diff --git a/docs/404.html b/docs/404.html index 984a8d5ec..1f5bc4914 100755 --- a/docs/404.html +++ b/docs/404.html @@ -4,13 +4,13 @@ Page Not Found | ☀️ - - + +
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Page Not Found

We could not find what you were looking for.

Please contact the owner of the site that linked you to the original URL and let them know their link is broken.

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General

  • Avoid processed foods: sausages, processed or industrial sauces and oils, Processed drinks (soda, industrial malt, etc.)

  • Avoid artificial sweeteners and added sugar: processed chocolate milk, processed banana milk, processed fruit juice, and other artificially made combinations that contain unhealthy additives such as refined or added sugar and preservatives. These refined sugar weaken the immune system and have other negative effects

  • Limit daily milk intake to maximum one glass Mixed with honey

  • Avoid consuming cold temperature foods or drinks right after waking up, like cold milk or cold juice

  • Avoid consuming cold nature foods or drinks right after waking up, like cheese or yogurt

  • It's best to start your day with something warm, such as green tea or honey tea with dates

  • Use bran bread: The bran is the outer layer of the grain and is rich in fiber, vitamins, minerals, and antioxidants. It provides essential nutrients like B vitamins, iron, magnesium, and dietary fiber

  • Do not drink water with meals. From half an hour before the meal to 1.5 hours after the meal. It puts pressure on the stomach.

  • Avoid straining to poop, as it can put pressure on the stomach.

  • Maintain your body's energy levels.

  • Avoid self-indulgence and masturbation, as it weakens the body.

  • Reduce stress levels, as stress weakens the immune system.

  • Going to bed late is not good for digestion. you should be Sleep by 11 max.

  • Sleep properly. Lie on your back or side, but not on your stomach.

  • Proper posture when sitting

  • Walking for at least 10 minutes in the sun before or after noon.

  • Take a shower at least twice a week. Showering is also exercise for your muscles.

Back, Neck, and Body

  • Do daily light core exercises
  • Do isometric neck exercises to strengthen your neck muscles. make sure your head is not in the middle. For example, if you are doing resistance exercise in front of your head, your head better be slightly bent forward. Do this for all directions. several sessions per day, such as 3 sessions. Each movement 10 seconds or 10 repetitions.
  • Stay active and avoid keeping your neck still. Move your neck every 30 minutes.
  • Limit work to a maximum of 6 hours per day.
  • Avoid impact. Avoid carrying heavy bags
  • Avoid bumps and cars with weak shock absorbers.
  • Avoid bending your neck backward too much or keeping your neck bent backward for extended periods Foods
  • Mattresses: A medium-firm mattress is generally considered to be the best option. This is because it provides enough support to keep the spine in alignment, without being too firm that restrict your movement and may causing pain, or too soft and causing sinking and misalignment

Juice

  • Pineapple juice
  • Cantaloupe juice
  • Strawberry juice
  • Pomegranate juice
  • Mulberry juice
  • Orrange juice

Foods

  • Sesame seeds
  • Figs
  • Bananas
  • Lentils
  • Honey

Drinks

  • Honey boiled with lukewarm water + Chia seed
  • Honey with warm boiled water + Rose water
  • Honey with lukewarm boiled water + lemon

disinfection

  • 10 minutes of breathing onion or mint fumes. 3 onions
  • Lemon and honey
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Lovely Tools

These are some of the lovely, simple, and friendly tools that I use:

  • Manjaro: A Linux operating system that is easy to use and customize
  • Pencil: A simple and lightweight text editor
  • BalenaEtcher: A tool for creating bootable USB drives
  • GParted: A tool for managing disk partitions
  • Firefox: A web browser that is fast, secure, and privacy-focused
  • KShutdown: A simple and easy-to-use shutdown timer
  • VLC: A media player that can play a wide variety of files
  • qBittorrent: A BitTorrent client that is free and open source
  • Stretchly: A productivity timer that helps you take breaks
  • Docusaurus: A static-site generator for creating documentation websites
  • QR Code Online

My ETH Address​

0xc9b64496986E7b6D4A68fDF69eF132A35e91838e

- - + + \ No newline at end of file diff --git a/docs/Wealth/index.html b/docs/Wealth/index.html index 8cc6da45e..9260c92f1 100644 --- a/docs/Wealth/index.html +++ b/docs/Wealth/index.html @@ -4,8 +4,8 @@ Wealth in the Quran | ☀️ - - + +
@@ -14,13 +14,13 @@ "O you who have believed, do not invalidate your charities with reminders or injury as does one who spends his wealth to be seen by the people and does not believe in Allah and the Last Day. His example is like that of a [large] smooth stone upon which is dust and is hit by a downpour that leaves it bare. They are unable [to keep] anything of what they have earned."

Rebellion​

Surah Ash-Shura (42:27)
"If Allah were to extend [excessively] provision for His servants, they would certainly rebel throughout the earth; but He sends [it] down in an amount which He wills. Indeed, He is, of His servants, Acquainted and Seeing."

Allah withholds excessive wealth from some individuals, even good ones, to prevent potential rebellion or arrogance. By limiting provisions, God preserves humility and righteousness among His servants

Sin​

Surah Al-An'am (6:44)
"So when they forgot that by which they had been reminded, We opened to them the doors of every [good] thing until, when they rejoiced in that which they were given, We seized them suddenly, and they were [then] in despair."

God allows some individuals to stray in their wrongdoing, letting them increase in wealth temporarily as a form of trial or punishment. Ultimately, such wealth lacks blessing and may lead to ruin.

Surah Al-Mu’minun (23:55-56)
-"Do they think that We extend to them wealth and children? We hasten for them good things in this life, but they do not perceive."

ثروت در قرآن

انسان خوبعملانسان بد
تا ۷۰۰ برابر⬆️ صدقه ⬇️مانند بذر روی سنگ
درسی از خداوند⬇️ گناه، جهل ⬆️موقتی، و به دنبال آن مجازات است
محدود شده توسط خدا برای منع طغیان⬇️ طغیان ⬆️موقتی، و به دنبال آن مجازات است

صدقه​

سوره بقره (۲:۲۶۱)
+"Do they think that We extend to them wealth and children? We hasten for them good things in this life, but they do not perceive."

ثروت در قرآن

انسان خوبعملانسان بد
تا ۷۰۰ برابر⬆️ صدقهمانند بذر روی سنگ
محدود شده توسط خدا برای منع طغیان⬇️ طغیانموقتی، و به دنبال آن مجازات است
درسی از خداوندگناه، جهلموقتی، و به دنبال آن مجازات است

صدقه​

سوره بقره (۲:۲۶۱)
«مَثَلُ الَّذِینَ یُنفِقُونَ أَمْوَالَهُمْ فِی سَبِیلِ اللَّهِ کَمَثَلِ حَبَّةٍ أَنبَتَتْ سَبْعَ سَنَابِلَ فِی کُلِّ سُنبُلَةٍ مِّائَةُ حَبَّةٍ ۗ وَاللَّهُ یُضَاعِفُ لِمَن یَشَاءُ»

كسانى كه اموال خود را در راه خدا انفاق مى‌كنند، همانند بذرى هستند كه هفت خوشه بروياند؛ كه در هر خوشه، يكصد دانه باشد؛ وخداوند آن را براى هر كس بخواهد و شايسته باشد ، دو يا چند برابر مى‌كند؛ و لطف خدا گسترده، و او به همه چيز داناست

سوره بقره (۲:۲۶۴)
«یَا أَیُّهَا الَّذِینَ آمَنُوا لَا تُبْطِلُوا صَدَقَاتِکُم بِالْمَنِّ وَالْأَذَىٰ کَالَّذِی یُنفِقُ مَالَهُ رِئَاءَ النَّاسِ وَلَا یُؤْمِنُ بِاللَّهِ وَالْیَوْمِ الْآخِرِ ۖ فَمَثَلُهُ کَمَثَلِ صَفْوَانٍ عَلَیْهِ تُرَابٌ فَأَصَابَهُ وَابِلٌ فَتَرَکَهُ صَلْدًا ۖ لَّا یَقْدِرُونَ عَلَىٰ شَیْءٍ مِّمَّا کَسَبُوا»

