHIIxZGa0Hwjgkg8DpmRAu7CCDw7KuHlxnFUs7n5YC5jjVzfm2uXSLfHoEX+SUQKAAIvECVi2QdAgg8lkGQCCDwglQtclUE0gIvafRPINQCAtkRQOBlx4lW9hBA4NlTC5UJO/DsqoeRbBB4RrATVJMAAk8THN2MEUDgGUNPYA0CCDwNaHQxSgCBZxQ/wXMkgMDLERjNjRNA4BkvQUYCCDy76mEkGwSeEewE1SSAwNMERzdjBBB4xtATWIMAAk8DGl2MEkDgGcVP8BwJIPByBEZz4wQQeMZLgMCzqwTms0Hgma8BGWRPAIGXPSta2kEAgWdHHcgiOwJ+C7xUUuSrNxLy48cJKa+Myfo7x2Xw2kXZJUcrCLRBAIHHsggSAQRekKpFrooAAs+udcAOPLvqYSQbBJ4R7ATVJIDA0wRHN2MEEHjG0BNYg4DfAk+Ju3FPNjVnFo+L7HNWqXTqFtPIli4Q4A481kCwCCDwglUvskXg2bYGrBZ4CxYtlbMuu1Nmz10oz91/WTO7aTPnyHlX3Ss//DhVBvbvLeecfJiMXHcN98d5xCL3JYbAy50ZPcwRQOCZY09kPQIIPD1u9DJDwG+BN/bhJpnyZSJjclv+X4kMXd8xeXwQ0CDADjwNaAHr0vBTtVQ/8YskFjRK2UZdpXK//hIrDqb0R+AFbPGRLjvwLFsD1gq86po6OeT4i2XbzTeQdz/6MkPgHXHy5bLDViPl0P12lg8++caReffI649eKyXFRQg8jQWGwNOARhdjBBB4xtATWJMAAk8THN2MEPBb4LEDz0hZQx0UgRfq8kqqLiHzzp0gqVrn/P2vX+c9+kjnPfsGcuIIvECWzU069elUSc1cJLFNh0qsX9fgTiTHzDlCmyMwn5tbK/Bqautk3oLF7n8uvPaBZoE3f+ES2e3/zpAPX7hViouW3ZlywDEXyBknHCKbbjgcgaexYBB4GtDoYowAAs8YegJrEkDgaYKjmxECfgu89B14Ez9KSEVX++/AmzDta3nzi5elvKRcdt90P+nfc5CRuhC0fQIIvHCvjoYJ1bLoxskZkyweWiE9T181kBNH4AWybJK85R1JvvHdsuRLiqTo/D0ltm40/n2AwLNrzVor8NKYPhs/IUPgfTZ+olx83QPyzH2XNpM89aLbZNTIEfL7vbaTuYvq7CIcgGy6di6RuoakNDRmHmkJQOrBTjGYO/+NMy8pikun8iJZXN1oPBcvE4gJC8JLnjaN1b2yVKpqm6Qp8dvuAZvyIxcItCTQo0upFMVjsnBpgySSqUjDmTRropxz38mSSi3j0Lm8Uq485jbp1bVPpLnYNvmeXUvd9fprmWxLj3zyJJB0duDNPvuHjB14XUf3lS7Of/L5UmLm17eK0mLn11iRqrrf7gLNZx709Z9AamG11B/+74xA8ZFDpPSi0QUI7n+IjiL06V6OY+kIUgF/XNXDyy/m/CbH018NWwu8Dz75Wm66+0l55I4LmvM+98p7ZNiqg+XwA3eVhib+gJRrQYud36gnnbJF/PfpuWLLv72nP1PyTycoI8Qcz6X+cNmUCBlA/F1QlmDOeRYXxVwR4u2/HXNOgw4QyIpASXHc/euERuf3UyH7VTar+bdsdP+rd8tTYx7N6Hfa7/8h26y3fc5j0cE/Auov9tRfkER9vfpH2PzItROqZO5/Z0rT3AapHNVd+hwyKP878AwtGPVwT8z5zWwibL+PNb9MfMsguaBaFhx0T8b4pRuvJF0v39e3mM0DW/DnA3X6Ccfif6mzjaDq4eXnu8D7/OuJcsE192fciff3C2+VLTZeRw4YvS1HaDWqyRFaDWh0MUaAI7TG0BNYkwBHaDXB0c0IAb+P0BqZlGbQt52js/e9cnNG79MPukzWXWVDzRHp5gcBjtD6QZUx/SLAEVq/yHo/bmNTo0xZME3ijnAd9MhPUvTmxGVBOELrPWxGzJpA4I7QLly8VHb6/aky9tlbpKK81J3o7n84Q/55/Yn0iwAAIABJREFU9jGy4TprIPCyLv1vDRF4GtDoYowAAs8YegJrEkDgaYKjmxECCLzfsKs/vF33xAXyzeQv3H+448g95Yhd/mKkLgRtnwACj9URJAIIvGBUq66xVl755g2pqa92E+7ZuafsULeGFM9ayiMWwShhaLMMnMBTlTjq71fJJhsMl2P+MFpefnuce6T25YeukiJnC/3M+bWhLZZfE0Pg+UWWcf0ggMDzgypj+knAVoG39OcvZP5X70pJZXfpu9nezv/t4ScGxg4IAQTe8oX6ZcFMKS8tl26VPY1Wsb5hsfN73RLnEbdORvOwLTgCz7aKkM+KCCDwgrE+vpn5rXw57auMZLdcfXNZudfKwZiAh1nyiIWHMD0YylqB98aYT+W0i2933mtOOfewJKSkpFhWGdJfnr73Upkxe5784/K75IefpsmQgX3lwlOPlLXXHOriQODlvioQeLkzo4c5Agg8c+yJrEfARoG3eOKnMumpa5onVNa9nww78nIpKqvQmyS9QkMAgWdfKZPJJpn2y5tSUzvTTa5nt3WkX69N7EvUUEYIPEPgCatFAIGnha3gnRB4vyFH4BV8+a0woLUCTxcTAi93cgi83JnRwxwBBJ459kTWI2CjwJvy3E2y8LsPMya06gFnStfVNtCbJL1CQwCBZ18p5y0aL3MXfJKR2MoDRzsvsvMaroKCwLNvzZJR+wQQeMFYHbUNtfLqt78doe3RqYfsNGJH5/q74mBMwMMsEXgewvRgKASeBxCDPgQCL+gVjFb+CLxo1TsMs7VR4M1671H55cNnMvCudcx1UtZzQBiQM4c8CCDw8oDnU9cZc96VJVU/Z4zev/cW0qPrmj5FDNawCLxg1Svq2SLwgrMCGpoaZNrCGe7L7Cv1WkmK40XBSd7DTBF4HsL0YCgEngcQgz4EAi/oFYxW/gi8aNU7DLO1UeA11VbJT49fKbWzfpSY8xvS/lsdIP023zcMuJlDngQQeHkC9KF7de0smTrrVWfklDt6cXEnWWXQPs5deOU+RAvekAi84NUsyhkj8KJc/WDOHYFnV90QeHbVw0g2CDwj2AmqSQCBpwmObsYI2CjwXBjOHbO182ZIcecuUtKpmzE+BLaLAALPrnqks6lxJN7CpRMkHi+RXt3WltISfs6m2SDw7FyzZNU2AQQeKyNoBBB4dlUMgWdXPYxkg8Azgp2gmgQQeJrg6JYXgV9+mCiTPvhYyrp0lmHbbyOde2X/GqW1Ai8vInQOK4EoCLxEbUKSVSkp6e3cZaTORvEFmgACL9Dli1zyCLzIlTzwE0bg2VVCBJ5d9TCSDQLPCHaCahJA4GmCo5s2gTkTf5J3brqjuX95166y+3mnS0l5dsfXEHja6OlogEDYBd7SD5dK1dhqSSUdgde/RHoe2FOKOsUNkCakVwQQeF6RZJxCEEDgFYIyMbwkgMDzkmb+YyHw8mcY+BEQeIEvYaQmgMCLVLmtmOwnjz4pP4/9KCOXrY87SgasPTyr/BB4WWGikSUEwizwmhY1yZw752aQ7rxppXTbvosl9ElDhwACT4cafUwRQOCZIk9cXQIIPF1y/vRD4PnDNVCjIvACVa7IJ4vAi/wSKDiAb199U75+4ZWMuLuc9XfpPii7F1sReAUvGQHzIBBmgVf7Xa0sfG5RBp2SAaXS5/BeeRCjq2kCCDzTFQhm/JqF1VJfXS89Bmd/JYYXM0XgeUGRMQpJAIFXSNodx0Lgdcwo9C0QeKEvcagmiMALVTkDMZnGunoZc8c9Mu+nSW6+w3feXtbbe4+sc0fgZY2KhhYQCLPASzWlZO4980TtxEt/PX7XQyqGZXcc3oLykEIbBBB4LItcCYy5920Z/9LnbrfB660ku52+t5RWlOY6jFZ7BJ4WNjoZJIDAMwi/jdAIPLvqYSQbBJ4R7ATVJIDA0wRHt7wJLJ41W8oqO0t5l9yO2ymB99nXP8mX33wvaw1bXVZfdWjeuTAABPwiEGaBp5glqhJSNa5aEksTUrF2hVSsgbzzay0ValwEXqFIhyPOrO9myNPnPZoxmc0O3VpG7rtJQSaIwCsIZoJ4SACB5yFMD4ZC4HkAMehDIPCCXsFo5Y/Ai1a9wzDb998fK9ff+aCkUil3Okf+4UDZa7edwjA15hBCAmEXeCEsWeSn1FLgNTZWS1XdPOleOURiMR4nifziaAPAZ09/LB89NDbjR1bfck3Z5ZQ9C4ILgVcQzATxkAACz0OYHgyFwPMAYtCHQOAFvYLRyh+BF616h2G2x//tLJm7YFGzwOverZvcc8tVYZgacwghAQReCIsa8imlBd7PM9+R//1wt/PCcJNUVvSTbTY4x/m/fUM+e6aXK4HaJTXy8En3O/ff1bldY7GY7HvxgTJgrcG5DqXVHoGnhY1OBgkg8AzCbyM0As+uehjJBoFnBDtBNQkg8DTB0c0YAQSeMfQE1iCAwNOARhejBJTAmzlvkTw15mhX3qW/If22lM3XPslobgS3k8CimQvlixc+lQbnEYt1d1u/YPJO0UDg2bkmyKp9Agg8u1YHAs+uehjJBoFnBDtBNQkg8DTB0c0YgdZHaI8+/GDZ3XkIgw8CNhJA4NlYFXJaEQEl8L6e9Km8/dklGc26dh4ou426DngQsIoAAs+qcpBMFgQQeFlAKmATBF4BYdsaCoFna2XIqy0CCDzWRdAI8IhF0CoW7XwReNGufxBnrwTenIU18vqn58uCxT82T2HDNf4oawzZNYhTIucQE0Dghbi4IZ0aAs+uwiLw7KqHkWwQeEawE1STAAJPExzdjBFQAm9xdaM0NiWN5UBgCGRLAIGXLSna2UIgfQdefUO1fDvlWamqnSVD+m4uK/XbwpYUyQMCzQQQeCyGoBFA4NlVMQSeXfUwkg0Czwh2gmoSQOBpgqObMQIIPGPoCaxBAIGnAY0uRgm0fIXWaCIEh0AWBBB4WUCiiVUEEHhWlUMQeHbVw0g2CDwj2AmqSQCBpwmObsYIIPCMoSewBgEEngY0uhglgMAzip/gORJA4OUIjObGCSDwjJcgIwEEnl31MJINAs8IdoJqEkDgaYKjmzECCDxj6AmsQQCBpwGNLkYJIPCM4id4jgQQeDkCo7lxAgg84yVA4NlVAvPZIPDM14AMsieAwMueFS3tIIDAs6MOZJEdgaAKvOT3TZJ8qk5SC1MSG1UsRfuWS6w4lt2kaRVoAgi8QJcvcskj8CJX8sBPGIFnVwnZgWdXPYxkg8Azgp2gmgQQeJrg6GaMAALPGHoCaxAIosBLVSWl6ZwqkYZU84zj+5VL0c5lGgToEjQCCLygVSza+SLwol3/IM4egWdX1RB4dtXDSDYIPCPYCapJICoCL+X8QbTozTqJT2iUxMCUJPeolFjnIk1qdDNJwHaBN23RIrn3f+Pkx/nzZOSgwXL0pptJlzLEh8k1YzJ2EAVe8mvn18lbazKwxUYUS/FJnU2iJHaBCCDwCgSaMJ4QQOB5gpFBCkgAgVdA2FmEQuBlASnsTRB4Ya9wuOYXFYFXcoezm+TDGqlZOkcSiUZJdYtL6dGDpWibXuEqaARmY7vAO+HpJ+SXpUubK7HdaqvLSVtuHYHKMMW2CARR4KVqUtJ09hJnB95vM2IHXnTWNwIvOrUOw0wReGGoYrTmgMCzq94IPLvqYSQbBJ4R7ATVJBAJgefsvis7aaHULpwvDfWOyFOfs/kutVKRlF+8lsS6lmjSo5sJAjYLvIW1NXL0449mYOle0UnuOfAgE6iIaQGBIAo8hY078CxYPIZSQOAZAk9YLQIIPC1sdDJIAIFnEH4boRF4dtXDSDYIPCPYCapJIBICz2FTduoiqZo03d19537OicbUgCIpPXYVKRrRRZMe3UwQsFngKR7swDOxKuyNGVSBZy9RMvObAALPb8KM7yUBBJ6XNBmrEAQQeIWgnH0MBF72rELbEoEX2tKGcmJREXjx8Y2SvG6W1C2Yv2z3Xb+4xFaukPLT13D+Ny8rBmlx2y7w1B14d437UCYtXODegXfMqM2kspQ78IK0xrzMFYHnJU3GKgQBBF4hKBPDKwIIPK9IMk6hCCDwCkU6uzgIvOw4hboVAi/U5Q3d5KIi8FTh1EMWyXcWSGJqlcT6lkrxDn0kVlkcupqGfUK2C7yw82d+uRFA4OXGi9bmCSDwzNeADLIngMDLnhUt7SCAwLOjDuksEHh21cNINgg8I9gJqkkgSgJPExHdLCOAwLOsIKSzQgIIPBZI0Agg8IJWsWjni8CLdv2DOHsEnl1VQ+DZVQ8j2SDwjGAnqCYBBJ4mOLoZI4DAM4aewBoEEHga0OhilAACzyh+gudIAIGXIzCaGyeAwDNegowEEHh21cNINgg8I9gJqkkAgacJjm7GCCDwjKEnsAYBBJ4GNLoYJYDAM4qf4DkSQODlCIzmxgkg8IyXAIFnVwnMZ4PAM18DMsieAAIve1a0tIMAAq+wdUjWJ2TxD4vdoN3W7CbxMucVGL6sCSDwskZFQ0sIIPAsKQRpZEUAgZcVJhpZRACBZ1ExnFTYgWdXPYxkg8Azgp2gmgQQeJrg6GaMAAKvcOgTdU3y0wMTpX5BvRu0rFe5rHrY6lJcweMv2VYBgZctKdp5RWBeTa0kncH6dqrQGrJLcqF89cStUjX9O6lceT0ZvNufpaRzD62x6AQBvwkg8PwmzPheE0DgeU00v/EQePnxC0VvBF4oyhiZSSDwIlPq0EwUgVe4Us7/bL7MfHVaRsCBuw6RXiN7FS6JgEdC4AW8gAFKP5lKyZ1ffCfvT5/lZr3pgL5y4kbrSDwWy2kWUx/+hyyY8p3zdPuybl2HbSqrHnB2TmPQGAKFIoDAKxRp4nhFAIHnFUlvxkHgecMx0KMg8AJdvsglj8CLXMkDP2EEXuFKiMDLnzUCL3+GjJAdgXEzf5GbP/06o/FJjsAbNbBfdgM4rZKN9fLdDX+QxqZEs8ArKu8k6/79oazHoCEECkkAgVdI2sTyggACzwuK3o2BwPOOZWBHQuAFtnSRTByBF8myB3rSCLzCla+ptkl+/s+PUj+/zg1a1rNMVjtiDSkq5whttlVA4GVLinb5Enjk2x/lhZ+mZAwzerWV5eARq+c0NDvwcsJFY8MEEHiGC0D4nAkg8HJG5msHBJ6veIMxOAIvGHUiy2UEEHishKARQOAVtmI8YpEfbwRefvzonT2BWVU1cu6Yj6Ve7Z5T/34visul22wqAys7Zz+I03K5O/B2de7Aq+QOvJwg0rhgBBB4BUNNII8IIPA8AunRMAg8j0AGeRgEXpCrF73cEXjRq3nQZ4zAC3oFo5U/Ai9a9TY926lLquSVn6e6j1jsPHSQrNa9W84p8QptzsjoYJAAAs8gfEJrEUDgaWHzrRMCzze0wRkYgRecWpEpO/BYA8EjgMALXs2inDECL8rVD+bcEXjBrFtUs0bgRbXywZ03As+u2iHw7KqHkWwQeEawE1STADvwNMHRrZlAoikpjUvrpLRrhcSLcnvtUAcjAk+HGn1MEUDgmSJPXF0CCDxdcvQzQQCBZ4I6MfMhgMDLh573fRF43jMN3IgIvBWXrK6uSWbOrpKVh3STogL8YT9wC6jACSPwCgw8ZOGqZi2VmeOmSqK+SYorSmTQFitJp96Vvs4SgecrXgb3mEDQBV5TMinzqpdIr05dpKSoyGM6DGcjAQSejVUhp/YIIPBYG0EjgMCzq2IIPLvqYSQbBF772D8YN0NuufMzqalplN69O8m5p28uQ1fO/X4WI4UNaVAEXkgLW6Bp/fjCd9JY3dAcraJnJxm68xq+Rkfg+YqXwT0mEGSBN2vpInnsq3FSVV8nZSUl8rsRG8tqvfp6TIjhbCOAwLOtIuSzIgIIPNZH0Agg8OyqGALPrnoYyQaB1zb2ZDIlRxz7kixdWt/cYOQG/eT8s7Y0UieCLiOAwGMl6BJoqm2Uic99m9E95uyqHX7AerpDZtUPgZcVJhpZQiDIAu+Bz8bK9EXzm0lWllXIyVvuYglZ0vCLAALPL7KM6wcBBJ4fVBnTTwIIPD/p5j42Ai93ZqHrgcBru6TTZyyVE099PeMHu3cvl/vv2CN0ayBIE0LgBala9uU6wzk+u2TywubEegzrLf03HORrolETeI3Oe5LfFS+WufF6GZzoJMMSXSTm/D++YBAIssC75r2XpL6pMQP0yVvtKpWl5cGAT5ZaBBB4WtjoZIgAAs8QeMJqE0DgaaPzpSMCzxeswRoUgdd+vS696gP55LPZzQ3+cNAIOfB3w4NV4JBli8ALWUELPJ1kIikLJs6T2nnVUtm3i3RfvZfE4v7KpagJvDdLZ8u0eHVzZddJ9JCNG3sWuNKE0yUQZIH3xo/fyLipPzZPfXifgbL/upvooqBfQAgg8AJSKNJ0CSDwWAhBI4DAs6tiCDy76mEkGwRe+9hra5vkhVd+lAk/LZSNN+wvO28/VOI+/2HfyCIIUFAEXoCKRaougSgJvCZJyUPlPzv//29ft1Sp/K5+SHBWQ1NCKt4bLyXfT5XG1QdK7fYbiJQUByf/PDMNssBLplLy6fRJMmnRXBncradsMmhVHrLIcz0EoTsCLwhVIsc0AQQeayFoBBB4dlUMgWdXPYxkg8Azgp2gmgQQeJrg6GaMQJQEnoL8WNkUqYk1NfPum6qQPeoHGuOfa+DOT4yRTq9/2tytfqNhsuTP0bk6IcgCL9da0z4cBBB44ahjVGaBwItKpcMzTwSeXbVE4NlVDyPZIPCMYCeoJgEEniY4uhkjEDWBN9U5Pju2bK40pBLSKVUiOzb2k17JMmP8cw3c6/S7JL7ktyPAqdJimXfjCeJsv851qEC2R+AFsmyRThqBF+nyB27yCLzAlSzyCSPw7FoCCDy76mEkGwSeEewE1SSAwNMERzdjBKIm8BRo9ZDFklij9EiVSdC0V/erHpOSn2Y2r5dk90qZf+XRxtZPoQMj8ApNnHj5EkDg5UuQ/oUkgMDznnaysVYaZ/wgqVhcygaPkOSCGjdIUZ+u3geL4IgIPLuKjsCzqx5GskHgGcFOUE0CCDxNcHQzRsAXgZeYLpJyHtiJr+L8p5exuYUxcPGk2dLt9hckvrhKkpUVsvRPu0rD2kPDONU254TAi0ypQzNRBF5oShmJiSDwvC1zqq5KFr1zv6RqFi8beK7zn/e7SSwZl5Ithknn43b0/bEyb2dk32gIPLtqgsCzqx5GskHgGcFOUE0CCDxNcHQzRsBrgRdres/Z4jbu1/k4+9tKR0uqaE1j8wtl4MaEFM+aL039ezh8S0I5xfYmhcCLVLlDMVkbBV5Dap7zmE+TlMX6h4Ixk/COAALPO5ZqpNof3pfab95xB01V10tyzhIpmtxfYguX7b7rfNIuUjpqdW+DRmw0BJ5dBUfg2VUPI9kg8IxgJ6gmAQSeJji6GSPgrcBrkljdLc7vUhub55OKDxApO9TY/AgcLgIIvHDVMwqzsU3gTU/eJ/NSr7vou8ZHytDYyc5VAtH6i4AorDvdOSLwdMm13a+lwEsurJbUopoMgVc2ekPpdPDm3gaN2GgIPLsKjsCzqx5GskHgGcFOUE0CCDxNcHQzRsBLgZdyxF287kZnLikEnrGKhjswAi/c9Q3j7EwLvLkyS4pTxdIj1keWpMbLz8nLMzAPjh8tvWM7hBE9c9IggMDTgLaCLsnapbL43QfcI7QpZ/d86qdaKfpukPPbJOeEQlmxdL3kQCka6Oym59MmgMDTRudLRwSeL1iDNSgCL1j1inq2CLyor4Dgzd9LgefOvnGsxJo+/BVEXFKl+zg3NXM8JHgrw86MEXh21oWs2idgSuA1Oc/13B+7VsbHPnKT21r2kK2b+sms1KMZyfaK7yhDYkdRQgi4BBB43i+Elo9YFCX6SOMb30nK+XvOsl3XleKhfbwPGLEREXh2FRyBZ1c9jGSDwDOCnaCaBBB4muDoZoyA5wJPzYRHLIzVM+yBEXhhr3D45mdK4H0Qe00ejd2WAfTY5CmSTDzo3H5X/es/L5ZhRRdJJ3EeHOKDAAKPNRBAAgg8u4qGwLOrHkayiZrAa6hPyvxf6qTfoAqJF8WMMCeoPgEEnj47epoh4IvAMzMVokaAAAIvAkUO2RRNCbxH47fLB/JqBs3RqUNlm+SWMkdekmSqzjk6u7N0jg0LGXGmkw8BduDlQ4++Jggg8ExQbz8mAs+uehjJJkoC75vPFsuzD0yTupqEdO1VKv93/MoycOVORrgTVI8AAk+PG73MEQiiwGuorpPvX/qfLJ4yRwZtsoasuu165gASuaAEEHgFxU0wDwiYEnhT5Ue5IX6WJJz9duqrkM5yZuIG9y48Pgi0RwCBx9oIGgEEnl0VQ+DZVQ8j2URF4CWTKbnqtG+lZumy32ipb5XhlfLHU1fLjnuqSeKJXyQR7yuxOK+JZQfN+1YIPO+ZMqK/BIIo8F479wGZP3FGM5gND99Jhu+5qb+gGN0KAgg8K8pAEjkQMCXwVIqT5Ht5P/6qFDkX5u+Y2l/6ysAcMqdpFAkg8KJY9WDPGYFnV/0QeHbVw0g2URF4c2fVy83nf5/BuKxTkZxz4zodco83TpXKqgcklljiPGrUWWoqD5Gm0jU77EcD7wkg8Lxnyoj+EgiawKtbWCVPH3dTBpSeqw+UXS870l9QjG4FAQSeFWUgiRwImBR4OaRJUwi4BBB4LISgEUDg2VUxBJ5d9TCSTVQEnoL70K2T5YcvFjdz3nav/rLj3v065F65+FYpapzc3C4Z7yZLe57bYT8aeE8Agec9U0b0l0DQBF5jbYM8/ecbJNHw227lIaOGy1Z/389fUIZHn5iok5cblkrcyWN0aTcZWlRqOCMz4RF4ZrgTVZ8AAk+fHT0LTwCBV3jmRMyPAAIvP35e90bgeU00gONFSeDV1yXlozfnyvTJNbLmul1l5FY9JR7v+CGLrgvOk1iyLqO6S3ueJ8l41wBWPNgpI/CCXb8oZh80gadqNOmdr+R/974qifpGqezfQ7Y98yDpOrBnaMs3I9Egf6ua4dxllXLnWBaLyY2dB0vfouhdl4DAC+0yD+3EEHihLW3oJra48WupSnzq/tmjMr6RdCleO3RzZELhI4DAs6umCDy76mEkmygJPF3A5dXPSlnt2ObujWXrS02XQ3WHo18eBBB4ecCjqxECQRR4ClRjbb1U/bJIug3p47zYrfalhfd7qn6RPFi3IGOCf67oLbuVRu8vabIVeI3OX2pNr/3YZTa4YhMpiVeEd4EwM6sJIPCsLg/J/Uqgtmm6zKp7ypV3zt8RSSKRkoHl+0t58SAYQcBqAgg8u8qDwLOrHkayQeBlgT2VlLK6DyTe+KMkSoZKQ/mWIrHo7czIgpTvTRB4viOObIAlqfkyIfW5dJWeskZ8Q3F+i+0Ji6AKPE8mH5BB3nWOzt5YOzcj2zM79ZNRJZ0DMgPv0sxG4DUkq+XNORfK0sbZbuCuJQNk+77nS1m80rtEGAkCWRJA4GUJimZGCcyvf18WNy7bfZcWeD1Lt5DupRsbzYvgEOiIAAKvI0KF/XEEXmF5WxkNgWdlWUiqHQIIPJaGHwRmpH6S/yavkobUsqPya8RHyoHxkz0JhcDzBKOvgySdo7NXV8+RcU3VbpxtSrvIX50deHGPJK6vyXs8eDYC78eqN+WzhfdnRB7Z40hZvXJHj7NhOAh0TACB1zEjWpgnUJtwduDVttqBV+HswCtiB5756pDBiggg8OxaHwg8u+phJBsEnhHsBNUkgMDTBEe3FRJ4JnmbfJscl9Hmz0WXS+/YwLzJIfDyRliwAWYnG6XYuQavdwTvvktDRuAVbLkRyCMCCDyPQDKM7wSWNI6XpYnPuAPPd9IE8JIAAs9LmvmPhcDLn2HgR0DgBb6EkZoAAi9S5S7YZBF4BUNNIMsJZCPw6pNV8vaci2VJ4yx3Nl1KnBfd+14opfHoHTm2vJyRSA+BF4kyh2aSvEIbmlJGZiIIPLtKjcCzqx5GskHgGcFOUE0CCDxNcHRbIYHWR2iHxzaV/Yr+4gk1duB5gpFBCkQgG4GnUmlM1jqPWHzi/LcUj1gUqDaEaZsAAo+VESQCCLwgVYtcFQEEnl3rAIFnVz2MZIPAM4KdoJoEEHia4OjWIYH2HrH4bsyTMunzN6WscxcZsfXvZcCwjTocq2UDBF5OuGhsmEC2As9wmoSHQDMBBB6LIUgEEHhBqha5IvDsWwMIPPtqUvCMEHgFR07APAgg8PKAR9ecCUz56l357MU7m/vF40Wy47HXSmX3vlmPhcDLGhUNLSCAwLOgCB6kUF01Rz764BaZNeNT6dd/XRm15V+la9f87/T0IDXPh0DgeY6UAX0kgMDzES5D+0KAHXi+YNUeNJAC7+DjL5bvJ04R9w1u5+ta2Unee/om97/PnF+rDSOqHRF4Ua18MOeNwAtm3YKa9f+euVmmf/dhRvqb7HOiDB6xRdZTQuBljYqGFhBA4FlQBA9SePWl02XGtP81j9RvwLqy5943ezCyfUMg8OyrCRm1TwCBx+oIGgEEnl0VC6TA2/Ows+TGi0+S1VdZ/tltBF7uCwyBlzszepgjgMAzxz6KkdmBF8WqR3vOCLxw1P/B+/aUhobq5snEnL/0PvSPL0tJSXk4JthiFgi80JU01BNC4IW6vKGcHALPrrIGUuBtu9/J8uidF0j/Pj2Xo4nAy32BIfByZ0YPcwSUwJs9aZZMnbxQVl1nsHTtUWkuGSJHggB34EWizEzyVwIIvHAshddfPkumTf2oeTLswAtHXZmFPwSSqaQ8NuEVeX3y+9K9vKscvOYesolz9NyPD4HnB1XG9JMAAs9PurmPHUiBt+Eux8g2o9aTz7+eKL17dpO/HXOAbLPZ+u7sZy3gCG2uy6BHZanU1iekrjGRa1faQ6DgBF57+H2Z+OUUaUompbikWH53zA4ycJXs7yMreMIEjDyBXl3LZEl1ozQmkpFnAQD7CfQPTCBnAAAgAElEQVTpVi7FRTGZu7hOmhIp+xMmwzYJpO/Amzl92R14m20V3jvwlHSe56zXJMuVnw2aBF5zxN1d4x9r7l0kcbltpwukZ0V3zRHb79aprNj9NXZJTaPnYzMgBPwgMKBnBY7FD7CaY6p6ePnFUs7n5YCtx0o6/3Y+76p7ZLftR8nmG4+Qdz/4Us76553y/L8vd3fk+Rvdz5nlP3aysUnmf/Gj1P0yXyqHDpAea6/SfE/gikZXVwm6RfO1cvnPjxEgsHDeUrn+nN9+g6WIrLvJqnLg0dsDBwLWEuDXWGtLQ2JtEPj1euFI/36KhREsAu6vsfweNlhFsyzbS8beLW9P+SQjq3O3PFp2GLqx55nya6znSBnQZwL8Gusz4ByHT/8akmO3dpv7LvDaivzHU66Q/ffcVkbvtHmkH7FY+PpH0jhzbjOiTusPk8oN1uywtmE9QptYkJSqD5ukuHdcOm1SLLF4hyhoYDmBmiW1cs+lT0lRPObsDFm2m2mVtQfLXkduZ3nmpBdlAjxiEeXqB2/uHKENXs2injF34EV9BeQ/f7UD7+4WO/BKi0vlpu3O8WUHHkdo868XIxSWAEdoC8u7o2iBO0JbU1svEydNl/VHrNY8t0NPvEwOO2AX2XW7TSIr8BK1dTL/8Tcy/gqyuFul9Nx3e4nVpaR4fIOkikSa1i0VKVn2em/6C6PAq5+ckNln10qyetlfyVZsVCT9zuuExOvoV4QA/Pgbj30oEz6b5Aq84tJi2fdojtAGoGyRThGBF+nyB27yCLzAlSzyCSPwIr8E8gbQ+g68/xs+Wjbqt3be47Y1AALPF6wM6iMBBJ6PcDWGDpzAW7S4SnY++DS58ZITZYuN15Ex476S0y+5Q178zxXSq0fXyAo8dXJ53n9fkZRzjDb9lQ7qKz1HbSIVty+VokXL7rdLDiyWqmO6iJT9JvHCKPDm3VYnS1/JvFtiwFUVUj68WOOnCV1sIlDi3Bvyy+TZMn3qIhk6YhCPWGRRnAUzmmTB1CbpvUqJdO/vmHy+ghJA4BUUN8HyJIDAyxMg3QtOAIFXcOTWBFTibfLiOdKvU3fpXBqMF5YReNYsHxLJkgACL0tQBWoWOIGnuLz74Zdyze2PyJz5i2RQ/95yxl8Okc1GjnCRRfkV2rops2TJ2M+dbXYJiXeukO47jpLOXxZL2WuZD3vUHlwpjes5O/F+/RB43v9sq69x7iOcUSulnYqk10Bn51/mpkfvA0ZoRPUKbZdOxTJ/SUOEZq0/1Qlj6uSbN+vcAdQ63HDvTrLyhr/9/NcfmZ7ZEkDgZUuKdjYQQODZUAVyyIUAAi8XWuFpO7t6oVzy4UMyZfEvztUqRfKXDfeSXYZuZP0EEXjWl4gEWxFA4Nm1JAIp8FaEMMoCT3FJOvIusXiplPToJuLcE1b2Tm0kBV7DzwmZdU6LI7Qji6Xf+RUFOUK7ZH69jH93jiQal93R1mtQJ1l7qz52/cwPcDYIvOyL5/zFtLx87RKpr/7t9dMuvYtkpxOdXbh8BSOAwCsYagJ5QCBsAm/aR5Nk9pczJV4Sl5W2WEX6DO/vASWGsIkAAs+mahQul+s/eUrenOJsXPj1UxLvkdH/kIoSu/+SEoFXuDVCJG8ItBZ46uTfpJ/Gytw5P0hZWaWsvOqW0qPHEG+CMUqHBBB4HSIKdoP4kqRU3NHiCO0A5wjtsc4f3kvDfYRWVc3UIxbffThP5k6tzlg4G+8+UDp1LQn2YrIkewRe9oVQAu/5yxc5Mvm3Pgi87Pl51RKB5xVJxikEgTAJvHkT5sj3z43PwDbyT5tJp56dC4GSGAUigMArEGjLwhz32o0yfem8jKyu3u4YWavXSpZlmpkOAs/q8pBcGwRaC7wZ0z+XyY7AS3/xomLZaNPDpbSUf7cWYgEh8ApB2XCMWG1SSr5tkmQsFZlHLEwiR+D5Sx+BlxvfiR/Wy9evLjtG7x6h3cc5QruB3X87ndsM7W+NwLO/RmT4G4EwCbwfX//e2X03I6O8q+00XAZsMIiSh4gAAi9ExcxhKq9N/lRu+vSZ5h7Deg6Ra7Y72jmAFM9hlMI3ReAVnjkR8yPQWuB9M/5ZWbRgasagI9bZW3r0Wjm/QPTOigACLytM4W4UxjvwTFas9RHa3kM6yYgtOELrVU0QeLmT5BGL3Jl52QOB5yVNxvKbQJgEHjvw/F4tdoyPwLOjDiayGDv9a3l32ngZWNlL9hu2pXQrs38HEALPxEohZj4E2IGXDz3v+yLwvGcauBEReN6XjEcsvGeaHhGB5x9bRvaHAALPH66M6g+BMAk8RUjdgTfz8xlSXFbEHXj+LBnjoyLwjJeABHIggMDLARZNrSCw/B14SZky6SP5Zfa33IFnoEIIPAPQbQuJwLOtIuSzIgIIPNZH0Agg8IJWsWjnGzaBF+1qRmP2CLxo1Dkss0TghaWS0ZkHr9DaVWsEnl31MJINAs8IdoJqEkDgaYKjmzECCDxj6AmsQQCBpwGNLkYJIPCM4id4jgQQeDkCo7lxAgg84yXISACBZ1c9jGSDwDOCnaCaBBB4muDoZowAAs8YegJrEEDgaUCji1ECCDyj+AmeIwEEXo7AaG6cAALPeAkQeHaVwHw2CDzzNSCD7AnYLvC+eGyRfPdilZRWxmSjQ7vL0M3tv1A5e/q01CGAwNOhRh9TBBB4psgXNm7VgomycMbHUlTaWXqvtI2UVvQsbAIeRkPgeQiToXwngMDzHTEBPCaAwPMYaJ7DsQMvT4Bh6I7AC0MVozMHmwXez2Oq5Z1r5mYUY79bB0n3wSXRKRAzXY4AAo9FESQCCLwgVUsv16oFP8mkT+/4f/bOA86Oqu77v9u3l2Q3m957b5SEkgRCDSJNQKooiKKCgvL4KDwooIiFFwQFrBQLShOQEkEINQkthZDey2ZTtpe7t79zd8nd3LS9d+7MnHNmfvd5efGTzDn///n+TsLud2fOpAZ7AyUYceyN8GoyT8UPBZ6KqTm3Zwo852av6sop8ORKjgJPrjyEdEOBJwQ7i+okILPAe/fBWqx5pTltZTO+1hNjzijWuVoOswMBCjw7pOicNVDg2T/rHaueRt32RWkLHTzlKyiuGK3k4inwlIzNsU1T4Dk2emUXToEnV3QUeHLlIaQbCjwh2FlUJwGZBd6Bd+C5PcA5v+YdeDqjts0wCjzbROmIhVDgGRNzczSOtxujaIkmMKXYixEF2n8QJPns3vwGdq17Ka2bYUd/CwWlAyXpMLs2KPCy48WrxRKgwBPLn9WzJ0CBlz0zM0dQ4JlJV5G5KfAUCYptdhDQI/A2fNiIVW/Xo7inD0edU4WCEq9pNPc/A2/qpeUYMrPAtFqcWA0CFHhq5MQuOwlQ4OW+E0KxBH67I4gGTd7t+1xclYcxhXJIvFg0hC1L/4jW+k0d7VUOnoPeI87MfeGCZqDAEwSeZXURoMDThY2DBBKgwBMI/xClKfDkykNINxR4QrCzqE4C2Qq81e/W45X7N6eqlVYFcPkvx8Drc+nsgMNIIDsCFHjZ8eLVYglQ4OXOf11bDH+paU+baHyRF1/oFch9cgNnCLXuhtuXD59f7WMeKPAM3BScynQCFHimI2YBgwlQ4BkMNMfpKPByBGiH4RR4dkjROWvIVuA9/8uN2Kjdgbf/58LbR6DvyCLnQONKhRKgwBOKn8WzJECBlyWwQ1xeG4nj19uCab9zYrkfJ5fzhUa50z14Bgo8M6hyTrMIUOCZRdb+8yYiIcTf+gfiaz4AynrBM+siuPuNNH3hFHimI86qAAVeVrjseTEFnj1zteuqshV4r/9pG5b/Z28Kh0u78e7L94/TDuv22xUR1yUZAQo8yQJhO0ckQIFnzAZ5qz6M/9ZHOibrF/Dg8j4B5Lt557cxdNNnsULgbdu2CU8+8Rh2Vm/HpMnT8IWLr0IgYO4dlWt3NmHByt0I+Nw4bWIf9C7LNwMf57SYAAWexcBtVC721j+xZ/Xz+HBUEPXFUQyoL8aME36DgL/U1FVS4JmKN+vJKfCyRma/ARR49svUzivKVuC1NkTw3N0bsXtTG9weF46/tC+mntnLzoi4NskIUOBJFgjbocCzaA+0xOLQnqZFpd8NqjvzoJst8GKxGH74/W+iuakhtYiT5s7D+V+4zLRFbd7Tijue+QSJz45RLAx48eMvTECPInOloWkL4sQpAhR43Ax6CYT+ejueG74Ezfnx1BQje5yCE/p/U++UGY2jwMsIk2UXUeBZhlreQhR48mbDzg4mkK3AS86Q/AK4dls7Css9yC/mI0zcV9YSoMCzljer5UaAd+Dlxo+jrSdgtsDbtnUTfvaTH6QtbNCQ4bj5+3eYttgnF23Fy0ur0+a/du4IHDO8p2k1ObE1BCjwrOFsxyp1ix7FswXPdC3N7UFZ1VScX3mLqculwDMVb9aTU+Bljcx+Ayjw7JepnVekR+DZmQfXJj8BCjz5M2KHXQQo8KzfDZGWGNY9sxdNG9vRZ2YJBp5SjuRxD/xkRsBsgXeoO/DmnHQ6Lrjoyswa1HFV8tHZx97amDbyxrPGYHx/cx+V09Eqh2RJgAIvS2C8PEUgFg7i6Y3XozmqHQ3k8cFV3AMji0/E8aWXmEqJAs9UvFlPToGXNTL7DaDAs1+mdl4RBZ6d07Xn2ijw7JmrXVdFgWd9sguuX4/6VW2pwhO+1hfDz6+wvhFFK5ot8JJYkmfg/fPvj6Jm5w5LzsCLaI9f3/fyaqzc3tSRypxxVbj8hCGKJsS29ydAgcf9kAuB2sh2LG55FnXR7RgUmIRjis6F323u+ZgUeLkkZvxYCjzjmSo3IwWecpE5umEKPEfHr+TiKfCUjE26puPtbWj7ZDFiTXXIGzoWgSFjTOmRAs8UrIedtL0ugpcvWpX2++WjCzD7/uHWNqJwNSsEnig8uxrbkae9xKK0gC/esjqDkHsTgt6VyIuOQl7cuD+PFHhWJ8l6uRKgwMuVoLHjKfCM5ankbBR4Ssbm2KYp8BwbvbILp8BTNjppGk8k4mj49+OI7uk6E6toxmnIHzPV8B4p8AxHesQJI8E4XrpgJeLhrkPJ+55QimP+b5C1jUhUbc/eZrw4fwXa2sKYc8JIjBnd54jd2VngSRSLo1pp8M1Hbd6fUmvuGboCZeF5hjCgwDMEIyexkAAFnoWwMyhFgZcBJLtfQoFn94TttT4KPHvl6YTVUOCJTbnVtRef5v8DjZ6tqIyMw9jQefAnisQ2lWX1aEMt6p/5Xdoof78hKD3t4ixn6v5yVQTeptbXsLzxTwjHWzC66HxMKDPvPLLuqeV2xdb5dVh6fzVioTgK+/ox8ydDUNTfmW8bbWpuxy23P4/Gxs5Hil3aYYD/851TjyjxKPBy238cfTCBzUXXIubqeuuwJ1GGwS0PG4KKAs8QjJxEJ4G63c2Y//j72LZuN4aM7YszrzoGhcV5R5yNAk8nbJOGUeCZBFalaSnwVEqLvVLgcQ+oRoACT2xi7xTcjWbPjlQTvaOTMSX4ZbFNZVk9Hgmj9q/3AvFYamRg2DiUzDo7y5m6v1wFgdcY2Yrnq5OHdmuvGP/sc2LlnRhUMKf7BUp6RbQthpadYZQMzoPb49w3WCz6YBMe/P2baSnNOXEUvnTZjMMmR4En6aZWuC0KPIXDY+tHJPCH/3sROzfXpq4ZOXUALvr2kf/bSYEn16aiwJMrDyHdUOAJwc6iOglQ4OkEx2HCCFDgCUOPkKsRrxfdmtZAIFGCk1ruFNbUp6Ew/tDUgupoDLPyA/hySTHy3N0Lm/a1y9D83vwOieftUYniky+At7jM8HVYJfAS4UZg4z+QaFwNFA+Da+hFcOVl9uKGNc3/wvt1v0hb+4jic3Bsj+8ZzoMTWktgw6Y9uP2uF9OKXnj+NMw7bQIFnrVROLragY/QVoauQkn4dEOY8A48QzByEh0EWhqDuPf6p5BIdP3wK1Dgw80PffGIs1Hg6YBt4hAKPBPhqjK1CIEX187zeWbjn/HB7gUoD1TgnCFfwujySaogY58CCVDgCYTP0roIUODpwmbYIJnuwAvFE7hyVy2a97uT7uKSIlxeXJjRehOhdkRbGjsEnsvlzmhMthdZJfDiqx4E6pZ3tVc0EO5J/5tRu3a8Ay+jhTvkon8+8xFefOWTjtWOGdUb3/7mXOQFvBR4DslflmXyJRayJME+jCTAO/CMpClmLgo8MdylqipC4L2+4zk8veGPKQ4+TwC3H/U7lPjLpWLDZuQjQIEnXybs6MgEKPDE7hCZzsBbFQ7ju3vq04CM9Pvw/yp7iIW0X3XLBN7iG4FocL/KLriO/X9waV8PZPLZdwZeJN6KUUXnKX0GXibrddo1jc1BtLaE0bdPabdL5yO03SLiBRIR4B14EoXhwFaSZ+C9/Mgi7Ni4l2fgKZo/BZ6iwRnZtgiB98Ant2FV/ZK0ZVw3/jaM6zHNyKVxLhsSoMCzYag2XxIFns0DzmJ5ud6Bl0Up3ZdaJvByuANP9+I40JYEKPBsGattF5WtwIs3xRGvj8Nd5oa71Jw7r20LmwszhAAfoTUEo2GTUOAZhlLdiUQIPN6Bp+5+Ed05BZ7oBFg/WwIUeNkSs/f1es/As4qKVQIvEW7SzsB7QtcZeFaxYB01CFDgqZETu+wkkI3Ai22PIboqknpfj3eUF55Bh3+cnIxJwAwCFHhmUNU/JwWefna2GSlC4MUSMbyw+S9YWPMaz8CzzU6yZiEUeNZwZhXjCFDgGceSM5lPwCqBZ/5KWMEpBCjwnJK0PdaZjcCLvBtGvDWeWrgr4IZ/lt8eILgKZQhQ4MkVFQWeXHno7qbp3U/R+M5K+HqXo+KcGfCWZnYgdrKgCIGne6Ec6HgCVgm8Fu2w+KZgEH1Ky7TD4rt/Q6TjgyGAwxKgwOPmUIkABZ5KabHXJAEKPO4DlQjkIvDgdyEwSzsnlF+WqhS58r1S4MkVIQWeXHno6qb+taWovv/51Ni8Ib0x9FdXawdBZ3ZOAgWeLuwcJIiAFQLvPyuX4aVPlyIWj2NAeQW+PusUFAfyBK2YZVUnQIGneoLO6p8Cz1l522G1FHh2SNE5a8hG4PERWufsC5lXSoEnVzoUeHLloaubLT/6K1qWbEgbO/yB6xAYUJHRfBR4GWHiRZIQMFvg1bY040cvPpW22pNGjce5k4+ShADbUI0ABZ5qiTm7Xwo8Z+ev4uop8FRMzbk9ZyPwkpT4Egvn7hVZVk6BJ0sSnX1Q4MmVh65uqh94AfWv7vdGV+1xv1GP3wRvcUFG81HgZYSJF0lCwGyB99HWjXhk4Ztpqx3UsxLfnXuWJATYhmoEKPBUS8zZ/VLgOTt/FVdPgadias7tOVuB51xSXLksBCjwZEmCAk+uJHLoJrynEVvv+DtCW3YDXg/6fPlU9JiX+d1CFHg5wOdQywmYLfAisRh++sqz2Kvdibfv85XjTsLk/oMsXysL2oMABZ49cnTKKijwnJK0fdZJgWefLJ2wEgo8J6RsrzVS4MmVJ+/AkysP3d0ktLO62rfsgb+yFJ6i7M7qosDTjZ0DBRAwW+All9QYbMNrq1do/27F9EHDMLHfQAErZUm7EKDAs0uSzlgHBZ4zcrbTKinw7JSm/ddCgWf/jO22Qgo8uRKlwJMrDyHdUOAJwc6iOglYIfB0tsZhJHBIAhR43BgqEaDAUykt9pokQIHHfaASAQo8ldJir0kCFHhy7QMKPLnyENKNKIEXjgKRmAuFgYSQdbOomgQo8NTMzcldU+AZl/4GTzU+9K1FyB3D+PBATI2OMG5yztRBgALvyBuhbnsL3nh4BWrW1WPI9CrMvmYcCkoD3D0CCVDgCYTP0lkToMDLGtkhB8SjCTSs1o67SbhQOrIQnoDbmIk5y0EEKPDk2hQUeHLlIaQbEQJvTbUbO2pdHestL0pgwsB48vg+fkigWwIUeN0i4gWSEaDAMyaQBlcLnsx/C3F0/dBnTmgSRsb6G1OAs1DgZbAH/nL9m9izqSl15YiZfXHW/07LYCQvMYsABZ5ZZDmvGQQo8HKnGm2PYd1ftiNUG+mYzF/qxcgrB8Cbz28mc6d78AwUeGZQ1T8nBZ5+drYZabXAa2gBPt6U/hfsyD5x9K/gnXi22VQmLoQCz0S4nNoUAhR4xmD91LsV7/g/SZtsTHQgTgxPMKYAZ6HA62YPtNa143dXvpp2VUF5Hq597BTuHoEEKPAEwmfprAlQ4GWN7KABdZ80Y+tLu9J+vf+pvVAxpST3yTnDQQQo8OTaFBR4cuUhpBurBd6WPS5sqEm/zbmqDBg3ICZk/SyqFgEKPLXyYrcABZ4xu4B34BnDsbtZ+AjtkQnxDrzudpD1v0+BZz1zVtRPgAJPP7t9I+tXNWPL8+kCr+9JFeh1lPYNJT+GE6DAMxxpThNS4OWEzx6DrRZ4ybPvFq/1aOffdfJLPkg7fXgMxfn24MlVmEuAAs9cvpzdeAIUeMYx5Rl4xrE83EwUeEdmzDPwzN+D2VagwMuWGK8XSYACL3f6sXAc6x7bjvbacMdkHY/Qfkl7hDaPj9DmTvfgGSjwzKCqf04KPP3sbDPSaoGXBNcWArbudSGmvcSiX884ygptg5MLMZkABZ7JgDm94QQo8AxHyglNJECBZyJcTm0KAQo8U7ByUpMIUOAZAzYp8RrXaOcyabeC8CUWxjA93CwUeObyzXZ2CrxsidnwehECz4YYuSSLCFDgWQSaZQwjQIFnGEpOZAEBCjwLIBtcomlrBJtfC8KlPdIwaG4BSgZ4Da4g93QUeHLnw+7SCVDgcUeoRoACT67EKPDkykNINxR4QrCzqE4CFHg6wXGYMAJOFXixmmrA64OnolIYexbOngAFXvbMRI5oqYni3dvrEA93vgjME3DhuFt7oLC3cyQeBZ7IHcja2RKgwMuWGK8XTYACT3QC6fUp8OTKQ0g3FHhCsLOoTgIUeDrBcZgwAk4TeIlIBK333Y3IRx90MA+ceiYKrrpWGH8Wzo4ABV52vERfveGlVqx9JvkYWddn3GUlGDjbOQcLU+CJ3oWsnw0BCrxsaPFaGQhQ4MmQQlcPFHhy5SGkGwo8IdhZVCcBCjyd4DhMGAGnCbzQf+ej7Q+/TeNd9IMfwzdhsrAMWDhzAhR4mbOS4cqdH7Zj6UONaa1M/lop+kzPk6E9S3owWuBFNy1H6PkHkNizDd6pcxE46xtw+Z3D05LQHFyEAs/B4Su6dAo8uYKjwJMrDyHdUOAJwc6iGRKIhUMI7d2F/N794HJ7QIGXITheJg0Bpwm8tj/+FqHX5qfxz7/4cuR9/gJpMmEjhydAgafW7kjEgaW/a0SNJvKSn74z8jHxqhLtv5dqrSOXbo0UeHHta462289GIth1V2Ng7pfgP+OaXFrkWBJIEaDA42ZQjQAFnlyJUeDJlYeQbijwhGBn0QwI1H+6BFuffRyx9iB8ZT0w7LLrUNZ/AIoLvKht6nx1PD8kIDsBpwm86Mb1aL7tf4BotCMaV0Ehiu++j2fhyb5RP+uPAk+RoA5os21vTHsXI5Bf4VFzATl0baTAi25egeD96Y/8uweOQ+ENv8uhQ+cNbW+PY83aEPx+YMTwPHi9yd3JT5IABR73gWoEKPDkSowCT648hHRzJIFXu2U1tix5DZFgG3qPmo5BU08S0iOLOo9AIh7Hsp/ciHgolFp8ychxGPvl6ynwnLcdlF6x0wReMqzI2tUIvfoSXNpLLPLOPg+ePv2UztBJzVPgOSlte6zVSIHXcQfenech0dqQgsM78LLbJ62tMTzyeD1aWmIdA6uqfLjkonJN5h0s8cLuBgS9O+GP9kR+vFd2hRS92myB14xdWIv/IOoKY1hiNiowTDlS7cF2LH53Ifbs3IWR40Zj4lQewSEyRAo8kfQPrk2BJ1ceQro5nMALNtfjo6fvRVKk7PuMPOE8VI2YIqRPFnUWgfY9NVh534/TFu0rKsG0W35BgeesraD8ap0o8JQPzcELECXwtm8G1q10Y+ioOAap9/2mg3eM+KUbKfCSq0mdgVe7A97JJ/EMvCwjXrS4FW++nf5ilbPPKsWY0ennCDZ7N2JvwUJt9s43KJe1T0R5eGKW1dS73EyBF0Q95rt+hAjaOsC4tP87KfED9MBgpUA98tvfo3rb9lTPc886HUcfN0OpNdipWQo8udKkwJMrDyHdHE7g7dm4HKsXPJnWU+XQCRg9+0IhfbKo8wise+TXaF6/KrXwvnPPxsC58yjwnLcVlF4xBZ7S8UnVfHNdPVa+/R7aW1ox6pjp6D18qOH9iRB4b81346k/dz36ee4VMcw5s+uHh4YvkhPaioDRAs9WcAQsJlOBt6P4BYRdXS9gccGDQU0Xd0gnO3/MFHib8S4+cD2Shm9U4nRMxPnKIK3X/jv34C/uTeu3z4D+uOo6nkMpKkQKPFHkD12XAk+uPIR0wzvwhGBn0QwIxLTHZ3e98yraarahdOQEVEybiYDfS4GXATteIg8BCjx5slC5k1AwiH/96n4Em5pTyzj9q1cZLvFECLwffs2H5q4nFlFcBvzkoYjKcbF3CwlQ4FkIO4NSyUdnH/3Lfo/Q9tIeof2i9gitL13MHSzwvJrAu4gCLwPGh7tEe3YFb7v+X9pvT0tcjqE4MYdZrR0aCoXx/+74GeKxzkewk5+xkyfinIvUkZDWEjO/GgWe+YyzqUCBlw0tm17b3Rl4mz9+FVHtJQJVo6Zh8NSTbUqBy1KFAN9Cq0pS7HMfAQo87gUjCGz5dCXeePTvaVONPPYozDzvbCOmT81BgWcoTk5mAQEKPAsgZ1kik5dYHPgIbXloEspCE7KspN7lZt6Bl6SxxPU3rMcbHWD6YAJmJK7T7m30KgVq+UdL8NKzL3RIvPKePXDxl69AeY9ypdZgp2Yp8ORKkwJPrjyEdMO30ArBzqI6CVDg6QTHYcIIUOAJQ2+rwnu378C/f/1Q2pqmnnYyJp4829B1ihB4Bz5Ce+wFW9wAACAASURBVMGX4jjx9K67LwxdICezHQEKPHUj5UsszMmuHY2IIoQiqPtikHB7CMnHaSuqesHjcZsDirNmRIACLyNMll1EgWcZankLUeDJmw07O5gABR53hWoEKPBUS0zefj96+VV88sZbHQ1WDRmMuV+5HD6/39CGRQi85AL4EgtDY3TUZBR4jopb+cWafQee8oC4AOkIUODJFQkFnlx5COmGAk8IdhbVSYACTyc4DhNGgAJPGHpbFg62tCDUFkRZr0pT1idK4JmyGE7qCAIUeI6I2TaLpMCzTZSOWQgFnlxRU+DJlYeQbijwhGBnUZ0EKPB0guMwYQQo8IShZ2EdBCjwdEDjEKEEKPCE4mfxLAlQ4GUJjJcLJ0CBJzyCtAYo8OTKQ0g3FHhCsLOoTgIUeDrBcZgwAhR4wtCzsA4CdhB4TQjjbU8NihJeHBfvrR3fbr/zk+LRONo31yLSEIS/VzHyBpTB5Up/y6eO+JUcQoGnZGyObZoCz7HRK7twCjy5oqPAkysPId1Q4AnBzqI6CVDg6QTHYcIIUOAJQ8/COgioLvBqXG34jn+RdoR7qGP1IxJl+GX4GPhsJvGaPtqGcE1TKuH8Eb1QONKcx6p1bCNLh1DgWYqbxXIkQIGXI0AOt5wABZ7lyI9YkAJPrjyEdEOBJwQ7i+okQIGnExyHCSNAgScMPQvrIKC6wPuTZw2e8m5MW/nt4emYnrCP3ErEEqidvxJIdC3TWxRA2azhOhJXfwgFnvoZOmkFFHhOStsea6XAkytHCjy58hDSDQWeEOwsqpOAXQResKUVqz/+BMHmFgwaPQL9hg3SSYTDZCdAgSd7Qur0FwpG8cHza7FjXR1GTO+DyacOhdtt7GOTFHjy74dEIoH619ch3h5JNeurKELpMc787wgFnvx7lh12EaDA425QjQAFnlyJUeDJlYeQbijwhGBnUZ0EZBV4DY1BfPxJNcpK8zF5XJ8jflMdi8Xw8mNPoq2pOUVhxry5GDB8iE4qHCYzAQo8mdNRq7d/3PkO1i7ekWr6hC+Ow+xLxhu6CNUF3k7tEdob93uEdkyiHD8LH227R2hDNc1oXb4D8UgMrnwfSqYNhK80z9C9oMpkFHiqJMU+kwQo8LgPVCNAgSdXYhR4cuUhpBsKPCHYWVQnARkF3vbqRvz8gTfRFuy8G2LCmN741tUzDyvx9u7chdf/+XwagYEjh+HYM07SSYXDZCZAgSdzOur0FgnFcPcXnkby7qt9n4r+Jfj6g2cYugjVBV4SRqMrjHfc9n6JRXKdCe1FFrHWELwl+YCxN2IauqfMnowCz2zCnN9IAhR4RtLkXFYQoMCzgnLmNSjwMmdl2ysp8GwbrS0XJqPAe/zJJXjzvfQzl/73htkYNrjnITNo0x6bffHPT6R9Iz5y8gRMnnWsLTNz+qIo8Jy+A4xZfzyewH1XvYCWumBqwiGTq3DZHbONKfDZLHYQeIYC4WTSE6DAkz4iNrgfAQo8bgfVCFDgyZUYBZ5ceQjppjuBF0IEH/lXYId7N4bHBmJSZLT2LjcH/6hXSErGF42F2xGu24q8XsPgcnuML2DSjHYQeEk0qz5chk8Xfoh4PI7yXhU44ezTkFdYYBI1TiuSAAWeSPr2qr1m0Q48d+9ihFojKKksxEW3HI/eQ8sMXSQFnqE4OZkFBCjwLIDMEoYRoMAzDCUnsogABZ5FoDMsQ4GXISg7X9adwHsq/z9Y59mcQnBsZDLmhI62MxLbr61x9QJUv3g3Yu2t8JZWYdAXfob8qmFKrFtGgbdNe4T2F/s9Qjt+TBWuv/q4bg+XDwXb0d7ahpKe5XC5KMWV2IA6mqTA0wGNQw5LINweRd2OZvQaXAa3x/i/NyjwuPlUI0CBp1pizu6XAs/Z+au4ego8uVKjwJMrDyHdHEngRRDFr4r+jIT2f/s+PRNl+GrrhUJ6dUrR9hagfk8CVYNcmgQydtUJ7Y6v1fd9HrG2xtTERcOOxeCLf25sIZNmk1HgJZeazUssTELDaSUlQIEnaTBs65AEZBd4bTt2oHHNShQOHIyS4SOYIgmAAo+b4EACiWgEri1LgOa9QK+hQJ9R2jmRxv/AQw95Cjw91DhGJAEKPJH0D66tpMDbVr0bt/78T1izfiv69q7AD2+4HFMndH4RV13bdTaMXKjl7aa7O/DuL/wrWlytqQX0i/fGFW1ny7sgxTtb/GICLz8SQzyqfc2hCbzLf+hCaaVxFi+0dwvWPXx5GiVvUQ+MvuFfSpCTVeApAY9NCiFAgScEO4vqJCCzwNv7/iJsfPRPqfNDB3z+XPQ97UydK+UwuxCgwLNLksatw/Wx9qKw2m1dEw4/Bokh040rkMNMFHg5wONQIQQo8IRgP2xRJQXelTfchZOOn4rLzjsF7334qSbz/ohX//Er+LweCjwd+6s7gbdWe3z23wULEEqEUZwowgXtp6B3rFJHJQ7pjkBbcwJ3XxnTvjnpunLaXBc+/w1jz6jb8s/vo3nde6kiVbOuRuXxV3TXnhS/T4EnRQxsIgsCFHhZwOKlwgnILPCW334rgjU1KUZuvx/T77lfO8fVuB9yGRVAuKkdzRsb4C8NoHhIuVHTcp5DEKDA47ZIIxBqheutR9KhFJYjMfMSKUBR4EkRA5vIggAFXhawLLhUOYFXW9+E0y+5GQv//Rt4PZ1S44JrbsPN130RR08ZTYGnY9N0J/CSU4a1R2nr3A3oFe+hvcBCvi+UdSxbyiHrPkrg8Ttjab1V9HPh+geMFXixcBB17z+Jtp2rUTx8BsonzZPyG6BDhUSBJ+XWZVNHIECBx+2hEgEKvNzTatnWhC3PrUI8Eu+YrGxsJQacNjz3iTnDIQlQ4HFjpBHQfgruWvB7QHuMdt8nUTEQmPI5KUBR4EkRA5vIggAFXhawLLhUOYH38SfrcPs9j+Jff74zheemH/8Wx0wdiws/Nxu1TWELsNmrRHGBFyHti8zwZ19o2mt1aq1GO54OD3wnguqNXbfgXXC9B9NPMVbgqUUlvVuvdmh7QZ4HTa3aM8b8kIACBEoLfWjVXjwQje13a60CfbNFZxIoK/LDo/2crqEljFinf5Lms3vxQqz90x9Tj9AOPvc8DDhjnjT97Wtkw7Mr0bC+Pq2vidcdBV+hX7pe7dBQebG/Y7/u//SCHdbFNegnkKhZj+jyV4FYFK7CUnimaD+oLu6pf0IDR+b5PR1/x7a2p//A3sASnIoEDCXQs8RPx2Io0dwmS+Zh5MeV0D5GTnjgXO99uAK//sPTeOKh21K/dcvdf8TIof1xxRdOM7M05yYBSwi0NCa0R8Ij2FOdwLTZHu0fryV1WYQESIAESIAEZCfQvG079qxYgbKhQ9Fj1Egp2/30qZXYtWJ3Wm8zbjgW+eV5UvbLpkjAjgQSUe2HEM0N8JRWKPOUiR1z4JpIgATkJmC6wFuyYh1u++UjeP6Rn6RI3Pij32Dm9PG44KxZHXeS8ZMdgeQdTfF4Atr/40cSArFIGC17d6C4V3+4PT5JupKjDbf2EjG39v/xbiY58mAX3RNI/h0b0/6CNffHW933wSvMJxDX7rKs2xFEWe88eP1qHjeRPKYg+bLGcDTOPatzyzRta8SKv3yC+Ge3MPaa2Asjzx6tczYO646AT/s7Nvk1Ab+M7Y4Uf18GAsm777RX4nZ8XcAPCahAwOd1IRLlfpUlq4DP2K8vTRd49Y3NmHvhTXjnuQeQn9d5++AZl96Mn/7vNZgyfgTPwNOxszI5A0/HtByik0D95uVY+/JvEQ02w68dujvqrOtR0k/Ouwx0LjGnYTwDLyd8HCyAAM/AEwBdQMnGXe1Y8KctCNaH4cv34OgL+mHAhFIBneRWUuYz8HJbmbWjQ40hNG+oQ0C7644vsTCXPc/AM5cvZzeWAM/AM5YnZzOfAM/AM59xNhWUOwMvubiv3PhzHDV5NK659Cy8/MbijkdqX/7rz+HRfqRRXRvMZv28ViNAgSfXNvjwD99GqGlPqqmiqqGYdOkdcjVpdTfaTyndbREk8rzwaf8kz23keZdWh8B6eglQ4Oklp9a4N/+0GTtXN6ea9gbcOP/HY7VHt7Tb2RT6UOApFBZb7SBwKIFXU9OIxYs2obm5HcOGVeLoY4Z0fJ/ADwmIJkCBJzoB1s+WAAVetsTMvV5JgbejZi9+cNfvsWbDNgzo2ws/uulLGDdqcAcpCrzsNwwFXvbMzBoRaW3A+w9/I216l/YI7cwbHjGrpPTzJsVd8Ypd8ASjSGiPyYRHVSBvWDkFnvTJscF9BCjwnLEX/nXHarQ3d731MLnqM787EiW9AkoBoMBTKi42ewiBFwlH8cTfP0BY+/e+z5SpAzF12iDyIgHhBJwk8Bo2bMf2t5bBkx/A4FOOQn5P9e5KF75hJGiAAk+CEPZrQUmBdySEFHjZbzAKvOyZmTli7SsPYc/Kt1Ml+kw5DUPnXGFmSannLvp0N/x721I9upI/QT91KGrb+BZaqYNjcykCFHjO2Awr/7sby+fvSi22akQR5lwzRLnFU+ApF5njGz7wDryd1Q146cVP0rhU9CrG5z8/2fGsCEA8AacIvKS8+/i+J1NvDfcV5OHYW78Ef1GB+BDYQVYEKPCywmX6xRR4piOWvwAFnlwZxaIR7Fw6H0071qJ84DhUTTxZe5GFc99EW7ZwO9z7/RTdlTxd/fj+qHV75AqO3ZDAYQhQ4DljayRfUrLh/TpUa4/RVvTPx4jjK+DTHqNV7UOBp1pi7PdAgcc78LgnZCbgFIG39qkF2Pbmx2lRjP/Smaiaxhf6yLw/D9UbBZ5ciVHgyZWHkG4o8IRgZ9EMCeTtaELB+rrU1YmyfLhPGMBHaDPkx8vEE6DAE58BO8icAAVe5qx4pRwEeAaeHDmwi8wIWCnwEtqbsKMrtiDRHoFv4iC4tEdZrfpsfu0DbHiu64miZN3pN30RpYP7WNUC6xhEgALPIJAGTUOBZxBIlaehwFM5PQf0rt3V4t/VAn9tG2KFfsQGlaG41E+B54Do7bJECjy7JOmMdVDgOSNnO62Sb6G1U5r2X4tVAi8RjaH1wVcQr+78Ibi7uACF3zwDrhJrHmGNtoex/Hf/Qv267R31B8yZhpHnzbJ/wDZcIQWeXKFS4MmVh5BuKPCEYGdRnQT8XjffQquTHYeJIeB0gbc31IqYK4Eqf5GYAFg1KwIUeFnh4sUSEBAp8JpaW/Dah4uxeVc1+ldW4fSjZ6KkkH/XSbAtpG3BKoEXXbMDbY+8nsYhcNoUBGaPt5RNa00dfAUB+EsKLa3LYsYRoMAzjqURM1HgGUFR8Tko8BQP0GHtU+A5LHAbLNfJAu83Wxbi3dpNHSlOLxuAbw2eCR/Pr5R6V1PgSR0PmzsEAZEC72+vvYwtNdWprgZW9calp8xjTiRwWAJCBd7JExGYO4npkEBWBCjwssJl+sUUeKYjlr8ABZ78GbHDLgIUeNwNqhFwqsBb0rgDv9jwZlpcXx88Ayf0UO/NrKrtuVz6pcDLhR7HiiAgUuDd88/HEQqHU8tOvmjrxouugN/r3JePidgDKtW0SuAlH6Fte2g+YjtqO/C4ivJRdP08uIrzVcLFXiUgQIEnQQj7tUCBJ1ceQrqhwBOCnUV1EqDA0wmOw4QRcKrA+/uOpXhh18o07idXDsdXBhwtLAsW7p4ABV73jHiFXARECjzD78BrbUN8bx3cA/pqh5ap9xZruXaG/m5q1m3AqrcXam8S92PC3Nkoreqlf7IDRlol8JJl973EIp58icWEgXAX5Bm2Dk7kHAIUeHJlTYEnVx5CuqHAE4KdRXUSoMDTCY7DhBFwqsDbG27F91a9iFAs2sHe43Lj7jFnom9eibAsWLh7AhR43TPiFXIRECnwjDwDL/byq4g8+ne4olG4Bg2E9/vfhruyQi7YDuhm79ZteO6n9yCR0N6ipn0CBQW44PYfIL/YmLMNrRR4DoiLS7SAAAWeBZCzKEGBlwUsu15KgWfXZO25Lgo8e+Zq51U5VeAlM93a1oBX9qxGVPtG6LTKkRhW2NPOUdtibRR4tojRUYsQKfCMAp2ob0D4qzdot0x1SqPkx3PayfBec6VRJUyfJ9Gu/bDG64LL6zG9lpkF3n/mBSyf/9+0EnOuvgLDjppqSFkKPEMwchILCVDgWQg7g1IUeBlAsvslFHiHT3j5mnV49LkX0NDUglNmHoMLTz9Fe6KBjzSI/DNBgSeSPmvrIeBkgaeHF8eIJUCBJ5Y/q2dPwA4CL/7xMkR++qu0xbtGDIP/rtuyB2LxiEQ0jsSWRiSCESTPAESvAu3OQXXfOLryjbfx3hNPp1E863vXo/fwoYaQpcAzBCMnsZAABZ6FsDMoRYGXASS7X0KBd+iE6xoacf1Pf45wOJK64Mvnn4PTT5hp9y0h9foo8KSOh80dggAFHreFSgSyFXgJJLCk9XWsCi5ClX8wji86B3ludb95Vykr9tpJwA4CD7EYwjffqomw7alYvddfC8+Jx0kfc7ymBYm9bWl9ekZqd1v71bwTLxaJYv5vfofqVWs71jTupBMx46LzDMuBAs8wlJzIIgIUeBaBzrAMBV6GoOx8GQXeodN99+OluO+xv6X95swpk/DtKy+183aQfm0UeNJHxAYPIECBxy2hEoFsBd67Lf/CS/V/SC2xf2AUvt4r/U4ildbPXtUjYAuBp2FPNDUh9vzLSNTshmfm0XBrT36o8IltqAe0u+/2/7gHlMBVqvYLExp27tLOv8tHfqmx57ZS4Kmwq9nj/gQo8OTaDxR4cuUhpBsKvENj5x14QrZjt0Up8LpFxAskI0CBJ1kgbOeIBLIVeA/uugnbw2vS5vx+38dQ7OlB0iRgCQG7CDxLYJlQJN4UQmJrY9fMPg/cI3rA5dYep+XnIAIUeNwUqhGgwJMrMQo8ufIQ0g0F3uGx8ww8IVvyiEUp8OTLhB0dmQAFHneISgSyFXj/qP0llrctSC3R7fLilr5/R8Cdr8yyg23NWLvyfeTlFWD4mKPg8XiV6Z2N2uQRWsWDTEo8NGgiz+eGp0L7s69JPH4OTYACjztDNQIUeHIlRoEnVx5CuqHAE4KdRXUSoMDTCY7DhBGgwBOGnoV1EMhW4O2N7MCje3+EuuhO+FwBnF3+dUwtnKujspghjfV78Pc/3oZga+cdRFX9huHCL90Kr9cnpiFWzZoA78DLGhkHCCRAgScQPkvrIkCBpwubaYMo8ExDq87EFHjqZMVOtTORvW4UF3hR2xQmDhJQggAFnhIxscnPCGQr8JLDYokYdke2ooe3SrvzrkAplgsXPI1Fbz6T1vM5l9yMISMmKbUOJzdLgefk9NVbOwWeepk5vWMKPLl2AAWeXHkI6YYCTwh2FtVJgAJPJzgOE0aAAk8YehbWQUCPwNNRRpohb7/2BD589wUKPGkSyb4RCrzsmXGEOAIUeOLYs7I+AhR4+riZNYoCzyyyCs1LgadQWGyVd+BxDyhHgAJPucgc3bDTBF5rcz0ef/iHqUdo+w4YifOv+AEfoVXoTwEFnkJhsVVQ4HETqEaAAk+uxCjw5MpDSDcUeEKws6hOArwDTyc4DhNGgAJPGHoW1kHAaQIviYgvsdCxUSQaQoEnURhspVsCFHjdIuIFkhGgwJMrEAo8ufIQ0g0FnhDsLKqTAAWeTnAcJowABZ4w9Cysg4ATBZ4OTBwiEQEKPInCYCvdEqDA6xYRL5CMAAWeXIFQ4MmVh5BuKPCEYDelaF0ojAU1e7EnFMKgwkLM6V2BgMdtSi1Rk1LgiSLPugcS2NrYiCW7dmFEeQ+Mraw4LCAKPO4dlQhQ4KmUFntNEqDA4z5QiQAFnkppsdckAQo8ufYBBZ5ceQjphgJPCHZTij66fguaItHU3GNLi3Fy316m1BI1KQWeKPKsuz+BBZu34N73P0Qikej45YvHjcUXx489JCQKPO4dlQhQ4KmUFnulwOMeUI0ABZ5qibFfCjy59gAFnlx5COmGAk8IdsOLNkYieGz91rR5ywN+XDZ0gOG1RE5IgSeSPmvvI/CNl+dje1NzCojf68E/zjsHbpfrIEiZCLxIJI5/Pb4MH76zHT0q8nHBlydj2OjD39XHJEjALAIUeGaR5bxmEeAdeGaRlWPetu01CO7ai6LB/RHoWSZHUzl0QYGXAzwOFUKAAk8I9sMWpcCTKw8h3VDgCcFuSlHegWcKVk5KAgcROFjgeTWB93ndAu+Vp1bh3//4NFUnv8CHOx48E3nav/khASsJUOBZSZu1jCBAgWcERTnn2P3WB6j9YHlHcy63G/3mzUbxyCFyNpthV5kIvNi6BkRf3KItGvCePQSeISUZzs7LSMB4AhR4xjPNZUYKvFzo2WQsBZ5NgtSW0XkG3h7tDLwwz8CzT6xciYQEDnyE9pLx43DRuDGH7DSTO/Duu+1NrFu5J238jT+Zg6Eje0q4erZkZwIUeHZO155ro8CzZ65x7UiYNfc/Bu2sitQC8/pUYsglZyu94O4EXnx7C9q+9RZc0XjnOvM8yH9gFtxVBUqvm82rS4ACT67sKPDkykNINxR4QrCzqE4CfIRWJzgOM5zAloZGLN1tzEssDrwDL3nn3Z28A8/wzDhh9wQo8LpnZOYV7dpLqP678HVs3L4FE0eNxwnTZsKt3XnEz+EJUODZc3c4VeBFnlyP8GOr00L1XzcBvjMG2TNorkp6AhR4ckVEgSdXHkK6ocATgp1FdRKgwNMJjsOEEcjkDjyegScsHmkKJ28yWa49KbZqtQtVVS7MnBFHIGB9exR41jPfv+L9f3kIy1Z1PjKY/Jx10pk45+SzxDYleXUKPMkDyqG9gx6h/dxJKB6utsjq7g68yBvbEb5naRq1wA+nw3ts7xxIcigJ6CdAgaefnRkjKfDMoKrYnBR4igXm8HYp8By+ARRcfiYCT8FlGd7yxuZaLNm7HUW+AGZWDUaxL8/wGjJP+M47wOuvd91pNUj7HvXKKz97hMrCxinwLIR9QKlwJIxv/Pg7qbdbJ3+7d2UV7vz2beKaUqAyBZ4CIeXQYuu2nWjfXeucl1jEE2i/6yPEFtV0UPPO0V7e8e1J0A7ZzYEih5KAfgIUePrZmTGSAs8MqorNSYGnWGAOb5cCz+EbQMHlU+B1H9qaht14clPXHQflgQJcPepYBDze7gfb5IqHH3Zj1670xdx4YxxFRdYukALPWt77V4vH4/jez3+IxubG1C+PGT4aN111vbimFKhMgadASGwxRaC7O/D2XRiraYPLo3m7Sp59x+0jlgAFnlj+B1anwJMrDyHdUOAJwc6iOglQ4OkEx2HCCFDgdY/+mc3LsbK+826DfZ+Lh07F8NKK7gfb5Iq//c2F9eu77rDwa4/P3vy9uHb+mbULpMCzlveB1ZasXIY/Pf0Ygu1BlJf1wLcu+xoG9ukvtinJq1PgSR4Q20sjkKnAIzYSkIUABZ4sSXT2QYEnVx5CuqHAE4KdRXUSoMDTCY7DhBGgwOse/Rs71+Pdmo1pF359zHHomVfY/WCbXLFHewnxX//mRpN285XXB5z9uTjGj7d+cRR41jM/sGIoHMauvTXo17sfPG7tFhx+jkiAAk/fBolqbzn99/zNWLZyL8aN7oFzzhgKr9finxjoa13pURR4SsfnyOYp8OSKnQJPrjyEdEOBJwQ7i+okQIGnExyHCSNAgdc9+mA0jL9vWILqts5HB+f0GY7jeg/tfqDNrojFgN2ayOtRDiEvsEjipMCz2aZywHIo8PSF/OvfL8Mrr21JDT7tpIG44drJ+ibjqIwJUOBljIoXSkKAAk+SID5rgwJPrjyEdEOBJwQ7i+okQIGnExyHCSNAgZc5+t3BFhT6fCj0Cnj9auZt2vpKCjxbx2vLxVHg6Yv1kmv/g4aG9tRgf8CDZx45U3tsny9L0Ec0s1EUeJlx4lXyEKDAkyeLZCcUeHLlIaQbCjwh2FlUJwEKPJ3gOEwYAQo889DHkMAOhFABHwrARw2NIE2BZwRFzmElAQo8fbSvvekNbNvenBo8oH8xHv7VHH2T2XBUfTCMd7fVoSUUwfR+PTC8hzFHOlDg2XCz2HxJFHhyBUyBJ1ceQrqhwBOCnUV1EqDA0wmOw4QRoMAzB/0eVwT3eLZityusqTsXroz2xnGJMnOKOWhWCjwHhW2TpVLg6QtyhXb23R33fITm5hCKiwO49cZpGD/WOS8OOhK1UDSGexdtQLMm7/Z9Lp04AGMqS/TB3m+UbAJv+94WfLRxLwI+N44dWYWyQt4Bn3PINpuAAk+uQCnw5MpDSDcUeEKws6hOAhR4OsFxmDACFHjmoH/YU4333Z1n5iU/SYn368hI5IGHsOdCnAIvF3ocK4KAGQKvftlu7PzPRsSCMVQc1xd9Th4iYmmm14xE4ti6vQn9+xZr527yLuZ9wNfubcZjy7am8T9Kuwvv86P75JyJTAJvm7bOv729DolE57LyfF589dSxKAh4c14nJ7APAQo8ubKkwJMrDyHdUOAJwW6rott2RVFS5EJpoflf/FHg2WrrOGIxFHjmxHyLbyN2ao/P7v/53+ggDE8UmFPQIbNS4DkkaBst02iB1767FavueR+J2GdWQ2M16OIx6Dktd3ljI+y2XsrethDuXbg+bY2zBlfilGG9cl63TALvtWXb8eGG3WlrOvvoIRjbX3uTEj8k8BkBCjy5tgIFnlx5COmGAk8IdlsUbQ3G8Yu/NWLNlnDHei44qRDnzy4ydW0UeKbi5eQmEKDAMwGqNuV/XXX4m3dXavIh2il4P4gM1O6/4wHsuRCnwMuFHseKIGC0wNuzcAe2PbMmbSkVx/bDwPNHiVgeawoi8JomthZs1l4Lrn0GlRXiskkDkO/N/QfVMgm8xet24Y1PdqQRvnTWSAzoae7X8oIiZVmdBCjwdIIzaRgFnklgVZqWAk+ltOTq9anXW/D0gta0pu7+Rg8MrPKZ1igFnmloObFJBCjwTAKrvgZGaQAAIABJREFUTfuuqwFL3C2oSvgxL96TL7IwADUFngEQOYWlBIwWeLwDz9L4pC7WEo6iVfunqijPsD5lEnjJs/6eXbQRm3d3vsxk+vBemDuxv2Fr5UT2IECBJ1eOFHhy5SGkGwo8IdhtUfSux+qxfH3n3Xf7Pl/+XAlOOSrftPVR4JmGlhObRIACzySwnNYUAhR4pmDlpCYSMFrgJVt1yhl4JsbCqQ9DQCaBt6/F2uZ27fw7DwrzzPsBPDeEugQo8OTKjgJPrjyEdEOBJwS7LYqu2BjGTx6pT62lrNiDe77VA/l55h0iT4Fni63jqEVQ4DkqbuUXS4GnfISOW4AZAs9xELlgywjIKPAsWzwLKUmAAk+u2Cjw5MpDSDcUeEKw26boJxvC+O+HQZQVuzFvRj4qy819cxUFnrFbZ9PSpVj09JMItQUx4aSTMOnMeaiNRtHD44XPzbPEjKBNgWcERc5hFQEKPKtIs45RBCjwjCLJeawgQIFnBWXWMJIABZ6RNHOfiwIvd4bKz0CBp3yEjloABZ5xcdfX1OCfP/o/xGOxjknDiQSiX7gQsclTkO/24HOlJRjs9xtX0KEzUeA5NHhFl02Bp2hwDm6bAs/B4Su4dAo8BUNzeMsUeHJtAAo8ufIQ0g0FnhDsLKqTAAWeTnCHGLZiwQK8/dfHU79Tq4m8xNFHI3Du+R2/Vubx4KsVFcYVdOhMFHgODV7RZVPgKRqcg9umwHNw+AounQJPwdAc3jIFnlwbgAJPrjyEdEOBJwQ7i+okQIGnE9whhh14B94u7dFZ34UXwTd1Wurqb1RWoFC7G48f/QQo8PSz40jrCVDgWc+cFXMjQIGXGz+OtpYABd4heCeA2NY2xHe3w13mh3tIIVxeHuNi7c48fDUKPFmS6OyDAk+uPIR0Q4EnBDuL6iRAgacT3GGG7X8GnmfmTOw8/oTUlePy8zGvpMTYgg6cjQLPgaErvGQKPIXDc2jrFHgODV7RZVPgHRxcdE0L4mubU7/h7p0H71HliiZsv7Yp8OTKlAJPrjyEdEOBJwQ7i+okQIGnE1wGw2LaT0CXBtuwORxGf58P0woK4HXxJ6AZoDviJRR4uRLkeCsJUOBZSZu1jCBAgWcERc5hFQEKvEMIvDf2IN4S7foN7UtP3+m9eReeVZuymzoUeJIE8VkbFHhy5SGkGwo8IdhZVCcBCjyd4DhMGAEKPGHoWVgHAQo8HdA4RCgBCjyh+Fk8SwIUeAcDiyyqQ2JPqOs3/G74T60C+DPkLHeXOZdT4JnDVe+sFHh6ydloHAWejcJ0wFIo8BwQss2WSIFns0BtvhwKPJsHbMPlUeDZMFQbL4kC7+Bw440RRD+oB4IxuHxueCaVwt0nz8a7QK2lUeDJlRcFnlx5COmGAk8IdhbVSYACTyc4DhNGgAJPGHoW1kFAVYHnWrUB7hffBLwexL9wOhL9tLs3+HEEAQo8R8Rsm0VS4B0mSu0Yl3hTGK5CHx+dlWy3U+DJFQgFnlx5COmGAk8IdhbVScAJAi8aiWLlJxtQu7sOJWXFGDVuKIqKC3QS4zDRBCjwRCfA+tkQUFHgudZvgfemu+GKxjqXWlSIyP23INGrRzZL57WKEqDAUzQ4h7ZNgefQ4BVeNgWeXOFR4MmVh5BuKPCEYGdRnQScIPBWLF2Hndt2pQjlF+Th+JOn6yTGYaIJUOCJTkD++g2JvdiZ2IqeqEIvdz+hDaso8Dx/fgaep+ancYt+41LEzzxRKEsWt4YABZ41nFnFGAIUeMZw5CzWEaDAs451JpUo8DKhZPNrKPBsHrDNlucEgffmf95HOBROS27WKUfDn+e3WZrOWA4FnjNy1rvKTfFVWBj/DxLa/yU/k93HYZz7KL3T5TxORYHnfuVteO//S9raI3d+G4kpY3LmwQnkJ0CBJ39G7LCLAAUed4NqBCjw5EqMAk+uPIR0Q4EnBDuL6iTgBIHHO/B0bg5Jh1HgSRqMQW1F0YYtnidQ6/4YhRiIwZFLtH/3z3j2F2KPoSlRl7reAy8u8l6nvXzPnfEcRl6oosCDduyA966H4V68vANFbN4sxK67xEgsnEtiAhR4EofD1g4iQIHHTaEaAQo8uRKjwJMrDyHdUOAJwc6iOgk4QeDxDDydm0PSYRR4kgZjUFsbPY9hl1t7ecJnH7+rJ6aGf5axgDtI4Ll8uMjz9YzHG7SM1DRKCrzPundV70ZCe4kFevU0Ggvnk5gABZ7E4bA1CjzuAeUJUODJFSEFnlx5COmGAk8IdhbVScAJAk8nGtOGeWItyI/s7Jg/6OuLmKfQtFp2nJgCz46pdq1pqe+HCKImbZGTo3ciP9Eno4XzEdqMMPEiEjgsAQo8bg6VCPAOPJXSyr3XcKQVy7e8iNqmzRhSdQxG9D0RLpcr94ktnIECz0LYGZSiwMsAkt0vocCze8L2Wh8FnrV5umNBlAWXwpWIdxROuDxoKJiEuDvf2kYUrkaBp3B4GbR+4B14gUQFpkTvyuoOOr7EIgPQvIQEDkOAAo9bQyUCFHgqpZV7ry+8/yPsrFuZmmjGmCsxYdC83Ce2cAYKPAthZ1CKAi8DSHa/hALP7gnba33ueBSx5hrEC6vg8frstTgJV5Mf3o6C0Ja0zlryhiHk6y1ht3K2RIEnZy5GdZXrGXhG9WHUPCo/QmsUA86jFgEKPGPySuzai9iW7XDlBeAeOUT7d54xE3OWNAIUeM7ZEG2hevzljWvTFlxZOgLnzviJUhAOJ/Bi0Tg2frAd9TXNqBxYjsFT+sDlVuvuQqWC+KxZCjwVUzO4Zwo8g4FyOtMI7Nm0Cov+ei+i7a3wFpTi2C9+E5VD+JZB04BrE/uje1EcXJNWojl/FMLeCjPL2mpuCjxbxWn7xVDg2T5i2y2QAi/3SGM7dyO2+OPURK7CAnhnz4TL5819cs5AgefQPRCJhTSBd432nqX2FIHkY7SnTLlJKSKHE3gfPrcSO9fuTa1l6FH9MW72UKXWpmKzFHgqpmZwzxR4BgPldKYReOXe76FldzU82k93orE4iqv64fRv/8K0epw4SSCBouBaBDSRl/yEfL3Qkjdc+1/8CVum+4MCL1NSvE4GAhR4MqTAHrIhQIGXDa1DXxv9YBniOzrPut338c6YBndVZe6Tc4Y0ArwDz1kbYl31W3jr098hFgujpKA3Tpt6M8qLMn9TvQy0DiXwYpE4Xr7vHSQSXR0GCv049bpjZWjZ1j1Q4Nk63swWR4GXGSdeJZZAsLkBL971jY4m9gk8t/YI7fl3PCq2MYdUd8c7f3oYd/ORmmwjp8DLlhivF0mAAk8kfdbWQ4ACTw+19DGxVesQW7Mh7Rd9c0+Aq4gvrcqdbvoMFHhGE5V/vkgkiMZgDXoUDYTbrb0pXbHP4e7Ae/XBxWhvCaVWU96vFMdfMkmx1anXLgWeepkZ3jEFnuFIOaFJBN5/8kFsXfJOSuANOfokTD/3apOqcVoSMIYABZ4xHDmLNQQo8KzhLEOVaGsYsdYoAr0KZGhHdw8UeLrRpQYmwmFEFmqP0NY3aGdYueEeNQwe7R8nf+qDbXhm3Qo0tgcxd8hITKzM7M3i3TGjwOuOEH9fNgKHE3i7N9Xh43+vRqQ9iryiAI4+byxKq4pla992/VDg2S7S7BdEgZc9M44QQyAei2Ljwvlo3LEOpQPGYOgxJ8Pt4fksYtIwr2qsqRFunx+ufHu86ZYCz7y9wpmNJ2CGwGsNtsDn9cOv/bnmRw4Cta9vRd3b2zqayR9Yij4Xj4InX80XQ1HgGbSntGfh4s0tcAW0//4GAgZNmj7NdtcmrPEuw7DYWAyOjzSlhhGTBiMR3PT6C9jT1pKa7uZj5uCovgNynp4CL2eEnMBiAkd6C200EkNrXRuKehZqLxd0W9yZM8tR4Dkz97RVU+BxE6hEwK/9x6G4wIvaprBKbbPXDAgkolG0vL0A0V01HVfnjRmH/ElTMhgp9yUUeHLnw+7SCRgp8KKxCFZtWIam5vqOIv37DMWgvs6+q0eG/Rba1YqtDy1N/1rwhAHoedJAGdrLugcKvKyRCRmwwPsingw8pJ2s23lo1nnhr2Bu5FwhvXRXdEnNDvx04X/TLjtFuwvvq5NzP9+LAq87+vx92QgcSeDJ1qsT+qHAc0LK3ayRAo+bQCUCFHgqpZVdr8FVn6J92ZK0QcWnnA5vT7XfeOs0gffBjnr8e00N2mMJnDSkAqcN75XdRuDVQgkYKfC2Vm/Atp0b09YzecwMFBYUCV2j04s3fFiDPS+mn3dWMKwM/S4bpyQaCjw1Yrsl/yrUufekmi1BOX7W+riUzVe3NOGGV/+V1tt5oybgi2Nz/6EiBZ6UkbOpIxCgwJNre1DgyZWHkG4o8IRgZ1GdBCjwdIJTYFjre+8gvHVzWqcF049BYPgIBbo/fItOEnjbGoO48801aTC+dvQQTOldqnSGTmreSIH36bqP0dBUm4Zv6MAx6FOp1hv47JZ/PBTD5gc+Rqyl6072vl8cg8KRPZRcKgWeGrH9uOBr2OXanmq2OFGGu9oeg3binpQLeGbNJ/j7ys4fKo6uqML/HDsbRb7cHy2mwJMybjZFgafMHqDAUyYq8xqlwDOPLWc2ngAFnvFMZZkx+ehs8wLtkZXP3knvzi9A8elnwh1Q+823ThJ4b26uxd+Wd56rte9zwuAKXDaRwkaWP2fd9WGkwGtorsOnaz9KlfT78zB17Ax4eHZpdzGY/vuR+nY0LKpGtCWC0mm9UTBUXclOgWf6djGkwLve+fhr4P7UXDI/QruvycZQEE2hEAaUlBnCIDkJBZ5hKDmRRQR4B55FoDMso6TAu/jrt2P1ui2Ay9WxzJKiArz17K87/nd1bTDDpfOyfQQo8LgXVCJAgadSWtn3mpR4oQ3rAZ+v4ww8T5H6j9o5SeBta2jDnW+tTQued+Bl/+dA5AgjBV5yHUmJV7Nnu/YCiwD69hqIvIA9Xk4jMiPWTidAgafOjtjm3oC1nk+kf4mFmUQp8Myky7n1ENhWs1g77uJ97YdrPgzudwKqeqYfp0CBp4eqeWOUFHjzLv8+7rv9Wxg+pN9BZCjwst8sFHjZM+MIcQQo8MSxZ2V9BJwk8JKEOs7AW7sL7dE45mhn4J3OM/D0bRxBo4wWeIKWwbIOIkCB56CwbbBUCjwbhGijJeypX4MVa59KW9FR47+CosLeqV+jwJMrcCUF3qzzbsA/Hr4NvSsPPquDAi/7DUaBlz0zjhBHgAJPHHtW1kfAaQJPHyWOkoUABZ4sSbCPTAlQ4GVKitcZSWDt6tV47qmn0dzYjBknHIfTzpoHt7v78/wo8IxMgXPlSmDN5pdRvevjtGlGDj4D/aqmUuDlCtek8UoKvCmnXoMTj5mIJSvWoaJHKb59zQU48dhJHYgo8LLfKRR42TPjCHEEKPDEsWdlfQScJPA27mjHc280oLYpiqmjC3D2rHJ4PZ3HXfCjBgEKPDVyYpddBCjwuBusJtDY0IC7bvsxIuGuF8Gce9GFOH72rG5bocDrFhEvsJDAoe7Amz7+ahQXVlHgWZhDNqWkFXir129FNBZLW4vP68WIIf1x68//iNPnHIMZ08fizfeW4fs/fRgvPHZXxx157eH0MdnAcOq1Pq8bsXgCce0ffkhAdgIu7exLnyYEwtrjefyQgAoEktI5Ekto7+aw99+x7aE4vnvfFiT/ve/zuRPL8flZar7ZUoW9ZUaPAZ+n44jhUCS2730yZpThnCRgGIGAz63tV35NYBhQTtQtgfcXfYDfP/iHtOumHzMd1153TbdjPW5Xx9+xUe3rAn5IQAYCG7YtxPqtC+H1+jFq8Gz0rxqf1lae3yOFYwkHE9jwURStDQn0Henp+MeJn2QeRn5c2jcohvxtdMvdf0SwPZTWW1lJEW79zhUH9XvVd36G8+fNwllzZ6CuuesnIUYuzM5zFed7O77woRCxc8r2WZvP40Z+wI2mtqh9FsWV2JpASaEPbe0x7Yt1e3+DuXZLO379xM60LAf1CeB7V/S1db52W1yptl+T32A2tkY6frjHDwnITqCsyI+m1jC4XWVPyj79NWh34N3+g9sQiXR933nBFy/CrJNmd7vI5Dffyb9jW9v5dWy3sHiBFASST+uJdixJw7T4yQha67q+Lhk924N+o42VWVIA76aJZB5GfgwTeIdrqi0YwrpN2zFp7LDUJZd98ye4/IJTcdrso/gIrY40+QitDmgcIowAH6EVhp6FdRJwyiO0Ie3Ou9t/vwPB/e7AO3VGKU6fWaaTHIeJIMBHaEVQZ81cCPAR2lzocaxeAjwDTy85jhNNoDXagse2PoT369/FoIJhuGrQddq/hx62LRleYtGm3XX30VPpT1qW93dh/OnOE3jSPkJ7uB3U0NiCUy7+Lu6745uYOX083l68HN+74yG8+PjP0LO8hAJPx98IFHg6oHGIMAIUeMLQs7BOAk4ReEk8HWfgLdDOwGvkGXg6t4vwYRR4wiNgA1kSoMDLEhgvF0qAZ+AJxc/iGoHfb74Pr+16McWiZ6AXHpj0GNyuQ7+ERQaBFwkl8P5fY9qRX10R9h7lwogTKPBy3dSm34GXbPDNhcvwywefwO7aBvTrXYGbv/FFHDt1bEfvfIlF9hFS4GXPjCPEEaDAE8fejMrtsSiWtu6BW7sjfkpxFXwZvMHNjD7MnNNJAs9MjpzbGgIUeNZwZhXjCFDgGceSM5lPgALPfMascGQC31n+FVQHt6VddM/EP6Bf/sBDDpRB4HV4nk8T2LhYO59Xk3iFPVwYe6oLeUXdv/nZbvtBuTvwuguAAq87Qgf/PgVe9sw4QhwBCjxx7I2u3BQN4ZdbP0JDpL1j6t6BQtw4cDry3Pb6aRoFntE7h/OZSYACz0y6nNsMAhR4ZlDlnGYRoMAziyznzZTAgXfg9Qr0xn2THpH6Drx9a9O+dUCoNYGC8s6XwTjxQ4HnxNQPWDMFHjeBSgQo8FRK68i9vlG/Fc/uXp920ZV9x2GadieenT4UeHZKU821hIJRLP9ob0fzE6dVIKC9vOpwHwo8+TKOxmKY/+5CfLJ2I8pLi3H6ccdiUN8+8jUqqCMKPOPBB7WX221riqLA50K/Yp9jv1E2nqx251CeF15P54uC+CEBEQRUPANPBCdZa1LgyZqMhX1R4FkIm6VyJkCBlzNCaSZ4tXYzXti7kQJPmkTYiB0JtGnfJN5/58fYU9PWsbxefQrwzR9ORYH2ttlDfSjw5NsFry/+EAs++CjVWJ4/gJu+dAkCfmPfRCffyjPriAIvM06ZXtUQjOG1LW2IRDsPm+pT7MWsgYWUeJkC7OY6CjyDQHIaywjI8gitZQuWvBAFnuQBWdEeBZ4VlFkjGwKvfbIF766pRr7fi3lThmDcgIrUcAq8bEjKfW1rLIK7t3yQeoS2b14RvjNgGgJ8hFbu4NidUgTee6Mazzy2Jq3n864YhZlz+lLgKZLkw08+ix27dqd1e/UFn8fA3r0VWYG5bVLgGcv3g+og1teF0yadO7QQlQWHv3PX2A7sPRsFnr3ztePqKPDkSpUCT648hHRDgScEO4sehsCHG2rwxHtd32x63C587/NHo6Ior2OEEwWea/c2uGq2IN53GFBhr8em+BIL/lVgNoFoIoxd7evRGq1FvqcYvQIjEPAUml1Wmvkp8KSJQncjB96BFwj48d0rL+UdeJ8RpcDTvbUOOZACz1ieB85GgWcuX85uPAEKPOOZ5jIjBV4u9GwylgLPJkHaZBlJeZeUePt/Lj1hDKYM7uVIgef5eAG8i+d34tBOa43MPg/x0dNtkrYzlsEz8MTmXB1chaZI198pfk3eDS08WmxTFlZPPkL7wE8+xu6dnY/QVvYuwLdu4SO0FkaQc6lwJIpXFy7iGXiHIUmBl/MWS5ugXnuE9vVNrQjHtdfDax8+QmssXwo8Y3lyNvMJUOCZzzibChR42dCy6bUUeDYNVtFl8Q68/YKLx+F/7C64gi1dv1heidDFN6IOIax21yGu3aE4OlaKikS+oonbv20KPLEZr295D9G49tqy/T7Di2fC6wqIbczC6nyJhYWwWcpyAhR4xiPnSyyMZ7pvRgo889hyZnMIUOCZw1XvrBR4esnZaBwFno3CtMlS/rNsMxau28kz8JIC748/giu635vKNIFXd9E38S//ZsTQ+dNx7UXqODs6GKUJHmgu4x8BCjyxqTj9Drxs6fMlFtkS4/WiCVDgiU6A9bMhQIGXDS1eKwMBCjwZUujqgQJPrjyEdEOBJwQ7i+ok4LQz8DzL3ob3vZc6aSUfoZ1zAVaNGYrFnl1pBKfGKjA+3lMnVQ4zkwAFnpl0u5/b6WfgdU8o/QoKvGyJ8XrRBCjwRCfA+tkQoMDLhhavlYEABZ4MKVDgyZWC4G4o8AQHwPJZEXCawOvwdge8xGK7qwWve3ekcTsu1gfD4iVZseTF1hCgwLOGM6sYQ4ACzxiOnMU6AhR41rFmpdwJUODlzpAzWEuAAs9a3t1V4x143RFywO9T4DkgZBst0YkC71DxveOtwUZXY8dvDYgX40RN4Hm0R2n5kY8ABZ58mbCjwxOgwOPuUI0ABZ5qiTm7Xwo8Z+ev4uop8ORKjQJPrjyEdEOBJwQ7i+okQIHXBa41EUZMc3Yl4Nl3OreTJcMo8CzBzCIGEaDAMwgkp7GMAAWeZahZyAACFHgGQOQUlhKgwLMUd7fFKPC6RWT/Cyjw7J+xnVZIgWenNJ2xFgo8Z+Rsl1VS4NklSeesgwLPOVnbYaUUeHZI0VlroMCTK28KPLnyENINBZ4Q7CyqkwAFnk5wHCaMAAWeMPQsrIMABZ4OaBwilIDTBV7biqUIbl6PoukzEejdV2gWLN49AQq87hnxCrkIUODJl4eRHbkS2sfICbOdq7o2mO0Qx19Pgef4LaAUAAo8peJisxoBCjxuA5UIUOCplBZ7TRJwssCr+dMDqH/x2Y6N4PL50P/m21E09WhuDIkJUOBJHA5bOyQBCjy5NgbvwJMrDyHdUOAJwc6iOglQ4OkEx2HCCFDgCUPPwjoIUODpgMYhQgk4VeDFQyGsuXQesN+9E4WTp2PgrXcLzYPFj0yAAo87RDUCFHhyJUaBJ1ceQrqhwBOCnUV1EqDA0wmOw4QRoMAThp6FdRCgwNMBjUOEEnCswGtvx5rLzkoXeOMmYeDt9wjNg8Up8LgH7EWAAk+uPCnw5MpDSDcUeEKws6hOAhR4OsFxmDACFHjC0EtVOBoOY8lLr6B61WoMmjgBE047BR6vV6oek81Q4EkXCRvqhoBTBV4SS9ojtB4P+n//Tj5CK/mfGN6BJ3lAbO8gAhR4cm0KCjy58hDSDQWeEOwsqpMABZ5OcBwmjAAFnjD0UhV+5b7fYNWCN1M9TZ53OuZc/WWpeqTAky4ONpQBAdUFXjwUQ+tzKxD6uBqeigIUnjsO/mEVGay885J9L7Eonnos/H37ZzyOF4ohQIEnhjur6idAgaefnRkjKfDMoKrYnBR4igXm8HYp8By+AQQuvx0JNHtciGuvairQzhwqTrgy6oYCLyNMtr4oHo/jN5dciah2ZtW+T0FZGa798++kWzfvwJMuEjbUDQHVBV7Lc58i+Nq61CpdhQH0uP1UuP0eZm9DAhR4NgzV5kuiwJMrYAo8ufIQ0g0FnhDsLKqTAAWeTnAclhOBqObq9rjSX7Jeqgm8ggzeu06BlxN62wz+83XXo2FnTWo9lUMG47J7fi7d+ijwpIuEDdlc4NX/8k1Et9SnrbLsxhPhG9KD2duQAAWeDUO1+ZIo8OQKmAJPrjyEdEOBJwQ7i+okQIGnExyH5UQgqAm8hgMEXj5cKIt3Py0FXveMnHDFlmWf4KVf3Yv25mYUlpfjzJtuQP9xY6VbOgWedJGwIZsLvLZ/r0Lr/DWpVfIOPHtveQo8e+drx9VR4MmVKgWeXHkI6YYCTwh2FtVJgAJPJzgOy4kA78DLCR8Hf0Ygoj1CW7+jGj0G9IfX55OSCwWelLGwqSMQUP0R2n1n4IU/2gF3ZWHWZ+Bxc6hFgAJPrbzYLUCBJ9cuoMCTKw8h3VDgCcHOojoJUODpBMdhORPoOAPPrZ2Bp82Urz06W5LB47PJorwDL2f0nMBCAhR4FsJmKUMIqC7wDIHASZQhQIGnTFRs9DMCFHhybQUKPLnyENINBZ4Q7CyqkwAFnk5wHCaMAAWeMPQsrIMABZ4OaBwilAAFnlD8LJ4lAQq8LIHxcuEEKPCER5DWAAWeXHkI6YYCTwh2FtVJgAJPJzgOE0aAAk8YehbWQYACTwc0DhFKgAJPKH4Wz5IABV6WwHi5cAIUeMIjoMCTKwLx3VDgic+AHWROgAIvc1a5XJlIJLB088fY1ViDaUOPQmVJr1ymc/RYCjxHx