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https://www.math.uci.edu/~xiangwen/pdf/LaTeX-Math-Symbols.pdf |
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// bit for modifiers | |
// bits: 0 None | |
// bits: 1 SHIFT | |
// bits: 2 CONTROL | |
// | |
// Note: | |
// If the default key layout is lower case, | |
// and you want to use `Shift + q` to trigger the exit event, | |
// the setting should like this `exit: Some(( code: Char('Q'), modifiers: ( bits: 1,),)),` | |
// The Char should be upper case, and the shift modified bit should be set to 1. |
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import numpy as np | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
# Generating a right-skewed data distribution | |
data = np.random.exponential(scale=2, size=1000) | |
# Performing log transformation | |
log_transformed_data = np.log(data + 1) # Adding 1 to avoid log(0) issue |
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[alias] | |
br = branch | |
st = status -s -b | |
lg = log --pretty=format:"%C(green)%h\\ %C(yellow)[%ad]%Cred%d\\ %Creset%s%Cblue\\ [%cn]" --decorate --date=relative -20 | |
ac = !git add -A && git commit -m | |
oops = !git add -A && git commit --amend --no-edit | |
unstash = stash pop | |
bd = branch -D |
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You are ChatGPT, a large language model trained by OpenAI, based on the GPT-4 architecture. Knowledge cutoff: 2023-04 Current date: 2024-02-13 | |
Image input capabilities: Enabled Personality: v2 | |
# Tools | |
## bio | |
The `bio` tool allows you to persist information across conversations. Address your message `to=bio` and write whatever information you want to remember. The information will appear in the model set context below in future conversations. |
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for token in my_agent.stream({"input": query, "chat_history": chat_history}): | |
print(token, end="", flush=True) |
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""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory | |
git clone https://github.com/myshell-ai/OpenVoice | |
cd OpenVoice | |
git clone https://huggingface.co/myshell-ai/OpenVoice | |
cp -r OpenVoice/* . | |
pip install whisper pynput pyaudio | |
""" | |
from openai import OpenAI | |
import time |
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import datetime | |
from pydantic import BaseModel | |
import sqlite3 | |
db = sqlite3.connect("mydb") | |
class Document(BaseModel): | |
name: str | |
chunk: str | |
date: datetime.date |