طغیان​

سوره شوری (۴۲:۲۷)
«وَلَوْ بَسَطَ اللَّهُ الرِّزْقَ لِعِبَادِهِ لَبَغَوْا فِی الْأَرْضِ وَلَـٰکِن یُنَزِّلُ بِقَدَرٍ مَّا یَشَاءُ ۚ إِنَّهُ بِعِبَادِهِ خَبِیرٌ بَصِیرٌ»

خداوند ثروت بیش از حد را از برخی افراد، حتی افراد خوب، دریغ می‌کند تا از طغیان یا تکبر احتمالی جلوگیری کند. با محدود کردن روزی، خداوند تواضع و پرهیزگاری را در میان بندگانش حفظ می‌کند

گناه​

سوره انعام (۶:۴۴)
«فَلَمَّا نَسُوا مَا ذُکِّرُوا بِهِ فَتَحْنَا عَلَیْهِمْ أَبْوَابَ کُلِّ شَیْءٍ حَتَّىٰ إِذَا فَرِحُوا بِمَا أُوتُوا أَخَذْنَاهُم بَغْتَةً فَإِذَا هُم مُّبْلِسُونَ»

خداوند به برخی افراد اجازه می‌دهد در گناهکاری خود گمراه شوند و به آنها اجازه می‌دهد موقتاً ثروتشان به عنوان آزمایش یا مجازات افزایش یابد. در نهایت، چنین ثروتی فاقد برکت است و ممکن است به نابودی منجر شود.

سوره مؤمنون (۲۳:۵۵-۵۶)
«أَیَحْسَبُونَ أَنَّمَا نُمِدُّهُم بِهِ مِن مَّالٍ وَبَنِینَ * نُسَارِعُ لَهُمْ فِی الْخَیْرَاتِ ۚ بَل لَّا یَشْعُرُونَ»

منابع

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Chat Uis

Librechat​

sudo pamac install mongodb-bin
sudo systemctl enable mongodb.service
sudo systemctl restart mongodb.service
mongosh 'mongodb://localhost:27017'

sudo pacman -S meilisearch redis
sudo systemctl enable redis
sudo systemctl restart redis
sudo systemctl enable meilisearch
sudo systemctl restart meilisearch

git clone https://github.com/danny-avila/LibreChat.git --depth=1
cd LibreChat
cp .env.example .env
nano .env
# Comment PLUGIN_MODELS, GOOGLE_API_KEY, openAI and other providers to disable them
# you may want to change ENDPOINTS variable and add ollama as well. not necessary
RAG_API_URL=http://127.0.0.1:11434/v1/
EMBEDDINGS_PROVIDER=ollama
OLLAMA_BASE_URL=http://127.0.0.1:11434/v1/
EMBEDDINGS_MODEL=mxbai-embed-large:latest
DEBUG_RAG_API=true

mv librechat.example.yaml librechat.yaml

custom:
  - name: "Ollama"
    apiKey: "ollama"
    baseURL: "http://127.0.0.1:11434/v1/"
    # or baseURL: "http://localhost:11434/v1/chat/completions" 
    models:
      default: [
        "llama3.1:8b"
        ]
      fetch: true
    titleConvo: true
    titleModel: "current_model"
fileConfig:
  endpoints:
    custom:
      fileLimit: 5
      fileSizeLimit: 10  # Maximum size for an individual file in MB
      totalSizeLimit: 50  # Maximum total size for all files in a single request in MB
      supportedMimeTypes:
        - "image/.*"
        - "application/pdf"
        - "application/text"
        - "application/x-sh"
        - "application/json"
        - "application/javascript"
        - "application/x-yaml"
        - "application/x-shellscript"
        - "application/text-plain"
        - "text/plain"
    default:
      totalSizeLimit: 20
      supportedMimeTypes:
        - "image/.*"
        - "application/pdf"
        - "application/text"
        - "application/x-sh"
        - "application/json"
        - "application/javascript"
        - "application/x-yaml"
        - "application/x-shellscript"
        - "application/text-plain"
        - "text/plain"
  serverFileSizeLimit: 100  # Global server file size limit in MB
  avatarSizeLimit: 2  # Limit for user avatar image size in MB

# local rag still need wokr https://github.com/danny-avila/LibreChat/discussions/3293
npm ci
npm run frontend
npm run backend

https://github.com/mckaywrigley/chatbot-ui​

https://github.com/open-webui/open-webui​

https://github.com/huggingface/chat-ui​

https://github.com/nomic-ai/gpt4all​

LobeChat​

curl -fsSL https://ollama.com/install.sh | sh
sudo systemctl enable ollama.service 
ollama pull llama2

curl -fsSL https://get.pnpm.io/install.sh | sh -
git --depth 1 clone https://github.com/lobehub/lobe-chat.git
# git fetch --unshallow 
# git pull --all
cd lobe-chat
pnpm install
mv .env.example .env
OLLAMA_PROXY_URL=http://127.0.0.1:11434/v1
OLLAMA_MODEL_LIST=llama2

pnpm dev
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Geenrative AI

How do large language models work?​

Tokenizer, text to numbers​

Large Language Models receive a text as input and generate a text as output. However, being statistical models, they work much better with numbers than text sequences. That’s why every input to the model is processed by a tokenizer, before being used by the core model. A token is a chunk of text – consisting of a variable number of characters, so the tokenizer's main task is splitting the input into an array of tokens. Then, each token is mapped with a token index, which is the integer encoding of the original text chunk.

alt text

Predicting output tokens​

Given n tokens as input (with max n varying from one model to another), the model is able to predict one token as output. This token is then incorporated into the input of the next iteration, in an expanding window pattern

Selection process, probability distribution​

The output token is chosen by the model according to its probability of occurring after the current text sequence

Foundation Models versus LLMs​

The term Foundation Model was coined by Stanford researchers and defined as an AI model that follows some criteria, such as:

  • They are trained using unsupervised learning or self-supervised learning, meaning they are trained on unlabeled multi-modal data, and they do not require human annotation or labeling of data for their training process.
  • They are very large models, based on very deep neural networks trained on billions of parameters.
  • They are normally intended to serve as a foundation for other models, meaning they can be used as a starting point for other models to be built on top of, which can be done by fine-tuning.

alt text

References​

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Langchain

LangChain is a framework for developing applications powered by language models

Install​

python -m venv .venv
source .venv/bin/activate

pip install --upgrade pip
pip install langchain
pip install langchain[llms]
pip install langchain[all]
pip install huggingface_hub

Simple huggingface example​

from langchain.llms import HuggingFaceHub
from langchain import PromptTemplate, LLMChain
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_n"

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])

repo_id = "tiiuae/falcon-40b"
llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 64}
)

question = "Who won the FIFA World Cup in the year 1994? "

llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run(question))
print("done")

References​

https://python.langchain.com/docs/get_started/introduction.html https://github.com/langchain-ai/langchain

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Ollama

Ollama is an open-source AI model server. It can get and run large language models (LLMs) locally on your machine.

Rocm​

First make sure you have rocm or NVIDIA CUDA installed.