6/c4inwlIvM8Q1T4Dl+CygFgAJPqbjYrEaAAk+ubcA78OTKQ0g3FHhCsLOoTgIUeDrBZTnsvpd+iffWvt0xKs+fj/895/8wuq98b8zMcllCLqfAE4KdRXUSoMDTCY7DhBGgwBOGnoV1EKDA0wEtwyGtsRje1970visaRi+fH8cUFaPQ48lwNC87HAEKPLn2BgWeXHkI6YYCTwh2FtVJgAJPJ7gshu2o244bH/tG2oiZI0/ADWd+N4tZeOk+AhR43AsqEaDAUykt9pokQIHHfaASAQo889J6taEeNeFwqkCVJvFOLS83r6BDZqbAkytoCjy58hDSDQWeEOwsqpMABZ5OcFkMo8DLAlYGl1LgZQCJl0hDgAJPmijYSIYEKPAyBMXLpCBAgWdeDE/s3Y1IPJEq4NL+10WVveBzJf8XP3oJUODpJWfOOAo8c7gqNSsFnlJxOb5ZCjxrtsCvX/4V3l3zVkexgC8Pt55/B0b0HmlNcZtVocCzWaA2Xw4Fns0DtuHyKPBsGKqNl0SBZ164vAPPHLYUeOZw1TsrBZ5ecjYaR4FnozAdsBQKPGtC5kssjOMVwCL6AAAgAElEQVRMgWccS85kPgEKPPMZs4KxBCjwjOXJ2cwlQIFnHt/kGXiLW5qxO8Iz8IykTIFnJM3c56LAy52h8jNQ4CkfoaMWQIHnqLhtsVgKPFvE6JhFUOA5JmrbLJQCzzZROmIhFHiOiNlWi6TAkytOCjy58hDSDQWeEOwsqpOAEQIvof1kLl5fA3fPvnB5vDo74TASyIwABV5mnHiVHAQo8OTIgV1kToACL3NWvFI8AQo88Rmwg+wIUOBlx8vsqynwzCaswPwUeAqExBZTBHIVeLFNn6L95UeQCLbAVVSKwFnXwNtvGAmTgGkEKPBMQ8uJTSBAgWcCVE5pKgEKPFPxcnKDCVDgGQyU05lOgALPdMRZFaDAywqXPS+mwLNnrnZdVa4Cr/X3tyDRVJvC46kahPzLvm9XXFyXBAQo8CQIgS1kTIACL2NUvFASAhR4kgTBNjIiQIGXESZeJBEBCjyJwtBaocCTKw8h3VDgCcHOojoJ5CLwEq2NaH3oAFmnPUJb9O37dXbDYQcSiEcbEA1vh8fbAx5/XwLSCFDgcRuoRIACT6W02GuSAAUe94FKBCjwVEqLvSYJUODJtQ8o8OTKQ0g3FHhCsLOoTgK5CLxkyfaXH0V05aJUdd/UOQjMuVBnNxy2P4FoaCvaG/6r/VK845f9hRPhLzrK8ZAo8By/BZQCQIGnVFxslgKPe0AxAhR4igXGdinwJNsDFHiSBSKiHQo8EdRZUy+BXAVeIhpBZMkCxKrXwztgNHyTTgQ8Hr3tcNx+BNrqnkc8sme/X3GhsNcVcLmc/aIQCjz+MVGJAAWeSmmx1yQB3oHHfaASAQo8ldJir0kCvANPrn1AgSdXHkK6ocATgp1FdRLIVeA1J+rgTmhiyV2uswMOOxwBCrxDk6HA458ZlQhQ4KmUFnulwOMeUI0ABZ5qibFfCjy59gAFnlx5COmGAk8IdhbVSUCvwIslongn/Fdsi63oqDzEOw3H+S/W2QWHHYoAH6EVL/Ca61rR3hpG5QAKav4p1UeAAk8fN44SR4B34Iljz8rZE6DAy54ZR4glQIEnlv+B1Snw5MpDSDcUeEKws6hOAnoF3sboB3gv/M+0qnMCX0Y/zxidnXDYoQjEo43aSyy28SUW+8Gx6g6853/7Fha/2Cmoh03uj0tvOQOBfB83KglkRYACLytcvFgCAhR4EoTAFjImQIGXMSpeKAkBCjxJgvisDQo8ufIQ0g0FnhDsLKqTgF6Btzj8NNZFu15ekSw/1jsHU/1n6uyEw0ggMwJWCLzNn1Tj99//V1pDZ117AmacPSGzJnkVCXxGgAKPW0E1AhR4qiXm7H4p8Jydv4qrp8CTKzUKPLnyENINBZ4Q7Cyqk4BegdcU34MX2n+FBGIdlT0uH84OfI9n4enMgcMyJ2CFwHvznx/jP4+mC+qJJ47ARf9zSuaN8koS0AhQ4HEbqEaAAk+1xJzdLwWes/NXcfUUeHKlRoEnVx5CuqHAE4KdRXUS0CvwkuX2xrdhTfRd7SUWboz2nYBydx+dXXAYCWROwGyBF08AH1Q345nnViC+ox6+5Vu0PQ58/d4L0G94ZeaN8koSoMDjHlCQAAWegqE5uGUKPAeHr+jSKfDkCo4CT648hHRDgScEO4vqJJCLwNNZksNIICcCZgu8BTVt+KA2iGgkhpaGIEr2NuGi0T0xeHzfnPrmYGcS4B14zsxd5VVT4KmcnvN6p8BzXuaqr5gCT64EKfDkykNINxR4QrCzqE4CFHg6wXGYMAJmC7zfrmlAa7Tz0fDkx6X9c/2YHvC7k/+LHxLIjgAFXna8eLV4AhR44jNgB5kToMDLnBWvlIMABZ4cOezrggJPrjyEdEOBJwQ7i+okQIGnExyHCSNgtsD726Ym7GiLpNZX5HPj6yPLha2XhdUmQIGndn5O7J4Cz4mpq7tmCjx1s3Nq5xR4ciVPgSdXHkK6ocATgp1FdRKgwNMJjsOEETBb4O1qj+LprS1o1R6hDXjd+Fy/Igwp8glbLwurTYACT+38nNg9BZ4TU+9cs6s9DM/Gari1f8cG9EKsskx6GBR40kfEBg8gQIEn15agwJMrDyHdUOAJwc6iOglQ4OkEx2HCCJgt8JILS77IYo8m8soDHj46KyxpexSmwLNHjk5aBQWek9LuWmsiFkP+W8vgCoZSvxiaOhLx3j2lBkKBJ3U8bO4QBCjw5NoWFHhy5SGkGwo8IdgtK5r36U7kv78Z0L7BD04fhPaJah9sT4Fn2dZhIYMIWCHwDGqV05AAKPC4CVQjQIGnWmLG9OuubURg8cq0yWJ9KhCeMsKYAibNQoFnElhOaxoBCjzT0OqamAJPFzZ7DaLAs1ee+6/Gt6MBpU8tSVtg4/lTEOkv/yMGh0uFAs+++9WuK6PAs2uy9lwXBZ49c7Xzqijw7Jzu4dfmamtH3oL0r3GjQ/oiMmaQ1EAo8KSOh80dggAFnlzbggJPrjyEdEOBJwS7JUXzP9iCwvc2ptVqmzYQbccPs6S+GUUo8MygyjnNJECBZyZdzm00AQo8o4lyPrMJUOCZTVje+b3rt8Ov/ZPQzpGIlxYhdPQYwOeVt2GtMwo8qeNhcxR40u8BCjzpIzK/QQo88xmLquDbXo/Sp5emlW88bzIiA9R9QyUFnqjdxLp6CVDg6SXHcSIIUOCJoH5wzUgihDrXVvRIDITPFZCjKUm7oMCTNBir2gpH4ApFkCgq0N5qYVVR/XUo8PSz40gxBHgHnhjuh6tKgSdXHkK6ocATgt2yovufgZe8+y40qZ9ltc0oRIFnBlXOaSYBCjwz6XJuowlQ4BlNNPv5tmE5nvXejiAakY9SnBv9PwzAxOwncsgICjyHBG2TZVLg2SRIBy2DAk+usCnw5MpDSDcUeEKws6hOAhR4OsFxmDACFHjC0LOwDgIUeDqgGTzkD96rUYstqVl7YhCujv7B4Cr2mY4Czz5ZOmElFHhOSNlea6TAkytPCjy58hDSDQWeEOwsqpMABZ5OcFYMS8Th2/kiPHWLEC8ejXC/CwBvvhWVpa5BgSd1PGzuAAIUeOK3xC+9ZyKGSKoRD3z4bvQl8Y1J2gEFnqTBsK1DEqDA48ZQjQAFnlyJUeDJlYeQbijwhGBnUZ0EKPB0grNgmH/znxHY/GiqUrR8OoKTfmlBZblLUODJnQ+7SydAgSd+R7zivgfL3C+nGpkUPwOnx28U35ikHVDgSRoM26LA4x6wBQEKPLlipMCTKw8h3VDgCcHOojoJUODpBGfBsKL3r4CrbWtXJZcLzcdr34R68iyoLm8JCjx5s2FnBxOgwBO/K2KI4kP3M9juWoH+ifGYHj8PHsj9Zk2R1CjwRNJn7WwJ8A68bInxetEEKPBEJ5BenwJPrjyEdEOBJwQ7i+okQIGnE5wFw/KX/w+8dYtTlRLa47Mtx72ovRXObUF1eUtQ4MmbDTujwOMeUJ8ABZ76GTppBRR4TkrbHmulwJMrRwo8ufIQ0g0FnhDsLKqTAAWeTnAWDHM1r0fBpz+Eu30X4CtEcNTNiP7/9s4ETq6qzNtvLb2msy+EsEuAgCAEYQCVBFEQAUEQ+UAEBSeODKAoiFFwWATkQ1CDIuCCLMoER4aIMIAiAmERhsWwKBAgBLLva2/VVTW3KqaT6sT0vafuve859z71+2Vgwjnnfc/zP2k7T99lxMQYKttdAoFndz50V0uAK/A4Ea4RQOC5lli6+0XgpTt/F3ePwLMrNQSeXXmodIPAU8FOUUMCCDxDcHFNKxclu+ZNKbVun/pbZ9cjR+DFdfioEwYBBF4YFFkjTgIIvDhpU6teAgi8egkyP24CCLy4iW+5HgLPrjxUukHgqWCnqCEBBJ4hOKapEUDgqaGnsAEBBJ4BNKaoEkDgqeKneEACCLyAwBiuTgCBpx5BTQMIPLvyUOkGgaeCnaKGBBB4huCYViWwcvZfZPGLd0m5XJJhux4mw8cdETkZBF7kiCkQIgEEXogwWSoWAgi8WDBTJCQCCLyQQLJMbAQQeLGh9lUIgecLU7IHIfCSnW/SdofAS1qi8e2nc9nbMuvBS2oKbjvhXBm4zT6RNoHAixQvi4dMAIEXMlCWi5wAAi9yxBQIkQACL0SYLBULAQReLJh9F0Hg+UaV3IEIvORmm8SdIfCSmGo8e1r6t3tl0Yzf1hQbtvuRstU+J0baAAIvUrwsHjIBBF7IQFkucgIIvMgRUyBEAgi8EGGyVCwEEHixYPZdBIHnG1VyByLwkpttEneGwEtiqvHsqWPZLHn7wUtrinEFXjzsqeIOAQSeO1nR6ToCCDxOgksEEHgupUWvFQIIPLvOAQLPrjxUukHgqWCnqCEBBJ4hOKZVCax/Bp6UijJs3Mdl2G6HRU6GK/AiR0yBEAkg8EKEyVKxEEDgxYKZIiERQOCFBJJlYiOAwIsNta9CVgu8ZStWy+QrbpIFi5fLPbdc0buhd+ctkm9ffbO89sY7Mmb0CLnwK6fKvnvtUv3v85Z2+No4gzYQQOBxGmwiUJayLMw+L525JbJVYT9pkeE17SHwbEqLXvwQQOD5ocQYWwgg8GxJgj78EkDg+SXFOBsIIPBsSIEeghBA4AWhFf1YawXe2vZOOfnMy2TiQfvIo3+ZUSPwPveV78qhH9pXPnv8YfLks694Mu8X8sc7r5WGfA6BZ3BmEHgG0JgSGYHnmq+TebmnquvnpVkO7LhQhpbG9tZD4EWGnoUjIoDAiwgsy0ZCAIEXCVYWjZAAAi9CuCwdOgEEXuhIWTBiAgi8iAEHXN5agdfe0SlLlq2s/rrk2lt7Bd7S5avkiM9cIE/de73kc7nqdk+YdLFc8O8ny7+MH4fAC3gAKsMReAbQmBIJgdXZufJIy/k1a48pHiTv7/xyxAKv5MnC30sm84KUy+OkUD7e+/fGSPbIoukjgMBLX+Yu7xiB53J66ewdgZfO3F3dNQLP1eTS2zcCz67srRV46zE9/9LrNQLv+ZdmymXfv1Wm/fLyXpLnXfoTOWDfPeTETxyCwDM4Xwg8A2hMiYSAlsDLy88ln9nwdtJi+UNSkIsi2SOLpo8AAi99mbu8YwSey+mls3cEXjpzd3XXCDxXk0tv3wg8u7JXFXiVq+nmL1q6CZFtvOfaDR08sPr7fQXek8++LNf9/C6ZeuPFvfMu+v+/kF3fs62c9umPyer2gl2EHeimpSknhZ6y9BRLDnSboBYzCdpLiFuZnrta5smz1RUrt9AeUvwP7yl4G26hzWUy0tiQlY7uYmhVS90nipSXb7ReRrJN93j/f3NoNVgovQRam/LSVShKsVROL4SE7Lwwe6a0P/GAt5uMtB5ytDSM2TEhO9uwjcpfLrPe19m1nT1SKnNmExdwAjdUObPt3nnltCYw3ARuqSGXlWw2U/2+gA8E+iVgwRe2ga0NOJZ+g4pvQCWPMD+Zsvfxu+D0p1+UaQ88vsnwE4/5sBwwfvfq7/cVeC+8PFMuvuaWmmfife2S6+UD++0pJxw9UVYh8Pzi7x3X0ugJPE/e9RR9Rxe4BhM2QwDcmz0WlZdYzJPnpMN7icWY4v7S2uclFrlcRpo8gdfeGd43PuWec71bZ1/ZqJ/hkm2YGuuxzXh/YeaTTAKtzTnpLJS8F9/yh97lhHvmzpKlP/iGSLFn3TYam2TE5B9JbugIl7e1Se8DWioCT2RtR0XgJWprbCahBNq8M1sRzv7/BpJQEGwrEIEAf2UNtG5/gys/hK58je3s5sKJ/ljx3z0CFvz1YJAnjHAs9pzGSh5hfgIJPD+F+wq85StXy0dPPE8e/92PpaV53TOqPn7KBXLlNyfJ+D134RZaP1D7jOEWWgNoTFEjEMVLLLIyUxqz3lW95WWePhzsPQPv61KS/XztsWP6TOl8eKbkxgyWASfuK7nBLb7mMSg9BLiFNhlZdz9yt3Q98J81m2n65CRpPPCwZGzwH7vgFtpExZmKzXALbSpiTswmuYU2MVGmZiPcQmtX1Kq30PpB0VfgVeZ84WtXy/77jJNJpxwt9//56eottff/+mrJeZckz1va4WdZxmxEAIHHcXCJQBQCb93+C94PuWZ7Am9b79/93Trb/tDfZeVVf+jFl99puIy88TMi3tciPhBYTwCBl4yzUHj+Uen8zfU1m2n53GTJ775vMjaIwEtUjmnaDAIvTWm7v1cEnvsZpm0HCDy7ErdW4D00/Tk5/7IbvCtiyt7z2YrS0JCXnbYbLXfffLnMXbBEvvXdn8lrb74r240ZJZec93l57247Vski8IIfMARecGbM0CMQncALvqdlk6dJ17OzayaOvPlUyW8/LPhizEgsAQSeG9G+M7dTnn1xtTQ1ZeSD7x8sQwbX3qJQ7ilI539OkZ5XnqluKD9+grT8v7Pd2FyALrkCLwAshlpBIJ8X+eF9L8rMhStl3JihctKBY2Vom78fxFmxAZpIFQEEXqriTsRmEXh2xWitwDPFhMALTg6BF5wZM/QI2CTwVl73Z2m/58UNMLzn2I268wuSGzZADxCVrSOAwLMukk0aqsi72+5a0PsMrcrLnc783BgZ0JLbZGxp2UKRbE6yQ5L17Lv1G0Xg2X9e6bCWwPUPvSQzZi/xfui/7vcrEu9rR+4DJghYSQCBZ2UsNLUFAgg8u44HAs+uPFS6QeCpYKeoIQGbBF7PolWy/KJ7peetxd7lOFkZ/KUJ0vrJvQ13xrSkEkDg2Z/sg48uk2f+uqqm0eM/PlLeu2v6ZDwCz/7zSocbCHT3lORrv54ulX+uF3gtjXmZctrBYIKAlQQQeFbGQlMIPGfOAALPmaiiaxSBFx1bVg6fgE0Cr7K7sveaxp7ZyyQ3sk2ybU3hb5gVnSeAwLM/wqeeXykPTV9e0+jnPj1ath+TvtvwEHj2n1c6rCXAFXicCJcIIPBcSoteKwS4As+uc4DAsysPlW4QeCrYnSha9n6cvSz3mnTlVsqIwu7SWB6k3rdtAk8dCA1YTwCBZ31E0tVdkv+6d5HMerez2uwB4wfJ4RPS+SxLBJ7955UOawnkvGfgTeEZeBwLRwgg8BwJijZ7CSDw7DoMCDy78lDpBoGngt36omUpySutv5Ll+ZnVXvPem1n3WnOGtJW2Vu0dgaeKn+IGBNIu8JobS9LsXZxa9l5K1d6VlUIhY0AxnilLlhW8XrPSNmDTZ9/F04F+FQSefgZ0EIwAb6ENxovRugQQeLr8qR6cAAIvOLMoZyDwoqTryNoIPEeCirnNNdl58kKb9ybojT6jC/vLLh3HxNxJbTkEnip+ihsQSLPAa2woS1uL92yqjT4rVmelVLZX4hlEnKgpCLzo4ny9NFPmlOfKntk9ZFRmVHSFUrYyAi9lgTu+XQSe4wGmsH0Enl2hI/DsykOlGwSeCnbri7ZnF8tzbdfV9DmqMF526zhetXcEnip+ihsQSLPAG+DJuyZP4m38WduZ9W5ZReAZHKVYpiDwosH8Xz3/LY8UH6su7r3DWP614Qx5X3bPaIqlbFUEXsoCd3y7CDzHA0xh+wg8u0JH4NmVh0o3CDwV7E4Ufbn1Nm6hdSIpmrSZQJoFHlfg2XwyN98bAi/8zLqlIF/rusB7quwGmb1DZge5oPGr4RdL4YoIvBSG7vCWEXgOh5fS1hF4dgWPwLMrD5VuEHgq2J0oWnkO3rLc69KZXSkje6J/icWLL5fl6WdFhg4py8c+mpGBbZtepcMVeE4cHZrciECaBV4Fg0vPwOPgiiDwwj8FXeVuOa/7Gwi88NFWV0TgRQSWZSMhgMCLBCuLRkgAgRchXIOlEXgG0JI2BYGXtETd3M//Pl+WW27f8KysEcMzctHkrDR4b5fb+IPAczPfNHeddoGX5uxd3DsCL5rUuIV2y1xL5ZK81fWmLCoslOH5ETK2eRfJZfy9TAaBF82ZZdVoCCDwouHKqtERQOBFx9ZkZQSeCbWEzUHgJSxQR7dz4y9K8pJ3Bd7Gn/O+kpX37Fh7FR4Cz9GAU9y2H4FXKs2TnsI9HqVOyeWPkFxuXIqJsXVNAgi86OjzEot/zvbFtTNkZudrvQO2bdxODhh4kK8wEHi+MDHIEgIIPEuCoA3fBBB4vlHFMhCBFwtmu4sg8OzOJy3dTf1tSaY/sUHgZTxvd9m3szJsKAIvLWcgqfvsV+CVl0tX+zne7XVr/4EgI40tV0s2OzapSNiXxQQQeBaHk+DW7lv+e+ksdfTusHL13bFDj5dM5ZuBfj4IvP4I8d9tIoDAsykNevFDAIHnh1J8YxB48bG2thICz9poUtXYqlVlueFnZXlnTtm7+kjkk5/IyqETeQZeqg5BQjfbn8Ar9kyXQtf3a3afazhOGhpPSygRtmUzAQSezekkt7dHVj0sSwtLejfYnG2Ro4Z+wteGEXi+MDHIEgIIPEuCoA3fBBB4vlHFMhCBFwtmu4sg8OzOJ03dlb0L8OYtEBk8qCxtAzb/U3duoU3TiUjGXvsVeMUZUui8pGaz+cZJkm84MhkA2IVTBBB4TsWVmGaX9SyVp9Y8KZ3FDmnMNsm/DDhAtmoc7Wt/CDxfmBhkCQEEniVB0IZvAgg836hiGYjAiwWz3UUQeHbnQ3e1BBB4nAjXCPQn8Cr7KXT/VIqF+6tby+T2lsamC71bxxpc2yr9JoAAAi8BITq6haL3IovVxZXSlh0o+WyfN1htYU8IPEcDT2nbCLyUBu/wthF4doWHwLMrD5VuEHgq2ClqSACBZwiOaWoE/Ai8anPes/DK5U7JZLdW65XCEEDgcQZcI4DAcy2xdPeLwEt3/i7uHoFnV2oIPLvyUOkGgaeCnaKGBBB4huCYpkbAt8BT65DCENhAAIHHaXCNAALPtcTS3S8CL935u7h7BJ5dqSHw7MpDpRsEngp2ihoSQOAZgmOaGgEEnhp6ChsQQOAZQGOKKgEEnip+igckgMALCIzh6gQQeOoR1DSAwLMrD5VuEHgq2ClqSACBZwiOaWoEEHhq6ClsQACBZwCNKaoEEHiq+CkekAACLyAwhqsTQOCpR4DAsysC/W4QePoZ0IF/Agg8/6wYaQcBBJ4dOdCFPwIIPH+ctEetWdElr7+wUErljOy0xzAZPnqAdktq9RF4augpbEAAgWcAjSmqBBB4qvg3Kc4VeHblodINAk8FO0UNCSDwDMExTY0AAk8NPYUNCCDwDKDFPKV9dZc8/JuZ3turi9XKmVxGJh43VoaObI25EzvKIfDsyIEu/BFA4PnjxCh7CCDw7Mmi0gkCz648VLpB4Klgp6ghAQSeITimqRFA4Kmhp7ABAQSeAbSYp7z1ylKZ8dicmqq77DNK9jwonW+wRuDFfAApVxcBBF5d+JisQACBpwB9CyUReHblodINAk8FO0UNCSDwDMExTY0AAk8NPYUNCCDwDKDFPGXhu2vkyXvfrKm6z8RtvVtph8fciR3lEHh25EAX/ggg8PxxYpQ9BBB49mRR6QSBZ1ceKt0g8FSwU9SQAALPEBzT1Agg8NTQU9iAAALPAJrClBnT58pbLy+pVt56p8Gy/2E7SM67lTaNHwReGlN3d88IPHezS2vnCDy7kkfg2ZWHSjcIPBXsFDUkgMAzBMc0NQIIPDX0FDYggMAzgKY0pfIsvFJJpG1wk1IHdpRF4NmRA134I4DA88eJUfYQQODZk0WlEwSeXXmodIPAU8FOUUMCCDxDcExTI4DAU0NPYQMCCDwDaExRJYDAU8VP8YAEEHgBgTFcnQACTz2CmgYQeHblodINAk8FO0UNCSDwDMExTY0AAk8NPYUNCCDwDKAxRZUAAk8VP8UDEkDgBQTGcHUCCDz1CBB4dkWg3w0CTz8DOvBPAIHnnxUj7SCAwLMjB7rwRwCB548To+whgMCzJws66Z8AAq9/RoywiwACz748wuwoU/Y+YS4YdK15SzuCTkn9eARe6o+AUwAQeNHG1b1YZPFUkY43RFrHiYw6pSz5Qel8MHpYpBF4YZFknTgIIPDioEyNMAkg8MKkyVpRE0DgRU2Y9cMmgMALm2h963ELbX38EjEbgZeIGFOzCQRetFHPvlKka9aGGgP2EdnmrGhrJn11BF7SE07W/hB4ycozDbtB4KUh5eTsEYGXnCzTshMEnl1JI/DsykOlGwSeCnaKGhJA4BmC8zGt1C3yxtnewI2uo862ioyd4mMyQ/4pAQQeh8MlAgg8l9Ki1woBBB7nwCUCCDyX0qLXCgEEnl3nAIFnVx4q3SDwVLBT1JAAAs8QnM9pXIHnE1SAYQi8ALAYqk4AgaceAQ0EJIDACwiM4aoEEHiq+CluQACBZwAtwikIvAjhurI0As+VpOizQgCBF+052OQZeJ/xnoE3mGfg1UMdgVcPPebGTQCBFzdx6tVLAIFXL0Hmx0kAgRcnbWqFQQCBFwbF8NZA4IXH0tmVEHjORpfKxhF4qYzd6U0j8JyOL3XNI/DSFXlnZ7s8/syfZP7Cd+W9u42X8XsdKJmMWz+0QeCl68y6vlsEnusJpq9/BJ5dmSPw7MpDpRsEngp2ihoSQOAZgmOaGgEEnhp6ChsQQOAZQHN4ys9+da28Pfv13h0cdsixcsgHj3RqRwg8p+JKfbMIvNQfAecAIPDsigyBZ1ceKt0g8FSwU9SQAALPEBzT1Agg8NTQU9iAAALPAJqjU1avWSlXTbmgpvttx+wkZ54+2akdIfCciiv1zSLwUn8EnAOAwLMrMgSeXXmodIPAU8FOUUMCCDxDcD6nFVcVZc3D7dI9uyAN2+al7SMDJD8k53M2wzZHAIHHuXCJAALPpbTq67VUKsl3rj1Xuru7ehfadec95XMnnVPfwjHPRuDFDJxydRFA4NWFj8kKBBB4CtC3UBKBZ1ceKt0g8FSwU9SQAALPEJzPaSvuWiXdbxZ6Rzds2yBDPzPI52yGIfA4A64TQOC5nmCw/l946S/yu/t/LYVCtwwbOlJO/fRZMmrk1sEWUR6NwFMOgPKBCCDwAuFisAUEEHgWhLBRCwg8ux33JBEAACAASURBVPJQ6QaBp4KdooYEEHiG4HxOWzxlmZS7yhtGe88yH3HuMMk2uPVQc5/bjWUYV+DFgpkiIRFA4IUE0qFluro6ZdqcGfLCwLK05RrlmAE7yvuahzmzAwSeM1HRqEcAgccxcI0AAs+uxBB4duWh0g0CTwU7RQ0JIPAMwfmcxhV4PkEFGIbACwCLoeoEEHjqEcTewDMdi+T2VTN76+YkI98cPl62yrfE3otJQQSeCTXmaBFA4GmRp64pAQSeKblo5iHwouHq1KoIPKfiSn2zCLxojwDPwAufLwIvfKasGB0BBF50bG1deerqN+WJ9gU17Z04aGc5uGW0rS3X9IXAcyImmvwHAQQeR8E1Agg8uxJD4NmVh0o3CDwV7BQ1JIDAMwQXYFqpKLL0tay0L85K2+iyDNu1KBnuoA1AsHYoAs8YHRMVCCDwFKArl+QKPOUAKJ8qAgi8VMWdiM0i8OyKEYFnVx4q3SDwVLBT1JAAAs8QXIBpc55okBVvbzB2w3crytb7eVaPjxEBBJ4RNiYpEUiEwCuUpNxTlkwLb9D2e4z+Z8078njnQmnN5OSoth1kfNNwv1PVx3EFnnoENBCAAAIvACyGWkEAgWdFDL1NIPDsykOlGwSeCnaKGhJA4BmC8zmt1CPy6l2NUvnn+k/lMUjjju/2uQLD+hJA4HEmIiFQLErj/LmS9Rbv2mprKTc0hFLGdYFXmr1WSgs617EY1CC5XQdKJsclxKEcDksXQeBZGgxtbZYAAo+D4RoBBJ5diSHw7MpDpRsEngp2ihoSQOAZggsw7bVpjVJYu2FCy/Cy7HxEIcAKDN2YAAKP8xA6gZ6iDHnqUcmvWlVdutjSKis/cLCUmut/6YDLAq+0uiClv61jsv6T2a5VcmPq5xJ6hiwYGgEEXmgoWSgGAgi8GCBTIlQCCLxQcda9GAKvboTuL4DAcz/DNO0AgRd92mvmZeXdJ/NS7BKpXH23/YRuaR0Rfd2kVkDgJTVZvX01zZsjA194tqaBtbvtIR1jd627KacF3rwOKb3bXsMgO7xJsmPb6ubCAvYSQODZmw2dbUoAgcepcI0AAs+uxBB4duWh0g0CTwU7RQ0JIPAMwQWcVvRuoe32LmRpGiyS5TFSAenVDkfg1YWPyZshgMDb/LEoe8++K7240nv+Xal3QG73QZLxbqXlk1wCCLzkZpvEnSHwkphqsveEwLMrXwSeXXmodIPAU8FOUUMCCDxDcExTI4DAU0Of3MI9PTLkycckv3qjW2g/OEFKTc1179nlK/Aqmy93FL1n4HVIxnvvTmZUE/Ku7hNh/wIIPPszosMNBBB4nAbXCCDw7EoMgWdXHirdIPBUsFPUkAACzxAc09QIIPDU0Ce7MC+xSHa+7M43AQSeb1QMtIAAAs+CEGghEAEEXiBckQ9G4EWO2P4CCDz7M6LDDQQQeJwG1wgg8FxLLN39un4FXrrTS+fuEXjpzN3VXSPwXE0uvX0j8OzKHoFnVx4q3SDwVLBT1JAAAs8QHNPUCCDw1NBT2IDA5gRep/RIKZOR1jIPxDRAypSICSDwIgbM8qESQOCFipPFYiCAwIsBcoASCLwAsJI6FIGX1GSTuS8EXjJzTfKuEHhJTjd5e+sr8F7Nr5a52Y7qRkeUm2SvwiDJSiZ5G2dHzhJA4DkbXSobR+ClMnanN43Asys+BJ5deah0g8BTwU5RQwIIPENwTFMjgMBTQ09hAwIbC7yF5S75a35FzSrjegbKNqUWg5WZAoFoCCDwouHKqtEQQOBFw5VVoyOAwIuOrcnKCDwTagmbg8BLWKAJ3w4CL+EBJ3B7CLwEhprgLW0s8N6QNfJmbm3NbivyriLx+EDAFgIIPFuSoA8/BBB4figxxiYCCDyb0hBB4NmVh0o3CDwV7BQ1JIDAMwTHNDUCSRR4pZ6CdC94RxpHbSvZxiY1thQOn8DGAm9NsUeeaVwuBSlVC1VunN2/MFQGlhvCL8yKEDAkgMAzBMc0FQIIPBXsFK2DAAKvDngRTEXgRQDVtSUReK4llu5+EXjpzt/F3SdN4LW/8bLMv+V70rNymeQGDJIxZ3xDWnd9n4vR0PNmCPR9Bl679wKLd/Id3v8ty7bFZhlSboQbBKwigMCzKg6a6YcAAo8j4hoBBJ5diSHw7MpDpRsEngp2ihoSQOAZgmOaGoGkCbxZV54l3fPf6eXZuPX2stO3rlfjS+FwCWzuLbThVmC1NBDoecZ7d/E93u3XnSK5Q5ql4egBkW0bgRcZWhaOgAACLwKoLBkpAQRepHgDL47AC4wseRMQeMnLNMk7QuAlOd1k7i1pAu/1rx4vZe8W2t5PJiO7fO83km1qTmaAKdsVAi9lgUew3dLcHun6j2U1KzeeOVhy+0Vzuz0CL4IQWTIyAgi8yNCycEQEEHgRgTVcFoFnCC5J0xB4SUoz+XtB4CU/46TtMGkCb8HUH8vKJx7sjWnwQYfJ6M98OWmxObGfnlJJ/jDrb/Ly0vkybthWcuR79pR8NltX7wi8uvAx2SPQ80iHFG5fXcMid0iLNJ4azctPEHgcO5cIIPBcSoteKwQQeHadAwSeXXmodIPAU8FOUUMCCDxDcExTI5A0gVfu6ZEVT9wva1+fIS3v2UOGTjhasg3+n4tWuXqv3OHdWpfLS7a1TS2XJBT+6YzH5U9vv9a7lUN32FX+bZ+D69oaAq8ufFZOLpSLck/heXmm5y0ZlhkgJzTuLzvntoqs181egXe2dwXeeK7Aiww6CztDAIHnTFQ0+g8CCDy7jgICz648VLpB4Klgp6ghAQSeITimqRFImsCrB2Spq0OKi+aKeFeOVT7ZAQMlN2LrepZM9dwvPvifsrKzvZdBoydFbz3qNMl6tzWbfhB4puTsnfdA9wy5t/DX3ga9a+Hk0tbjpTUTjVCrFNr4GXj5Q5slfyTPwLP3hNBZnAQQeHHSplYYBBB4YVAMbw0EXngsnV0JgedsdKlsHIGXytid3jQCb0N8xSXzpbS29ta6/JgdJRPgCj6nD0PIzX/t4btk7uoVvatuM3CIfP/QT9VVBYFXFz4rJ0/pfEBmFhfW9HZm00flvfltrOw3aFPcQhuUGOM1CSDwNOlT24QAAs+EWnRzEHjRsXVmZQSeM1HRqEcAgccxcI0AAg+BF9WZ/bsnRK/534dlTXentDU2y/n7Hyq713lFIwIvqrT01u17BV5zplEua4n2Crw4d4vAi5M2teolgMCrlyDz4yaAwIub+JbrIfDsykOlGwSeCnaKGhJA4BmCY5oaAQTeBvTcQhv+MSyUijJn9XIZM2CINOXzdRdA4NWN0LoFKs/Am1Z4Tp7tmRXLM/DiBoDAi5s49eohgMCrhx5zNQgg8DSo//OaCDy78lDpBoGngp2ihgQQeIbgmKZGAIFXi56XWKgdRV+Fgwq8BSuKsqazJKOH5qWtyfzZe76aYxAENkMAgcexcIkAAs+ltOi1QgCBZ9c5QODZlYdKNwg8FewUNSSAwDMExzQ1Agg8NfQUNiAQROA9P6tL3l7UU62SzYp8cLdmGTkoZ1CVKRAwJ4DAM2fHzPgJIPDiZ07F+ggg8OrjF/ZsBF7YRB1cD4HnYGgpbhmBl+LwHd06As/R4FLatl+B11koy/88v+ENuBVcowbn5EPjmoOTK4sUl3RJpi0n2Zb6bwMO3gAzXCaAwHM5vfT1jsBLX+au7xiBZ1eCCDy78lDpBoGngp2ihgQQeIbgmKZGAIGnhp7CBgTiFnjlTu8W3N/Mk+K8zmq3zROGS/MHhhl0zpS0EkDgpTV5N/eNwHMztzR3jcCzK32rBd6yFatl8hU3yYLFy+WeW67oJXfSmZfJqzNni2TWPWtlUFurPHb3ddV/n7e0wy7CDnSDwHMgJFrsJYDA4zC4RgCB51pi6e7Xr8CrUArjFtrOx5ZK55PLaqAP/OIOkhvWmO4g2L1vAgg836gYaAEBBJ4FIdBCIAIIvEC4Ih9srcBb294pJ3uibuJB+8ijf5lRI/COOnWyTLnsHBm70zabAELgBT8zCLzgzJihRwCBp8eeymYEEHhm3JilQyCIwKt02PsSiyE5aWv2HoQX8LPmjjnS807tD18HHDNaGvYYGHAlhqeVAAIvrcm7uW8Enpu5pblrBJ5d6Vsr8No7OmXJspXVX5dce2uNwJt4/FfkzpsultEjN73FAoEX/IAh8IIzY4YeAQSeHnsqmxFA4JlxY5YOgaACr94uC6+vlbX/Pa93mezgBhl4xvaSaQouA+vthfnmBLzHGEpnqSzFkkiL9x6T3D/ukjFf0f9MBJ5/VozUJ4DA08+ADoIRQOAF4xX1aGsF3vqNP//S65sIvPGHT5IJB7xPXnh5powYNljOnXSCTDhw7+oUBF7wI4PAC86MGXoEEHh67KlsRgCBZ8aNWToE4hZ4lV32vNkuXS+tlGxbXpoOHFr9Jx93CJQ9e7ekuyTdnsCrfCoPuBnVnJP8uifdRP5B4EWOmAIhEkDghQiTpWIhgMCLBbPvIqoCb+nyVTJ/0dJNmt1m9AgZOnjdrRN9BV7J++bg21f/Qo748AFy0H57yKNPzpDJV94kv7/tu9Ur8pav6fa9eQauI9DWnJeunpIUvF98IGA7gXw2Ky3elRmrO3psb5X+IFAlMLC1Qdq9B/UXS3yN5UjYT2CQd15z2Yysai94Z3adkOEDgS0R6Ogpy/z22v9NHtyYkeHe95dxfAYPaJRVa7uF0xoHbWrUS6CpIVf9Gtvexfex9bJkfjwEhrY14ljiQe2rSiWPMD+Zsvfxu+D0p1+UaQ88vsnwE4/5sBwwfvfq72/uCry+E07/6lXyqaMmytEfPUg6uop+yzPuHwQaG7JSLFZue/AdHewgoEbA83eSz2Wlu4AMUQuBwoEINHlfYwvefWX4u0DYGKxEoLkxV31HWGd3Ufx/R6fULGWtILDGOyvvrqmVEcO95yGO8mRwHJ/mxqx0ed8TcF7joE2Negnkc5nq19iCJ775QMAFAi1NORyLRUFV8gjzE0jg+SncV+C1d3TJzFlzZO89du6d/tmzr5BTTzhcPnbI/txC6wdqnzHcQmsAjSlqBLiFVg09hQ0JcAutITimqRDQuIVWZaMUDY1ARUNUb6H1fhhc+XALbWhoWSiBBLiFNoGhJnxL3EJrV8Cqt9D6QdFX4K1YuUYOO+l8mfKds+UD++0plav4vv6dG+W+26+S4UMHIfD8QEXgGVBiii0EEHi2JEEffgkg8PySYpwNBBB4NqTgXg+8xMK9zOhYhwACT4c7Vc0JIPDM2UUx01qB99D05+T8y26QyvXwhZ6iNDTkZaftRsvdN18ujz41Q665YaosWrpCKs/Lu+Csk+XAffeo8uElFsGPCVfgBWfGDD0CCDw99lQ2I4DAM+PGrGgIFLx7uVcWCjK0sXGzbwpF4EXDnVWjI8BLLKJjy8rhE0Dghc+UFaMlgMCLlm/Q1a0VeEE3sn48Ai84OQRecGbM0COAwNNjT2UzAgg8M27MCp/AnLUd8ueFS6SrWJTWfF4O9X4IOrqluaYQAi987qwYLQEEXrR8WT1cAgi8cHmyWvQEEHjRMw5SAYEXhFZCxyLwEhpsQreFwEtosAneFgIvweE6trWpb8+RNYUNLxsY2dwkx263NQLPsRxpt5YAAo8T4RIBBJ5LadFrhQACz65zgMCzKw+VbhB4KtgpakgAgWcIjmlqBBB4augpvBGBDu9xJL+e9W4Nk6z3KsQzxu6AwOOkOE0Aged0fKlrHoGXusid3zACz64IEXh25aHSDQJPBTtFDQkg8AzBMU2NAAJPDT2F+xB4xLt99o1Va3p/971DBslBI4ch8DgpThNA4DkdX+qaR+ClLnLnN4zAsytCBJ5deah0g8BTwU5RQwIIPENwTFMjgMBTQ0/hPgR6SmV5ZeUqWdjZJdt4z74bN3jgJi+y4Bl4HBvXCCDwXEss3f0i8NKdv4u7R+DZlRoCz648VLpB4Klgp6ghAQSeITimqRFA4Kmhp7ABAQSeATSmqBJA4Knip3hAAgi8gMAYrk4AgaceQU0DCDy78lDpBoGngp2ihgQQeIbgmKZGAIGnhp7CBgQQeAbQmKJKAIGnip/iAQkg8AICY7g6AQSeegQIPLsi0O8GgaefAR34J4DA88+KkXYQQODZkQNd+COAwPPHiVH2EEDg2ZMFnfRPAIHXPyNG2EUAgWdfHmF2lCl7nzAXDLrWvKUdQaekfjwCL/VHwCkACDyn4qJZjwACj2PgEgEEnktp0WuFAAKPc+ASAQSeS2nRa4UAAs+uc8AttHblodINAk8FO0UNCSDwDMExTY0AAk8NPYUNCCDwDKAxRZUAAk8VP8UDEkDgBQTGcHUCCDz1CGoaQODZlYdKNwg8FewUNSSAwDMExzQ1Agg8NfQUNiCAwDOAxhRVAgg8VfwUD0gAgRcQGMPVCSDw1CNA4NkVgX43CDz9DOjAPwEEnn9WjLSDAALPjhzowh8BBJ4/ToyyhwACz54s6KR/Agi8/hkxwi4CCDz78gizI56BFybNmNZC4MUEmjKhEEDghYKRRWIkgMCLETal6iaAwKsbIQvETACBFzNwytVFAIFXFz4mKxBA4ClA30JJbqG1Kw+VbhB4KtgpakgAgWcIjmlqBBB4augpbEAAgWcAjSmqBBB4qvgpHpAAAi8gMIarE0DgqUdQ0wACz648VLpB4Klgp6ghAQSeITimqRFA4Kmhp7ABAQSeATSmqBJA4Knip3hAAgi8gMAYrk4AgaceAQLPrgj0u0Hg6WdAB/4JIPD8s2KkHQQQeHbkQBf+CCDw/HFilD0EEHj2ZEEn/RNA4PXPiBF2EUDg2ZdHmB3xDLwwaca0FgIvJtCUCYUAAi8UjCwSIwEEXoywKVU3AQRe3QhZIGYCCLyYgVOuLgIIvLrwMVmBAAJPAfoWSnILrV15qHSDwFPBTlFDAgg8Q3BMUyOAwFNDT2EDAgg8A2hMUSWAwFPFT/GABBB4AYExXJ0AAk89gpoGEHh25aHSDQJPBTtFDQkg8AzBMU2NAAJPDT2FDQgg8AygMUWVAAJPFT/FAxJA4AUExnB1Agg89QgQeHZFoN8NAk8/AzrwTwCB558VI+0ggMCzIwe68EcAgeePE6PsIYDAsycLOumfAAKvf0aMsIsAAs++PMLsiGfghUkzprUQeDGBpkwoBBB4oWBkkRgJIPBihE2pugkg8OpGyAIxE0DgxQyccnURQODVhY/JCgQQeArQt1CSW2jtykOlGwSeCnaKGhJA4BmCY5oaAQSeGnoKGxBA4BlAY4oqga2GNMviVZ1SKqm2QXEI+CKAwPOFiUEWEUDgWRSG1woCz648VLpB4Klgp6ghAQSeITimqRFA4Kmhp7ABAQSeATSmqBJA4Knip3hAAgi8gMAYrk4AgaceQU0DCDy78lDpBoGngp2ihgQQeIbgmKZGAIGnhp7CBgQQeAbQmKJKAIGnip/iAQkg8AICY7g6AQSeegQIPLsi0O8GgaefAR34J4DA88+KkXYQQODZkQNd+COAwPPHiVH2EEDg2ZMFnfRPAIHXPyNG2EUAgWdfHmF2xEsswqQZ01oIvJhAUyYUAgi8UDCySIwEEHgxwqZU3QQQeHUjZIGYCSDwYgZOuboIIPDqwsdkBQIIPAXoWyjJLbR25aHSDQJPBTtFDQkg8AzBMU2NAAJPDT2FDQgg8AygMUWVAAJPFT/FAxJA4AUExnB1Agg89QhqGkDg2ZWHSjcIPBXsFDUkgMAzBMc0NQIIPDX0FDYggMAzgMYUVQIIPFX8FA9IAIEXEBjD1Qkg8NQjQODZFYF+Nwg8/QzowD8BBJ5/Voy0gwACz44c6MIfAQSeP06MsocAAs+eLOikfwIIvP4ZMcIuAgg8+/IIsyOegRcmzZjWQuDFBJoyoRBA4IWCkUViJIDAixE2peomgMCrGyELxEwAgRczcMrVRQCBVxc+JisQQOApQN9CSW6htSsPlW4QeCrYKWpIAIFnCI5pagQQeGroKWxAAIFnAI0pqgQQeKr4KR6QAAIvIDCGqxNA4KlHUNMAAs+uPFS6QeCpYKeoIQEEniE4pqkRQOCpoaewAQEEngE0pqgSQOCp4qd4QAIIvIDAGK5OAIGnHkGyBZ5deOkGAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ2EVB/Bp5dOOgGAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ2EUDg2ZUH3UAAAhCAAAQgAAEIQAACEIAABCAAAQhAoIYAAo8DAQEIQAACEIAABCAAAQhAAAIQgAAEIAABiwkg8CwOZ3OtLVuxWiZfcZMsWLxc7rnlit4hJ515mbw6c7ZIJlP9vUFtrfLY3dc5tjvaTRqBnmJRfviz38ovp94vj//uRzJ08MDeLf78jvtk6rQ/SXehRz46YT/51pdPkXwulzQE7MdRAt3dBRl/+CRpaMj37uDQD46X719ylqM7ou0kEuDraBJTTe6e+F41udkmaWd/9/4+9dWLr5cP/ctectG5p/ZubfrTL8k1N06VxUtWyJ7jdpIrvzlJRgwbnKStsxcHCfwzN/D9m34jt/zmAclms727uvPGi2W3nbdzcJe0vDEBBJ5D52Fte6ec7Im6iQftI4/+ZUaNwDvq1Mky5bJzZOxO2zi0I1pNOoFzLpwi48ZuLzfefk9VKK8XeM+88KpcfM3NcvuPLpTWliY556Lr5CMfer985riPJB0J+3OEwJJlK+XY0y+UJ373Y0c6ps20EeDraNoSd3+/fK/qfoZJ38ELL8+Uy394e/XvUwMHtPYKvNVr2uWIUy6Q6688tyrvrv/lNHn73fnyg0vPTjoS9mcxgS25gUuvvUV2ec92/N3K4vxMW0PgmZJTmNfe0SmVv1RWfl1y7a01Am/i8V+RO2+6WEaPHKbQGSUhsHkCr77xTlXg7XXo6TUC7zs/uE1Gjxomk045ujrxz0++ILfc+YDcOuWboISAFQRmvTNfzpz8A3ngjqut6IcmINCXAF9HOROuEeB7VdcSS1+/78xdWL2q7rb/+kP171vrr8B78JFn5K77HpOffu/8KpSK0Dv4uC/LM/fdII2NDekDxY6tILAlN3D+ZTfIxAP3lk8c/gEreqWJ8Agg8MJjGdtKz7/0+iYCr3Kr14QD3ieVnxxV/ofn3EknyATvDy0fCNhAoK/A+8J5V8tJxx4qh3m3zlY+b3my5PRzr5JH/3uKDe3SAwTkxb+9Wb0y9D07bC0z35oru43dTr597mmy43ajoQMBKwjwddSKGGgiAAG+Vw0Ai6GqBG687Z4agXfT7b+XpctXeo97+WxvXxM8gXfbdd/i+wLVpCheIbA5N/BvF1wrpVJZ3p6zQCoP2Pr0Jw7pvXACam4TQOBZlt/S5atk/qKlm3S1zegRvbcf9v1DWvnD+e2rfyFHfPgAOWi/PeTRJ2fI5Ctvkt/f9l2uyLMs36S109nVLW+8PXeTbQ0Z1Cbbbj2y9/f7CrxTzrpcvnTaMXKwJ50rn/kLl8onz7hInvZ+kskHAnER2NL57fLO9u2//aOcduLHpPL19ye3TJNHn5oh0355eVztUQcCWyTA11EOiEsE+F7VpbTota/AqzzPuVgsyXlfOrEXzuEnnS/XXf7l6p0mfCCgSWBzAu+nv/q9DPSeif+pIyfIbO/K0orQ++Y5p/RePKHZL7XrI4DAq49f6LOnP/2iTHvg8U3WPfGYD8sB43ev/v7m/pD2nXD6V6+STx01UY7+6EGh98iCEFhPYN6CJXKt95DUvp/3v2+3mmcu9BV4/3r+9+TTRx8iHztk/+rUNz0JWPm9P//2h8CFQGwE/J7fSkOFnqLsf8QX5Q9Tr5VRI4bE1iOFIPDPCPB1lLPhOgG+V3U9weT231fgVWTIIu/lFRu/1OJDx54jd/zkItl+m62SC4KdOUHAjxv4ya2/k4WLl8ml55/uxJ5o8p8TQOA5eDr6/iFt7+iSmbPmyN577Ny7m8+efYWcesLhvYLEwW3ScoII9BV4V173K+9NyQPk7DOOq+7y3j8+JdMefFx+fs3XE7RrtuIygcVLV8iq1Wtl5x3XvRio8lba93sCb+OXsbi8P3p3nwBfR93PME074HvVNKXt/l77Crw/Pvasd1X+H6q3zFY+CxcvlyM/+w15+n9ukHwu5/6G2YHTBDYn8Cq/t+duO/U+o3HKz++qPrtxYwnt9KZT3DwCz8Hw+/4hXbFyjRzmXcY95Ttnywf221MqV/F9/Ts3yn23XyXDhw5ycIe0nDQCfQVe5Qxf4J3RX11/kQxoaZbKs5xO/uRH5LiPH5y0rbMfRwlUvo5W3uB1q/fNeuXlQNffcrc8+ewrMvWG/3B0R7SdNAJ8HU1aosneD9+rJjvfpO2ur8CrvO3z8JPPl2sv/nfZb+/d5Mrrfi0d3gUU3/3WpKRtnf04SGBzAu+kL11afR7+l047VubMXySf9541fun5Z3iPL9rLwR3S8sYEEHgOnYeHpj8nlTfKSLlcvZ2roSEvO3kPVL/75surz2a65oapssi7aqTyvKYLzjpZDtx3D4d2R6tJI1D5Zv2QE86tbqtQ6Kme18rnoTuvrb5o5ZdT75fb7/pD9ZkiR37kQPn6mSdJNlt5zCofCNhB4Ge/vlfuuPsh6fKuvqv8FPPi8z5f/frKBwK2EODrqC1J0IcfAnyv6ocSYzQJXPXjO2Tq7x72Hv5f8v66VZacd3Xdp4+eKBd+5VTvh3gvy1U/uqP6cot99hwrV06eJEMGt2m2S+2UE9iSG3j73QXeSy9vkVffeMe766m1emde5Rcf9wkg8NzPkB1AAAIQgAAEIAABCEAAAhCAAAQgAAEIJJgAAi/B4bI1CEAAAhCAAAQgAAEIQAACEIAABCAAAfcJIPDcz5AdQAACEIAABCAAAQhAAAIQgAAEIAABCCSYAAIvweGyNQhAAAIQgAAEIAABCEAAAhCAs6ckogAAAT5JREFUAAQgAAH3CSDw3M+QHUAAAhCAAAQgAAEIQAACEIAABCAAAQgkmAACL8HhsjUIQAACEIAABCAAAQhAAAIQgAAEIAAB9wkg8NzPkB1AAAIQgAAEIAABCEAAAhCAAAQgAAEIJJgAAi/B4bI1CEAAAhCAAAQgAAEIQAACEIAABCAAAfcJIPDcz5AdQAACEIAABCAAAQhAAAIQgAAEIAABCCSYAAIvweGyNQhAAAIQgAAEIAABCEAAAhCAAAQgAAH3CSDw3M+QHUAAAhCAAAQgAAEIQAACEIAABCAAAQgkmAACL8HhsjUIQAACEIAABCAAAQhAAAIQgAAEIAAB9wkg8NzPkB1AAAIQgAAEIAABCEAAAhCAAAQgAAEIJJgAAi/B4bI1CEAAAhCAAAQgAAEIQAACEIAABCAAAfcJ/B/MPAK1AUCKIwAAAABJRU5ErkJggg==", + "text/html": [ + "