Arch​

sudo pacman -S base-devel cmake gcc python3
# pyenv is a tool to manage multiple versions of Python
curl https://pyenv.run | bash
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.zshrc
echo '[[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.zshrc
echo 'eval "$(pyenv init -)"' >> ~/.zshrc
source  ~/.zshrc
pyenv install 3.12
pyenv versions
pyenv global 3.12
# or use system to get back to system python
# pyenv global system


pip install --upgrade pip --break-system-packages
# If you have permission issues
# sudo rm /usr/lib/python3.12/EXTERNALLY-MANAGED
# sudo chmod a+rwx /usr/lib/python3.12/ -R

# https://wiki.archlinux.org/title/GPGPU
sudo pamac install rocm-core hsa-rocr rocm-opencl-runtime comgr roctracer hsakmt-roct rocm-language-runtime rocminfo rocm-cmake hip rocm-smi-lib rocm-clang-ocl rocm-hip-runtime rocm-hip-sdk rocm-opencl-sdk rocm-device-libs --no-confirm
sudo usermod -a -G render,video $LOGNAME
sudo reboot
rocminfo

Ubuntu​

# https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html
# https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/native-install/ubuntu.html
# https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/amdgpu-install.html
# https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/post-install.html
sudo apt update
sudo apt install "linux-headers-$(uname -r)" "linux-modules-extra-$(uname -r)"
sudo usermod -a -G render,video $LOGNAME # Add the current user to the render and video groups
wget https://repo.radeon.com/amdgpu-install/6.2/ubuntu/noble/amdgpu-install_6.2.60200-1_all.deb
sudo apt install ./amdgpu-install_6.2.60200-1_all.deb
sudo apt update
sudo apt install amdgpu-dkms rocm

Old GPUs​

# If you are using RDNA or RDNA 2 architecture like AMD Radeon RX 6500 XT, you may need to follow this step:
sudo nano ~/.profile
# Add the following lines:
export HSA_OVERRIDE_GFX_VERSION=10.3.0
export ROC_ENABLE_PRE_VEGA=1

sudo systemctl edit --full ollama.service
Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"
Environment="ROC_ENABLE_PRE_VEGA=1"

Test​

pip install -U transformers datasets evaluate accelerate --user
sudo pip install -U transformers datasets evaluate accelerate


# https://www.tensorflow.org/install/pip
# https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#install-tensorflow-versions
pip install --user tensorflow-rocm=="2.16.1" -f "https://repo.radeon.com/rocm/manylinux/rocm-rel-6.2/" --upgrade
# cp312 means you need to have python 3.12


# https://pytorch.org/
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1 --break-system-packages
pip3 install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1 --user
# sudo pip3 install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1
pip3 install -U xformers --user
# sudo pip3 install -U xformers
import tensorflow as tf
import torch

print(tf.config.list_physical_devices())
print(tf.__version__)

print(torch.cuda.is_available())
print(torch.version.hip)

Install​

Native​

curl -fsSL https://ollama.com/install.sh | sh

# Run a model
ollama run llama3.1:8b

# List models
ollama list

# Loaded Models
ollama ps

# Model Info
ollama show llama3.1:8b
#   Model
#         arch                    llama
#         parameters              8.0B
#         quantization            Q4_0
#         context length          131072
#         embedding length        4096

#   Parameters
#         stop    "<|start_header_id|>"
#         stop    "<|end_header_id|>"
#         stop    "<|eot_id|>"

# Logs
journalctl -u ollama.service --no-pager --follow

Docker​

ollama supports docker images. You can either use official images or build your own.

You need to have NVIDIA Container Toolkit installed for NVIDIA GPUs.

And rocm for AMD GPUs.

# NVIDIA
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
# AMD
docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm
# Run a model
docker exec -it ollama ollama run llama2

You can also build your own GPU supported image:

./scripts/build_linux.sh

Files​

Ollama files in Linux are located here:

/home/mlibre/.ollama
/usr/local/bin/ollama
/usr/share/ollama
/etc/systemd/system/ollama.service
/etc/systemd/system/default.target.wants/ollama.service

Debug​

sudo systemctl edit --full ollama.service

[Unit]
Description=Ollama Service
After=network-online.target

[Service]
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=/home/mlibre/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/bin:/usr/bin/site_perl:/usr/bin/vendor_perl:/usr/bin/core_perl"
Environment="OLLAMA_HOST=0.0.0.0"
Environment="OLLAMA_DEBUG=1"
Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"
Environment="ROC_ENABLE_PRE_VEGA=1"
[Install]
WantedBy=default.target
# you may also add Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0" to support older AMD GPUs


sudo systemctl restart ollama.service
journalctl -u ollama --no-pager -f

Usage​


# Generate text
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1:8b",
  "prompt":"Why is the sky blue?"
}'

# Chat
curl http://localhost:11434/api/chat -d '{
  "model": "llama3.1:8b",
  "messages": [
    { "role": "user", "content": "why is the sky blue?" }
  ],
  "stream": false,
  "system": "You are Sarah. you only uses emojies to answer and nothings else. you only uses one emoji each time"
}' | jq

# Chat with history
curl -s http://localhost:11434/api/chat -d '{
  "model": "llama3.1:8b",
  "messages": [
    {
      "role": "user",
      "content": "You are Sarah. you only uses emojies to answer and nothings else. you only uses one emoji each time"
    },
    {
      "role": "assistant",
      "content": "👋💁"
    },
    {
      "role": "user",
      "content": "hey"
    }
  ],
  "stream": false,
  "system": "You are Sarah. you only uses emojies to answer and nothings else. you only uses one emoji each time"
}' | jq

# Embeddings
curl http://localhost:11434/api/embed -d '{
  "model": "llama3.1:8b",
  "prompt":"Why is the sky blue?"
}'

# OpenAI Compatibile
curl -s http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.1:8b",
    "messages": [
      {
        "role": "user",
        "content": "You are Sarah. you only uses emojies to answer and nothings else. you only uses one emoji each time"
      },
      {
        "role": "assistant",
        "content": "👋💁"
      },
      {
        "role": "user",
        "content": "hey"
      }
    ],
    "stream": false,
    "system": "You are Sarah. you only uses emojies to answer and nothings else. you only uses one emoji each time"
  }' | jq

curl -s http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.1:8b",
    "messages": [
      {
        "role": "system",
        "content": "You are Sarah. you only uses emojies to answer and nothings else. you only uses one emoji each time"
      },
      {
        "role": "user",
        "content": "hey"
      }
    ],
    "stream": false
  }' | jq

Costomizing Model​

nano Modelfile

FROM llama3.1:8b

SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, only. And Always start your answer with HAYAYAYA
"""
ollama create mario -f ./Modelfile
ollama run mario

Embedding models​

Ollama supports embedding models, making it possible to build retrieval augmented generation (RAG) applications that combine text prompts with existing documents or other data.

Embedding models are models that are trained specifically to generate vector embeddings.

alt text

The resulting vector embedding arrays can then be stored in a database, which will compare them as a way to search for data that is similar in meaning.

You can store as many text as you want in these vector databases, such as a vector database. for example you can embed some books, and then ask chroma to find the most similar text to a given input. you then send chroma output to a LLM, and the LLM can use the context of the text to generate a response.

more information is here: https://ollama.com/blog/embedding-models

Installation​

ollama pull mxbai-embed-large

curl http://localhost:11434/api/embeddings -d '{
  "model": "mxbai-embed-large",
  "prompt": "Llamas are members of the camelid family"
}'

curl http://localhost:11434/api/embeddings -d '{
  "model": "llama3.1:8b",
  "prompt": "Llamas are members of the camelid family"
}'

Configuration​

sudo systemctl edit --full ollama.service
Environment="OLLAMA_HOST=0.0.0.0"

Memory and Quantization Options​

When working with models like the Codestral 22B, you have several quantization options that affect both memory usage and accuracy:

  • 32-bit floating-point: Uses 88 GB of memory (22 x 4 = 88G) and is the most accurate
  • 16-bit floating-point: Uses 44 GB of memory (22 x 2 = 44G)
  • 8-bit floating-point: Uses 22 GB of memory (22 x 1 = 22G)
  • 4-bit floating-point: Uses 11 GB of memory (22 x 1/2 = 11G) and is less accurate

These options allow you to balance between model accuracy and the amount of memory used, depending on your system's resources and the specific needs of your application.

GPU Compatibility​

To effectively run models like Codestral 22B, you need a GPU with sufficient memory to handle the model's requirements:

  • RTX 6000: With 48 GB of memory, this GPU can handle 16-bit floating-point quantization (44G) and lower, providing a good balance of accuracy and performance.
  • NVIDIA RTX 4090: With 24 GB of memory, this GPU is suited for 8-bit floating-point quantization (22G) and below.
  • GeForce GTX 1080 Ti: With 11 GB of memory, this GPU is limited to 4-bit floating-point quantization (11G), which may result in reduced accuracy but still allows you to run the model on less capable hardware.

You can find full list of ollama supported gpus here: https://github.com/ollama/ollama/blob/main/docs/gpu.md

Uninstall​

sudo systemctl stop ollama
sudo systemctl disable ollama
sudo rm /etc/systemd/system/ollama.service
sudo rm $(which ollama)
sudo rm -r /usr/share/ollama
sudo rm -r ~/.ollama
sudo userdel ollama
sudo groupdel ollama

Ollama.js​

npm i ollama
sudo npm i ollama -g
  • Simple example
const ollama = require('ollama').default

void async function main() {
  const response = await ollama.chat({
    model: 'llama3.1:8b',
    messages: [
      {
        "role": "user",
        "content": "You are Sarah. you only uses emojies to answer and nothings else. you only uses one emoji each time"
      },
      {
        "role": "assistant",
        "content": "👋💁"
      },
      {
        "role": "user",
        "content": "hey"
      }
    ],
  })
  console.log(response.message.content)
  • Streaming example
  const message = { role: 'user', content: 'Why is the sky blue?' }
  const response = await ollama.chat({ model: 'llama3.1:8b', messages: [message], stream: true })
  for await (const part of response) {
    process.stdout.write(part.message.content)
  }
}()
  • Advanced example
const { Ollama } = require( "ollama" )

// Simulates an API call to get flight times
// In a real application, this would fetch data from a live database or API
function get_flight_times ( departure, arrival )
{
 const flights = {
  "LAX-NYC": { departure: "02:00 PM", arrival: "10:30 PM", duration: "8h 30m" },
  "LHR-JFK": { departure: "10:00 AM", arrival: "01:00 PM", duration: "3h 00m" },
  "NYC-LAX": { departure: "08:00 AM", arrival: "11:30 AM", duration: "3h 30m" },
  "JFK-LHR": { departure: "09:00 PM", arrival: "09:00 AM", duration: "12h 00m" },
  "CDG-DXB": { departure: "11:00 AM", arrival: "08:00 PM", duration: "9h 00m" },
  "DXB-CDG": { departure: "03:00 AM", arrival: "07:30 AM", duration: "4h 30m" }
 };

 const key = `${departure}-${arrival}`.toUpperCase();
 return JSON.stringify( flights[key] || { error: "Flight not found" });
}

const get_flight_times_schema = {
 type: "function",
 function: {
  name: "get_flight_times",
  description: "Get the flight times between two cities",
  parameters: {
   type: "object",
   properties: {
    departure: {
     type: "string",
     description: "The departure city (airport code)",
    },
    arrival: {
     type: "string",
     description: "The arrival city (airport code)",
    },
   },
   required: ["departure", "arrival"],
  },
 }
}

const availableFunctions = {
 get_flight_times,
};

function handleToolCalls ( toolCalls, messages )
{
 for ( const tool of toolCalls )
 {
  const functionToCall = availableFunctions[tool.function.name];
  if ( functionToCall )
  {
   const functionResponse = functionToCall(
    tool.function.arguments.departure,
    tool.function.arguments.arrival
   );
   // Add function response to the conversation
   messages.push({
    role: "tool",
    content: functionResponse,
   });
  }
 }
}

void async function main ()
{
 const ollama = new Ollama({ host: "http://127.0.0.1:11434" })
 const model = "llama3.1:8b";

 let messages = [{ role: "user", content: "What is the flight time from New York (NYC) to Los Angeles (LAX)?" }];
 // First API call: Send the query and function description to the model
 const response = await ollama.chat({
  model,
  messages,
  tools: [get_flight_times_schema],
 })
 // Add the model's response to the conversation history
 messages.push( response.message );
 if ( response.message.tool_calls && response.message.tool_calls.length > 0 )
 {
  handleToolCalls( response.message.tool_calls, messages );
 }
 else
 {
  console.log( response.message.content );
  return
 }

 // Second API call: Get second response from the model
 const response2 = await ollama.chat({
  model,
  messages,
 });
 messages.push( response2.message );
 console.log( response2.message.content );

 messages.push({ role: "user", content: "What is the flight time from CDG to DXB?" });
 const response3 = await ollama.chat({
  model,
  messages,
  tools: [get_flight_times_schema]
 });
 messages.push( response3.message );
 if ( response3.message.tool_calls && response3.message.tool_calls.length > 0 )
 {
  handleToolCalls( response3.message.tool_calls, messages );
 }
 else
 {
  console.log( response.message.content );
  return
 }

 const response4 = await ollama.chat({
  model,
  messages,
  tools: [get_flight_times_schema]
 });
 console.log( response4.message.content );
}()

Reference​

- - + + \ No newline at end of file diff --git a/docs/ai/prompt/index.html b/docs/ai/prompt/index.html index 6a395377b..1fb0da520 100755 --- a/docs/ai/prompt/index.html +++ b/docs/ai/prompt/index.html @@ -4,8 +4,8 @@ Prompt | ☀️ - - + +
@@ -16,7 +16,7 @@ The table has two columns only: 'Command', 'Description'. Make sure examples are real-world examples and are very useful. The table format is:

|               Command               | Description |
| :---------------------------------: | :---------: |
|             `command1`              | Description |
|       `example of command 1`        | Description |
|   `advance example of command 1`    | Description |
| `very advance example of command 1` | Description |
|             `command2`              | Description |
|       `example of command 2`        | Description |
|   `advance example of command 2`    | Description |
| `very advance example of command 2` | Description |

Rewriting artiicle​

I've written an article that needs improvement. I'm looking for help with:

Please assist in enhancing my markdown article.

Extract important sections from article​

Extract only important and technical parts from this article. I don't want a long article with unnecessary, less important information.

References​

- - + + \ No newline at end of file diff --git a/docs/ai/python-for-ai/index.html b/docs/ai/python-for-ai/index.html index fe9208568..e24e7d243 100644 --- a/docs/ai/python-for-ai/index.html +++ b/docs/ai/python-for-ai/index.html @@ -4,13 +4,13 @@ Python | ☀️ - - + +
Skip to main content

Python

Install​

sudo pacman -S python
python --version

Libraries and Executable path:

import sys

print ('\n'.join(sys.path))
# /usr/lib/python312.zip
# /usr/lib/python3.12
# /usr/lib/python3.12/lib-dynload
# /home/mlibre/.local/lib/python3.12/site-packages
# /usr/lib/python3.12/site-packages

print (sys.executable)
# /usr/bin/python

help("a".find)
# Help on built-in function find:

# find(...) method of builtins.str instance
#     S.find(sub[, start[, end]]) -> int

#     Return the lowest index in S where substring sub is found,
#     such that sub is contained within S[start:end].  Optional
#     arguments start and end are interpreted as in slice notation.