" + ] + }, + "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}
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": 15, + "id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "text", + "marker": { + "color": [ + "#f8d349", + "#d6d07a", + "#a958c9", + "#7341ee", + "#268bba", + "#4862ce", + "#dd8cd7", + "#6a6c06", + "#8a29da", + "#0d2037", + "#805527", + "#e69670", + "#75b5e3", + "#796278", + "#6d4052", + "#1f6ab0", + "#99fe53", + "#3f0a72", + "#fe8e92", + "#c3e1f2", + "#f645e0", + "#b43417", + "#e0a8df", + "#7740be", + "#43c2e8", + "#64f999", + "#2cde7f", + "#29fa15", + "#580c96", + "#10384a", + "#845aa9", + "#7f03bd", + "#2b3af3", + "#335dcf", + "#22398f", + "#c932c1", + "#d43c00", + "#e6f378", + "#08808d", + "#6a0fce", + "#e1b5db", + "#75195e", + "#6ff3c5", + "#4099c1", + "#b25d7b", + "#d65c3a", + "#9b9d6e", + "#fc2b74", + "#571122", + "#422abb", + "#efed10", + "#dfc6c7", + "#02cada", + "#3ec815", + "#8e8cab", + "#df5d2e", + "#c457d7", + "#ec0a37", + "#da28db", + "#2d7f7d", + "#b27d2e", + "#d01b19", + "#fb9dce", + "#35303c", + "#4f86b8", + "#fbfef2", + "#ca3592", + "#c1e3c5", + "#c97596", + "#091a90", + "#b280bb", + "#7b4427", + "#b2140a", + "#dbde1c", + "#7ea8e9", + "#539908", + "#8069bc", + "#d01f72", + "#4ce72d", + "#73e76a", + "#20f2c3", + "#996ff1", + "#91f4db", + "#d70d97", + "#3678a7", + "#5af098", + "#ae5204", + "#badd6d", + "#a9541c", + "#d4b1ce", + "#51d0da", + "#ff2d6a", + "#1c2c7e", + "#ae7afe", + "#d156c8", + "#480c89", + "#e2a239", + "#39821f", + "#7bee34", + "#92b4fa", + "#b9fd23", + "#591ab9", + "#0bdacc", + "#2a2d25", + "#dc152c", + "#ac9648", + "#6ad041", + "#fe62a5", + "#52b6df", + "#4aaf9f", + "#d34482", + "#2fef1a", + "#7dd58b", + "#987252", + "#94a85d", + "#2b9f18", + "#ee26df", + "#c6016b", + "#9df332", + "#9b5e28", + "#2ebca4", + "#1b312a", + "#2e1afc", + "#574e28", + "#ac55ba", + "#f090af", + "#5cb9ca", + "#2dcfac", + "#804ce2", + "#ce865d", + "#3e5237", + "#482281", + "#2ae342", + "#6df6ca", + "#85fa26", + "#793548", + "#bbfe83", + "#15ae86", + "#70d1d9", + "#bb0ee6", + "#a95826", + "#8afd40", + "#505bd9", + "#0c777d", + "#ed694d", + "#4e797a", + "#dc95ec", + "#612b32", + "#ad8b14", + "#474ff9", + "#71c500", + "#bd53b1", + "#11a70e", + "#144ada", + "#72e048", + "#188ca3", + "#b52bf6", + "#b64eac", + "#f59c06", + "#b1c27d", + "#ac5faf", + "#5b3f83", + "#108c41", + "#b61e76", + "#22463b", + "#c959de", + "#a64739", + "#659222", + "#0f8781", + "#2c168d", + "#0faf59", + "#68bece", + "#696eaa", + "#af0f59", + "#a9e927", + "#601568", + "#9780cf", + "#e01073", + "#dd889c", + "#046e5c", + "#c6eff5", + "#b3dba5", + "#426575", + "#913568", + "#de30e4", + "#50f10d", + "#9a5ba2", + "#cc8ec0", + "#79c82a", + "#9baca0", + "#1a5613", + "#246fa5", + "#cb725f", + "#682d42", + "#a03134", + "#d54222", + "#01f59b", + "#12897b", + "#74a788", + "#fcdcad", + "#048452", + "#3626a5", + "#4dfb77", + "#4212f1", + "#116019", + "#ad6bd0", + "#a63fa4", + "#d24e5d", + "#1a6fdf", + "#6f745a", + "#cf7e83", + "#4b9a93", + "#799a24", + "#e6e164", + "#011995", + "#4c4355", + "#d937bd" + ], + "opacity": 0.8, + "size": 5 + }, + "mode": "markers", + "text": [ + "Video: 59506507
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
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
Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", + "Video: 60619275
Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", + "Video: 59472693
Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", + "Video: 59670121
Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", + "Video: 59295619
Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", + "Video: 60617163
Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", + "Video: 60616423
Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", + "Video: 59170227
Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", + "Video: 59169985
Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", + "Video: 60616927
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
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
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
Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", + "Video: 60616623
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
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
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
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
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
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
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
Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", + "Video: 59670369
Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", + "Video: 59166281
Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", + "Video: 59671567
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
Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", + "Video: 59166461
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
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
Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", + "Video: 59295579
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
Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", + "Video: 59472491
Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", + "Video: 59472425
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
Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", + "Video: 60619577
Text: And for the final piece of background information, I wanted to take another moment to talk about API...", + "Video: 59170291
Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", + "Video: 60619651
Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", + "Video: 59473191
Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", + "Video: 59170297
Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", + "Video: 59167015
Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", + "Video: 59170043
Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", + "Video: 59473147
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
Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", + "Video: 59166915
Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", + "Video: 59667365
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
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
Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", + "Video: 59471979
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
Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", + "Video: 59472505
Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", + "Video: 59669217
Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", + "Video: 59671221
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
Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", + "Video: 59473201
Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", + "Video: 60622463
Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", + "Video: 60619299
Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", + "Video: 59295607
Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", + "Video: 59297575
Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", + "Video: 59507687
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
Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", + "Video: 59673431
Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", + "Video: 59473137
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
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
Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", + "Video: 59669631
Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", + "Video: 59673663
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
Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", + "Video: 59504785
Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", + "Video: 59505337
Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", + "Video: 59271655
Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", + "Video: 59472883
Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", + "Video: 59673639
Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", + "Video: 59472463
Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", + "Video: 59297595
Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", + "Video: 60619149
Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", + "Video: 59297735
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
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
Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", + "Video: 59472067
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
Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", + "Video: 59295553
Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", + "Video: 59297773
Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", + "Video: 59295583
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
Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", + "Video: 59295429
Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", + "Video: 60595637
Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", + "Video: 59668027
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
Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", + "Video: 59295377
Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", + "Video: 59666211
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
Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", + "Video: 60616833
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
Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", + "Video: 59297561
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
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
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
Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", + "Video: 60620395
Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", + "Video: 59665127
Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", + "Video: 59668923
Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", + "Video: 59504887
Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", + "Video: 59170165
Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", + "Video: 60617251
Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", + "Video: 59166951
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
Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", + "Video: 60616663
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
Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", + "Video: 59670087
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
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
Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", + "Video: 59295435
Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", + "Video: 59297609
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
Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", + "Video: 59295549
Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", + "Video: 59665129
Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", + "Video: 59169991
Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", + "Video: 59472027
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
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
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
Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", + "Video: 59295431
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
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
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
Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", + "Video: 59166949
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
Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", + "Video: 59297585
Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", + "Video: 59170255
Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", + "Video: 60614589
Text: So we're now going to run a large language model directly on your box using a platform called llama,...", + "Video: 59297601
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
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
Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", + "Video: 59170135
Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", + "Video: 59472017
Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", + "Video: 59507017
Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", + "Video: 60619883
Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", + "Video: 59508297
Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", + "Video: 60619247
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
Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", + "Video: 59170233
Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", + "Video: 59671231
Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", + "Video: 60620397
Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", + "Video: 59170093
Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", + "Video: 59473089
Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", + "Video: 60395261
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
Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", + "Video: 59507313
Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", + "Video: 60619721
Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", + "Video: 59295451
Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", + "Video: 59166919
Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", + "Video: 59295441
Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", + "Video: 59295541
Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", + "Video: 59473101
Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", + "Video: 59507423
Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", + "Video: 59295545
Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", + "Video: 59472503
Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", + "Video: 60614591
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
Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", + "Video: 60617255
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
Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", + "Video: 59505329
Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", + "Video: 59669049
Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", + "Video: 60619439
Text: This now brings us to an extremely important property of LMS called the context window that I want t...", + "Video: 59668181
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
Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", + "Video: 59507785
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
Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", + "Video: 59166465
Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", + "Video: 59473071
Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", + "Video: 59295423
Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", + "Video: 59297723
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
Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", + "Video: 59666831
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
Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", + "Video: 60616855
Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", + "Video: 59506611
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
Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", + "Video: 59166317
Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", + "Video: 59295439
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
Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", + "Video: 59472137
Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", + "Video: 59297693
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
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
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
Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", + "Video: 59166847
Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", + "Video: 59170223
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
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
Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", + "Video: 60619281
Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", + "Video: 59472429
Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", + "Video: 59167009
Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", + "Video: 59166481
Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", + "Video: 59670933
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
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
Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", + "Video: 59297603
Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", + "Video: 60614541
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
Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", + "Video: 59667841
Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", + "Video: 59472007
Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", + "Video: 59507435
Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", + "Video: 59509185
Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", + "Video: 59473159
Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", + "Video: 60619447
Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", + "Video: 59166353
Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", + "Video: 60619123
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
Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", + "Video: 60619289
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
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
Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", + "Video: 59295601
Text: So it's time to continue our journey into the world of open source and understand which models we sh...", + "Video: 59170025
Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", + "Video: 59166443
Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", + "Video: 60620025
Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", + "Video: 59170055
Text: Welcome to the world of Google Colab.\nYou may already be very familiar with Google Colab, even if so..." + ], + "type": "scatter3d", + "x": [ + -54.867924, + -10.016936, + -31.258097, + 31.70372, + 30.986057, + -6.185337, + -33.489285, + 28.219635, + 17.128328, + 62.72473, + 47.31179, + -24.2394, + -1.2053607, + -51.674755, + 21.652782, + -16.459143, + -20.696423, + 10.337022, + 19.14492, + -3.3044112, + -35.514133, + -5.4719872, + -5.1226387, + 39.50191, + 4.9317994, + 2.9549847, + 4.1326537, + -11.681201, + 13.737143, + -38.953957, + 38.51183, + 12.244814, + -13.795913, + 25.426931, + -41.59921, + -4.846219, + 26.703289, + 4.1719337, + -16.057596, + -45.16751, + -42.312576, + 41.194798, + -5.312441, + -25.082775, + -19.01139, + -25.420565, + -33.847595, + 44.942863, + 42.343197, + -52.003693, + -2.027634, + -8.932509, + 7.358109, + 21.236534, + 8.489856, + -32.668716, + 19.729362, + 38.779385, + 11.676811, + -34.77253, + -19.88353, + -5.9252167, + -27.363443, + -23.733849, + 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", + "text/html": [ + "
" + ] + }, + "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}
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": "markdown", + "id": "9468860b-86a2-41df-af01-b2400cc985be", + "metadata": {}, + "source": [ + "# Time to use LangChain to bring it all together" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "129c7d1e-0094-4479-9459-f9360b95f244", + "metadata": {}, + "outputs": [], + "source": [ + "# create a new Chat with OpenAI\n", + "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", + "\n", + "# set up the conversation memory for the chat\n", + "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", + "\n", + "# the retriever is an abstraction over the VectorStore that will be used during RAG\n", + "retriever = vectorstore.as_retriever()\n", + "\n", + "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", + "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" + ] + }, + { + "cell_type": "markdown", + "id": "e85ddd60-6d97-44c4-a47a-1c7a6d4ce4df", + "metadata": {}, + "source": [ + "### When you run the next cell, you will get a LangChainDeprecationWarning about the simple way we use LangChain memory. They ask us to migrate to their new approach for memory. Just ignore this. Ed feels quite conflicted about this. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "968e7bf2-e862-4679-a11f-6c1efb6ec8ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ed Donner currently lives in New York City.\n" + ] + } + ], + "source": [ + "query = \"Can tell me where Ed Donner currently lives.\"\n", + "result = conversation_chain.invoke({\"question\":query})\n", + "print(result[\"answer\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e6eb99fb-33ec-4025-ab92-b634ede03647", + "metadata": {}, + "outputs": [], + "source": [ + "# set up a new conversation memory for the chat\n", + "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", + "\n", + "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", + "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" + ] + }, + { + "cell_type": "markdown", + "id": "bbbcb659-13ce-47ab-8a5e-01b930494964", + "metadata": {}, + "source": [ + "## Now we will bring this up in Gradio using the Chat interface -\n", + "\n", + "A quick and easy way to prototype a chat with an LLM" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c3536590-85c7-4155-bd87-ae78a1467670", + "metadata": {}, + "outputs": [], + "source": [ + "# Wrapping in a function - note that history isn't used, as the memory is in the conversation_chain\n", + "\n", + "def chat(message, history):\n", + " result = conversation_chain.invoke({\"question\": message})\n", + " return result[\"answer\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b252d8c1-61a8-406d-b57a-8f708a62b014", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7861\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# And in Gradio:\n", + "\n", + "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5435b2b9-935c-48cd-aaf3-73a837ecde49", + "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 +}