#     Return -1 on failure.

Jupyter Notebook​

pip install notebook --break-system-packages
jupyter notebook

You can also open http://localhost:8888/tree or http://localhost:8888/lab

Concepts​

String​

text = "abcd"
print ("thank :) %s" % text) # abcd
print (f"thank :) {text[0:3]}") # abc
print (f'''thanks
:)
{text[1:]} # bcd
{text[-1]} # d
''')

text2 = "pi is {pi:.2f}"
print(text2.format(pi = 3.144444)) # pi is 3.14

text3 = "pi is {} or {}"
print(text3.format(3.14, 3)) # pi is 3.14 or 3

text4 = "pi is {0} or {1}"
print(text4.format(3.14, 3)) # pi is 3.14 or 3

text5 = "a big number {0:,.3f}"
print(text5.format(3000.14567)) # a big number 3,000.146

text6 = "a big number {:,.3f}"
print(text6.format(3000.14567)) # a big number 3,000.146

text7 = "pi is a number"
isLoc, isLoc2 = text7.find("is"), text2.index("is")
print(isLoc, isLoc2) # 3 3

Number​

piString = "3.14"
pi = float(piString) # 3.14
b = int(pi) # 3
print(f'''
Multiplication: {pi * b}
Division: {pi / b}
Floor division: {pi // b}
Modulus: {pi % b}
Exponentiation: {pi ** b}
''')

List​

mixed = ["apple", 2, 3.5, True]
mixed.append("orange")
mixed.remove(3.5)
print(mixed) # ['apple', 2, True, 'orange']

squares = [x ** 2 for x in range(10)]
print(squares) # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Tuple​

fruits = ("apple", "banana", "cherry")
# Tuples are immutable, so you can't modify them directly
fruits[0] = "kiwi" # would raise an error

Dictionary​

person = {
  "name": "Alice",
  "age": 25,
  "city": "New York"
}
print(person) # {'name': 'Alice', 'age': 25, 'city': 'New York'}
print(person["name"]) # Alice
print(person.keys()) # dict_keys(['name', 'age', 'city'])
print(person.items()) # dict_items([('name', 'Alice'), ('age', 25), ('city', 'New York')])

Loop​

text = "abcd"
for i in text:
  print(i)
# a b c d

for i in range(5):
    print(i)
# 0 1 2 3 4

# Looping through a list
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
    print(fruit)
# apple banana cherry

# Looping through a dictionary
person = {"name": "Alice", "age": 25, "city": "New York"}
for key, value in person.items():
    print(f"{key}: {value}")
# name: Alice
# age: 25
# city: New York

Functions​

def greet(name="Guest"):
    return f"Hello, {name}!"

print(greet()) # Hello, Guest!
print(greet("Bob")) # Hello, Bob!


def sum_all(*args):
    return sum(args)

print(sum_all(1, 2, 3, 4)) # 10


def describe_person(**kwargs):
    for key, value in kwargs.items():
        print(f"{key}: {value}")

describe_person(name="Alice", age=25, city="New York")
# name: Alice
# age: 25
# city: New York


add = lambda x, y: x + y
print(add(3, 5)) # 8

# Using lambda with sort
points = [(2, 3), (1, 2), (4, 1)]
secondValue = lambda point: point[1] # always returns second value
points.sort(key=secondValue)
print(points) # [(4, 1), (1, 2), (2, 3)]


def my_decorator(func):
    def wrapper():
        print("Something is happening before the function is called.")
        func()
        print("Something is happening after the function is called.")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()
# Output:
# Something is happening before the function is called.
# Hello!
# Something is happening after the function is called.

class​

class Person:
    def __init__(self, name):
        self._name = name

    @property
    def name(self):
        return self._name

    @name.setter
    def name(self, value): # now we can set and get with "name" not "_name"
        self._name = value


class Employee(Person): # inherits from Person
    species = "Homo sapiens"  # Class variable
    def __init__(self, name, employee_id):
        super().__init__(name)
        self.employee_id = employee_id
        self._test1 = "test1"  # Protected attribute
        self.__test2 = "test2"   # Private attribute

    def get_employee_details(self):
        return f"Name: {self.name}, Employee ID: {self.employee_id}"

# Creating an instance of the subclass
employee1 = Employee("Bob", "E1234")
print(employee1.get_employee_details())
# Name: Bob, Age: 30, Employee ID: E1234
employee1.name = "Alice"
print(employee1.get_employee_details())
# Name: Alice, Age: 30, Employee ID: E1234
employee1.employee_id = "E1235"
print(employee1.get_employee_details())

Error handling​

try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero!")
except ValueError:
    print("Invalid value!")
else:
    print("error is not ValueError or ZeroDivisionError")
finally:
    print("This will execute no matter what")


# Rasing and error
def check_age(age):
    if age < 5:
        raise ValueError("Age must be 5 or older")
    return "Access granted"

try:
    check_age(4)
except ValueError as e:
    print(e)

Math​

import math as m
m.pi # 3.141592653589793

from math import *
print(pi)

import random as r
print(r.random())

NumPy​

pip install numpy --break-system-packages
import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr) # [1 2 3 4 5]


# Creating a 2D array
arr2d = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2d)
# [[1 2 3]
#  [4 5 6]]
print(arr2d.shape) # (2, 3)


# Creating a 2D array
arr2d = np.array([[1, 2, 3], [4, 5, 6, 7]])
# Raises ValueError. The requested array has an inhomogeneous shape after 1 dimensions


arr = np.array([1, 2, 3, 4, 5])
# Mean
print(np.mean(arr)) # 3.0
# Sum
print(np.sum(arr)) # 15
# Standard deviation
print(np.std(arr)) # 1.4142135623730951
# Minimum and Maximum
print(np.min(arr)) # 1
print(np.max(arr)) # 5


# Dot for 1d array
vector_a = np.array([1, 2, 3])
vector_b = np.array([4, 5, 6])
dot_product = np.dot(vector_a, vector_b) # 1∗4 + 2∗5 + 3∗6 = 4+10+18=32
print("Dot product of vectors:", dot_product) # Dot product of vectors: 32

# Dot for 2d array
matrix_a = np.array([[1, 2], [3, 4]])
matrix_b = np.array([[5, 6], [7, 8]])
matrix_product = np.dot(matrix_a, matrix_b)
print("Dot product of matrices:\n", matrix_product)
#  [ [19 22], [43 50] ]

# Save to a file
np.save('array.npy', arr)
# Load from a file
loaded_arr = np.load('array.npy')
print(loaded_arr) # [1 2 3 4 5]

python-dot

Pandas​

pip install pandas --break-system-packages
import pandas as pd
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)

print(df)
#       Name  Age         City
# 0    Alice   25     New York
# 1      Bob   30  Los Angeles
# 2  Charlie   35      Chicago

df['Age'] = df['Age'] + 1
print(df['Age']) # Age column
# 0    26
# 1    31
# 2    36

print(df.head(2)) # First And Second rows
#     Name  Age      City
# 0  Alice   25  New York
# 1      Bob   30  Los Angeles

print(df.iloc[0])  # First row
# Name       Alice
# Age           25
# City    New York


df1 = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30]
})

df2 = pd.DataFrame({
    'Name': ['Alice', 'David'],
    'Age': [35, 40]
})

# Concatenating DataFrames
df_concat = pd.concat([df1, df2])

# Merging DataFrames on a key
df3 = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

merged_df = pd.merge(df_concat, df3, on='Name')
print(merged_df)
#     Name  Age         City
# 0  Alice   25     New York
# 1    Bob   30  Los Angeles
# 2  Alice   35     New York

Matplotlib​

pip install matplotlib --break-system-packages
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

plt.plot(x, y)
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Simple Line Plot')
plt.show()

References​

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Your Docusaurus site did not load properly.

\n

A very common reason is a wrong site baseUrl configuration.

\n

Current configured baseUrl = ${e} ${"/"===e?" (default value)":""}

\n

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a.languages.objectivec["class-name"],a.languages.objc=a.languages.objectivec,a.languages.ocaml={comment:{pattern:/\(\*[\s\S]*?\*\)/,greedy:!0},char:{pattern:/'(?:[^\\\r\n']|\\(?:.|[ox]?[0-9a-f]{1,3}))'/i,greedy:!0},string:[{pattern:/"(?:\\(?:[\s\S]|\r\n)|[^\\\r\n"])*"/,greedy:!0},{pattern:/\{([a-z_]*)\|[\s\S]*?\|\1\}/,greedy:!0}],number:[/\b(?:0b[01][01_]*|0o[0-7][0-7_]*)\b/i,/\b0x[a-f0-9][a-f0-9_]*(?:\.[a-f0-9_]*)?(?:p[+-]?\d[\d_]*)?(?!\w)/i,/\b\d[\d_]*(?:\.[\d_]*)?(?:e[+-]?\d[\d_]*)?(?!\w)/i],directive:{pattern:/\B#\w+/,alias:"property"},label:{pattern:/\B~\w+/,alias:"property"},"type-variable":{pattern:/\B'\w+/,alias:"function"},variant:{pattern:/`\w+/,alias:"symbol"},keyword:/\b(?:as|assert|begin|class|constraint|do|done|downto|else|end|exception|external|for|fun|function|functor|if|in|include|inherit|initializer|lazy|let|match|method|module|mutable|new|nonrec|object|of|open|private|rec|sig|struct|then|to|try|type|val|value|virtual|when|where|while|with)\b/,boolean:/\b(?:false|true)\b/,"operator-like-punctuation":{pattern:/\[[<>|]|[>|]\]|\{<|>\}/,alias:"punctuation"},operator:/\.[.~]|:[=>]|[=<>@^|&+\-*\/$%!?~][!$%&*+\-.\/:<=>?@^|~]*|\b(?:and|asr|land|lor|lsl|lsr|lxor|mod|or)\b/,punctuation:/;;|::|[(){}\[\].,:;#]|\b_\b/},a.languages.python={comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0,greedy:!0},"string-interpolation":{pattern:/(?:f|fr|rf)(?:("""|''')[\s\S]*?\1|("|')(?:\\.|(?!\2)[^\\\r\n])*\2)/i,greedy:!0,inside:{interpolation:{pattern:/((?:^|[^{])(?:\{\{)*)\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}])+\})+\})+\}/,lookbehind:!0,inside:{"format-spec":{pattern:/(:)[^:(){}]+(?=\}$)/,lookbehind:!0},"conversion-option":{pattern:/![sra](?=[:}]$)/,alias:"punctuation"},rest:null}},string:/[\s\S]+/}},"triple-quoted-string":{pattern:/(?:[rub]|br|rb)?("""|''')[\s\S]*?\1/i,greedy:!0,alias:"string"},string:{pattern:/(?:[rub]|br|rb)?("|')(?:\\.|(?!\1)[^\\\r\n])*\1/i,greedy:!0},function:{pattern:/((?:^|\s)def[ \t]+)[a-zA-Z_]\w*(?=\s*\()/g,lookbehind:!0},"class-name":{pattern:/(\bclass\s+)\w+/i,lookbehind:!0},decorator:{pattern:/(^[\t ]*)@\w+(?:\.\w+)*/m,lookbehind:!0,alias:["annotation","punctuation"],inside:{punctuation:/\./}},keyword:/\b(?:_(?=\s*:)|and|as|assert|async|await|break|case|class|continue|def|del|elif|else|except|exec|finally|for|from|global|if|import|in|is|lambda|match|nonlocal|not|or|pass|print|raise|return|try|while|with|yield)\b/,builtin:/\b(?:__import__|abs|all|any|apply|ascii|basestring|bin|bool|buffer|bytearray|bytes|callable|chr|classmethod|cmp|coerce|compile|complex|delattr|dict|dir|divmod|enumerate|eval|execfile|file|filter|float|format|frozenset|getattr|globals|hasattr|hash|help|hex|id|input|int|intern|isinstance|issubclass|iter|len|list|locals|long|map|max|memoryview|min|next|object|oct|open|ord|pow|property|range|raw_input|reduce|reload|repr|reversed|round|set|setattr|slice|sorted|staticmethod|str|sum|super|tuple|type|unichr|unicode|vars|xrange|zip)\b/,boolean:/\b(?:False|None|True)\b/,number:/\b0(?:b(?:_?[01])+|o(?:_?[0-7])+|x(?:_?[a-f0-9])+)\b|(?:\b\d+(?:_\d+)*(?:\.(?:\d+(?:_\d+)*)?)?|\B\.\d+(?:_\d+)*)(?:e[+-]?\d+(?:_\d+)*)?j?(?!\w)/i,operator:/[-+%=]=?|!=|:=|\*\*?=?|\/\/?=?|<[<=>]?|>[=>]?|[&|^~]/,punctuation:/[{}[\];(),.:]/},a.languages.python["string-interpolation"].inside.interpolation.inside.rest=a.languages.python,a.languages.py=a.languages.python,a.languages.reason=a.languages.extend("clike",{string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^\\\r\n"])*"/,greedy:!0},"class-name":/\b[A-Z]\w*/,keyword:/\b(?:and|as|assert|begin|class|constraint|do|done|downto|else|end|exception|external|for|fun|function|functor|if|in|include|inherit|initializer|lazy|let|method|module|mutable|new|nonrec|object|of|open|or|private|rec|sig|struct|switch|then|to|try|type|val|virtual|when|while|with)\b/,operator:/\.{3}|:[:=]|\|>|->|=(?:==?|>)?|<=?|>=?|[|^?'#!~`]|[+\-*\/]\.?|\b(?:asr|land|lor|lsl|lsr|lxor|mod)\b/}),a.languages.insertBefore("reason","class-name",{char:{pattern:/'(?:\\x[\da-f]{2}|\\o[0-3][0-7][0-7]|\\\d{3}|\\.|[^'\\\r\n])'/,greedy:!0},constructor:/\b[A-Z]\w*\b(?!\s*\.)/,label:{pattern:/\b[a-z]\w*(?=::)/,alias:"symbol"}}),delete a.languages.reason.function,function(e){e.languages.sass=e.languages.extend("css",{comment:{pattern:/^([ \t]*)\/[\/*].*(?:(?:\r?\n|\r)\1[ \t].+)*/m,lookbehind:!0,greedy:!0}}),e.languages.insertBefore("sass","atrule",{"atrule-line":{pattern:/^(?:[ \t]*)[@+=].+/m,greedy:!0,inside:{atrule:/(?:@[\w-]+|[+=])/}}}),delete e.languages.sass.atrule;var t=/\$[-\w]+|#\{\$[-\w]+\}/,n=[/[+*\/%]|[=!]=|<=?|>=?|\b(?:and|not|or)\b/,{pattern:/(\s)-(?=\s)/,lookbehind:!0}];e.languages.insertBefore("sass","property",{"variable-line":{pattern:/^[ \t]*\$.+/m,greedy:!0,inside:{punctuation:/:/,variable:t,operator:n}},"property-line":{pattern:/^[ \t]*(?:[^:\s]+ *:.*|:[^:\s].*)/m,greedy:!0,inside:{property:[/[^:\s]+(?=\s*:)/,{pattern:/(:)[^:\s]+/,lookbehind:!0}],punctuation:/:/,variable:t,operator:n,important:e.languages.sass.important}}}),delete e.languages.sass.property,delete e.languages.sass.important,e.languages.insertBefore("sass","punctuation",{selector:{pattern:/^([ \t]*)\S(?:,[^,\r\n]+|[^,\r\n]*)(?:,[^,\r\n]+)*(?:,(?:\r?\n|\r)\1[ \t]+\S(?:,[^,\r\n]+|[^,\r\n]*)(?:,[^,\r\n]+)*)*/m,lookbehind:!0,greedy:!0}})}(a),a.languages.scss=a.languages.extend("css",{comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|\/\/.*)/,lookbehind:!0},atrule:{pattern:/@[\w-](?:\([^()]+\)|[^()\s]|\s+(?!\s))*?(?=\s+[{;])/,inside:{rule:/@[\w-]+/}},url:/(?:[-a-z]+-)?url(?=\()/i,selector:{pattern:/(?=\S)[^@;{}()]?(?:[^@;{}()\s]|\s+(?!\s)|#\{\$[-\w]+\})+(?=\s*\{(?:\}|\s|[^}][^:{}]*[:{][^}]))/,inside:{parent:{pattern:/&/,alias:"important"},placeholder:/%[-\w]+/,variable:/\$[-\w]+|#\{\$[-\w]+\}/}},property:{pattern:/(?:[-\w]|\$[-\w]|#\{\$[-\w]+\})+(?=\s*:)/,inside:{variable:/\$[-\w]+|#\{\$[-\w]+\}/}}}),a.languages.insertBefore("scss","atrule",{keyword:[/@(?:content|debug|each|else(?: if)?|extend|for|forward|function|if|import|include|mixin|return|use|warn|while)\b/i,{pattern:/( )(?:from|through)(?= )/,lookbehind:!0}]}),a.languages.insertBefore("scss","important",{variable:/\$[-\w]+|#\{\$[-\w]+\}/}),a.languages.insertBefore("scss","function",{"module-modifier":{pattern:/\b(?:as|hide|show|with)\b/i,alias:"keyword"},placeholder:{pattern:/%[-\w]+/,alias:"selector"},statement:{pattern:/\B!(?:default|optional)\b/i,alias:"keyword"},boolean:/\b(?:false|true)\b/,null:{pattern:/\bnull\b/,alias:"keyword"},operator:{pattern:/(\s)(?:[-+*\/%]|[=!]=|<=?|>=?|and|not|or)(?=\s)/,lookbehind:!0}}),a.languages.scss.atrule.inside.rest=a.languages.scss,function(e){var t={pattern:/(\b\d+)(?:%|[a-z]+)/,lookbehind:!0},n={pattern:/(^|[^\w.-])-?(?:\d+(?:\.\d+)?|\.\d+)/,lookbehind:!0},r={comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|\/\/.*)/,lookbehind:!0},url:{pattern:/\burl\((["']?).*?\1\)/i,greedy:!0},string:{pattern:/("|')(?:(?!\1)[^\\\r\n]|\\(?:\r\n|[\s\S]))*\1/,greedy:!0},interpolation:null,func:null,important:/\B!(?:important|optional)\b/i,keyword:{pattern:/(^|\s+)(?:(?:else|for|if|return|unless)(?=\s|$)|@[\w-]+)/,lookbehind:!0},hexcode:/#[\da-f]{3,6}/i,color:[/\b(?:AliceBlue|AntiqueWhite|Aqua|Aquamarine|Azure|Beige|Bisque|Black|BlanchedAlmond|Blue|BlueViolet|Brown|BurlyWood|CadetBlue|Chartreuse|Chocolate|Coral|CornflowerBlue|Cornsilk|Crimson|Cyan|DarkBlue|DarkCyan|DarkGoldenRod|DarkGr[ae]y|DarkGreen|DarkKhaki|DarkMagenta|DarkOliveGreen|DarkOrange|DarkOrchid|DarkRed|DarkSalmon|DarkSeaGreen|DarkSlateBlue|DarkSlateGr[ae]y|DarkTurquoise|DarkViolet|DeepPink|DeepSkyBlue|DimGr[ae]y|DodgerBlue|FireBrick|FloralWhite|ForestGreen|Fuchsia|Gainsboro|GhostWhite|Gold|GoldenRod|Gr[ae]y|Green|GreenYellow|HoneyDew|HotPink|IndianRed|Indigo|Ivory|Khaki|Lavender|LavenderBlush|LawnGreen|LemonChiffon|LightBlue|LightCoral|LightCyan|LightGoldenRodYellow|LightGr[ae]y|LightGreen|LightPink|LightSalmon|LightSeaGreen|LightSkyBlue|LightSlateGr[ae]y|LightSteelBlue|LightYellow|Lime|LimeGreen|Linen|Magenta|Maroon|MediumAquaMarine|MediumBlue|MediumOrchid|MediumPurple|MediumSeaGreen|MediumSlateBlue|MediumSpringGreen|MediumTurquoise|MediumVioletRed|MidnightBlue|MintCream|MistyRose|Moccasin|NavajoWhite|Navy|OldLace|Olive|OliveDrab|Orange|OrangeRed|Orchid|PaleGoldenRod|PaleGreen|PaleTurquoise|PaleVioletRed|PapayaWhip|PeachPuff|Peru|Pink|Plum|PowderBlue|Purple|Red|RosyBrown|RoyalBlue|SaddleBrown|Salmon|SandyBrown|SeaGreen|SeaShell|Sienna|Silver|SkyBlue|SlateBlue|SlateGr[ae]y|Snow|SpringGreen|SteelBlue|Tan|Teal|Thistle|Tomato|Transparent|Turquoise|Violet|Wheat|White|WhiteSmoke|Yellow|YellowGreen)\b/i,{pattern:/\b(?:hsl|rgb)\(\s*\d{1,3}\s*,\s*\d{1,3}%?\s*,\s*\d{1,3}%?\s*\)\B|\b(?:hsl|rgb)a\(\s*\d{1,3}\s*,\s*\d{1,3}%?\s*,\s*\d{1,3}%?\s*,\s*(?:0|0?\.\d+|1)\s*\)\B/i,inside:{unit:t,number:n,function:/[\w-]+(?=\()/,punctuation:/[(),]/}}],entity:/\\[\da-f]{1,8}/i,unit:t,boolean:/\b(?:false|true)\b/,operator:[/~|[+!\/%<>?=]=?|[-:]=|\*[*=]?|\.{2,3}|&&|\|\||\B-\B|\b(?:and|in|is(?: a| defined| not|nt)?|not|or)\b/],number:n,punctuation:/[{}()\[\];:,]/};r.interpolation={pattern:/\{[^\r\n}:]+\}/,alias:"variable",inside:{delimiter:{pattern:/^\{|\}$/,alias:"punctuation"},rest:r}},r.func={pattern:/[\w-]+\([^)]*\).*/,inside:{function:/^[^(]+/,rest:r}},e.languages.stylus={"atrule-declaration":{pattern:/(^[ \t]*)@.+/m,lookbehind:!0,inside:{atrule:/^@[\w-]+/,rest:r}},"variable-declaration":{pattern:/(^[ \t]*)[\w$-]+\s*.?=[ \t]*(?:\{[^{}]*\}|\S.*|$)/m,lookbehind:!0,inside:{variable:/^\S+/,rest:r}},statement:{pattern:/(^[ \t]*)(?:else|for|if|return|unless)[ \t].+/m,lookbehind:!0,inside:{keyword:/^\S+/,rest:r}},"property-declaration":{pattern:/((?:^|\{)([ \t]*))(?:[\w-]|\{[^}\r\n]+\})+(?:\s*:\s*|[ \t]+)(?!\s)[^{\r\n]*(?:;|[^{\r\n,]$(?!(?:\r?\n|\r)(?:\{|\2[ \t])))/m,lookbehind:!0,inside:{property:{pattern:/^[^\s:]+/,inside:{interpolation:r.interpolation}},rest:r}},selector:{pattern:/(^[ \t]*)(?:(?=\S)(?:[^{}\r\n:()]|::?[\w-]+(?:\([^)\r\n]*\)|(?![\w-]))|\{[^}\r\n]+\})+)(?:(?:\r?\n|\r)(?:\1(?:(?=\S)(?:[^{}\r\n:()]|::?[\w-]+(?:\([^)\r\n]*\)|(?![\w-]))|\{[^}\r\n]+\})+)))*(?:,$|\{|(?=(?:\r?\n|\r)(?:\{|\1[ \t])))/m,lookbehind:!0,inside:{interpolation:r.interpolation,comment:r.comment,punctuation:/[{},]/}},func:r.func,string:r.string,comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|\/\/.*)/,lookbehind:!0,greedy:!0},interpolation:r.interpolation,punctuation:/[{}()\[\];:.]/}}(a),function(e){var t=e.util.clone(e.languages.typescript);e.languages.tsx=e.languages.extend("jsx",t),delete e.languages.tsx.parameter,delete e.languages.tsx["literal-property"];var n=e.languages.tsx.tag;n.pattern=RegExp(/(^|[^\w$]|(?=<\/))/.source+"(?:"+n.pattern.source+")",n.pattern.flags),n.lookbehind=!0}(a),a.languages.wasm={comment:[/\(;[\s\S]*?;\)/,{pattern:/;;.*/,greedy:!0}],string:{pattern:/"(?:\\[\s\S]|[^"\\])*"/,greedy:!0},keyword:[{pattern:/\b(?:align|offset)=/,inside:{operator:/=/}},{pattern:/\b(?:(?:f32|f64|i32|i64)(?:\.(?:abs|add|and|ceil|clz|const|convert_[su]\/i(?:32|64)|copysign|ctz|demote\/f64|div(?:_[su])?|eqz?|extend_[su]\/i32|floor|ge(?:_[su])?|gt(?:_[su])?|le(?:_[su])?|load(?:(?:8|16|32)_[su])?|lt(?:_[su])?|max|min|mul|neg?|nearest|or|popcnt|promote\/f32|reinterpret\/[fi](?:32|64)|rem_[su]|rot[lr]|shl|shr_[su]|sqrt|store(?:8|16|32)?|sub|trunc(?:_[su]\/f(?:32|64))?|wrap\/i64|xor))?|memory\.(?:grow|size))\b/,inside:{punctuation:/\./}},/\b(?:anyfunc|block|br(?:_if|_table)?|call(?:_indirect)?|data|drop|elem|else|end|export|func|get_(?:global|local)|global|if|import|local|loop|memory|module|mut|nop|offset|param|result|return|select|set_(?:global|local)|start|table|tee_local|then|type|unreachable)\b/],variable:/\$[\w!#$%&'*+\-./:<=>?@\\^`|~]+/,number:/[+-]?\b(?:\d(?:_?\d)*(?:\.\d(?:_?\d)*)?(?:[eE][+-]?\d(?:_?\d)*)?|0x[\da-fA-F](?:_?[\da-fA-F])*(?:\.[\da-fA-F](?:_?[\da-fA-D])*)?(?:[pP][+-]?\d(?:_?\d)*)?)\b|\binf\b|\bnan(?::0x[\da-fA-F](?:_?[\da-fA-D])*)?\b/,punctuation:/[()]/};const o=a},7046:()=>{Prism.languages.go=Prism.languages.extend("clike",{string:{pattern:/(^|[^\\])"(?:\\.|[^"\\\r\n])*"|`[^`]*`/,lookbehind:!0,greedy:!0},keyword:/\b(?:break|case|chan|const|continue|default|defer|else|fallthrough|for|func|go(?:to)?|if|import|interface|map|package|range|return|select|struct|switch|type|var)\b/,boolean:/\b(?:_|false|iota|nil|true)\b/,number:[/\b0(?:b[01_]+|o[0-7_]+)i?\b/i,/\b0x(?:[a-f\d_]+(?:\.[a-f\d_]*)?|\.[a-f\d_]+)(?:p[+-]?\d+(?:_\d+)*)?i?(?!\w)/i,/(?:\b\d[\d_]*(?:\.[\d_]*)?|\B\.\d[\d_]*)(?:e[+-]?[\d_]+)?i?(?!\w)/i],operator:/[*\/%^!=]=?|\+[=+]?|-[=-]?|\|[=|]?|&(?:=|&|\^=?)?|>(?:>=?|=)?|<(?:<=?|=|-)?|:=|\.\.\./,builtin:/\b(?:append|bool|byte|cap|close|complex|complex(?:64|128)|copy|delete|error|float(?:32|64)|u?int(?:8|16|32|64)?|imag|len|make|new|panic|print(?:ln)?|real|recover|rune|string|uintptr)\b/}),Prism.languages.insertBefore("go","string",{char:{pattern:/'(?:\\.|[^'\\\r\n]){0,10}'/,greedy:!0}}),delete Prism.languages.go["class-name"]},366:()=>{Prism.languages.python={comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0,greedy:!0},"string-interpolation":{pattern:/(?:f|fr|rf)(?:("""|''')[\s\S]*?\1|("|')(?:\\.|(?!\2)[^\\\r\n])*\2)/i,greedy:!0,inside:{interpolation:{pattern:/((?:^|[^{])(?:\{\{)*)\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}])+\})+\})+\}/,lookbehind:!0,inside:{"format-spec":{pattern:/(:)[^:(){}]+(?=\}$)/,lookbehind:!0},"conversion-option":{pattern:/![sra](?=[:}]$)/,alias:"punctuation"},rest:null}},string:/[\s\S]+/}},"triple-quoted-string":{pattern:/(?:[rub]|br|rb)?("""|''')[\s\S]*?\1/i,greedy:!0,alias:"string"},string:{pattern:/(?:[rub]|br|rb)?("|')(?:\\.|(?!\1)[^\\\r\n])*\1/i,greedy:!0},function:{pattern:/((?:^|\s)def[ \t]+)[a-zA-Z_]\w*(?=\s*\()/g,lookbehind:!0},"class-name":{pattern:/(\bclass\s+)\w+/i,lookbehind:!0},decorator:{pattern:/(^[\t ]*)@\w+(?:\.\w+)*/m,lookbehind:!0,alias:["annotation","punctuation"],inside:{punctuation:/\./}},keyword:/\b(?:_(?=\s*:)|and|as|assert|async|await|break|case|class|continue|def|del|elif|else|except|exec|finally|for|from|global|if|import|in|is|lambda|match|nonlocal|not|or|pass|print|raise|return|try|while|with|yield)\b/,builtin:/\b(?:__import__|abs|all|any|apply|ascii|basestring|bin|bool|buffer|bytearray|bytes|callable|chr|classmethod|cmp|coerce|compile|complex|delattr|dict|dir|divmod|enumerate|eval|execfile|file|filter|float|format|frozenset|getattr|globals|hasattr|hash|help|hex|id|input|int|intern|isinstance|issubclass|iter|len|list|locals|long|map|max|memoryview|min|next|object|oct|open|ord|pow|property|range|raw_input|reduce|reload|repr|reversed|round|set|setattr|slice|sorted|staticmethod|str|sum|super|tuple|type|unichr|unicode|vars|xrange|zip)\b/,boolean:/\b(?:False|None|True)\b/,number:/\b0(?:b(?:_?[01])+|o(?:_?[0-7])+|x(?:_?[a-f0-9])+)\b|(?:\b\d+(?:_\d+)*(?:\.(?:\d+(?:_\d+)*)?)?|\B\.\d+(?:_\d+)*)(?:e[+-]?\d+(?:_\d+)*)?j?(?!\w)/i,operator:/[-+%=]=?|!=|:=|\*\*?=?|\/\/?=?|<[<=>]?|>[=>]?|[&|^~]/,punctuation:/[{}[\];(),.:]/},Prism.languages.python["string-interpolation"].inside.interpolation.inside.rest=Prism.languages.python,Prism.languages.py=Prism.languages.python},9385:()=>{!function(e){e.languages.ruby=e.languages.extend("clike",{comment:{pattern:/#.*|^=begin\s[\s\S]*?^=end/m,greedy:!0},"class-name":{pattern:/(\b(?:class|module)\s+|\bcatch\s+\()[\w.\\]+|\b[A-Z_]\w*(?=\s*\.\s*new\b)/,lookbehind:!0,inside:{punctuation:/[.\\]/}},keyword:/\b(?:BEGIN|END|alias|and|begin|break|case|class|def|define_method|defined|do|each|else|elsif|end|ensure|extend|for|if|in|include|module|new|next|nil|not|or|prepend|private|protected|public|raise|redo|require|rescue|retry|return|self|super|then|throw|undef|unless|until|when|while|yield)\b/,operator:/\.{2,3}|&\.|===||[!=]?~|(?:&&|\|\||<<|>>|\*\*|[+\-*/%<>!^&|=])=?|[?:]/,punctuation:/[(){}[\].,;]/}),e.languages.insertBefore("ruby","operator",{"double-colon":{pattern:/::/,alias:"punctuation"}});var t={pattern:/((?:^|[^\\])(?:\\{2})*)#\{(?:[^{}]|\{[^{}]*\})*\}/,lookbehind:!0,inside:{content:{pattern:/^(#\{)[\s\S]+(?=\}$)/,lookbehind:!0,inside:e.languages.ruby},delimiter:{pattern:/^#\{|\}$/,alias:"punctuation"}}};delete e.languages.ruby.function;var 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