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@python273
python273 / yt-0-fix-one-channel-audio.js
Last active August 6, 2024 18:52
Youtube: fix one channel audio
(function (){
var context = new AudioContext();
source = context.createMediaElementSource(document.querySelector('video'));
splitter = context.createChannelSplitter();
merger = context.createChannelMerger();
source.connect(splitter);
splitter.connect(merger, 0, 0); // first 0 is input channel
splitter.connect(merger, 0, 1);
merger.connect(context.destination);
})();
import time
import numpy as np
import pyopencl as cl
def main():
bytes_size = 10*1024*1024*1024
num_of_floats = bytes_size // 4
devices = sum([x.get_devices(device_type=cl.device_type.GPU) for x in cl.get_platforms()], [])
device = devices[0]
@python273
python273 / app.py
Created March 25, 2023 04:36
Flask SSE demo
from flask import Flask, Response
import time
import json
app = Flask(__name__)
@app.route('/')
def index():
return Response('''
<!DOCTYPE html>
@python273
python273 / app.py
Last active December 29, 2024 23:37
Flask Streaming Langchain Example
import os
os.environ["OPENAI_API_KEY"] = ""
from flask import Flask, Response, request
import threading
import queue
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import AIMessage, HumanMessage, SystemMessage
@python273
python273 / README.md
Last active April 21, 2023 13:53
Transformers LLaMA
conda create -n llama
conda activate llama

conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
conda install cudatoolkit==11.8.0 -c conda-forge
python -s -m pip install bitsandbytes accelerate sentencepiece
python -s -m pip install git+https://github.com/huggingface/transformers
#include "tglang.h"
#include "weights.h"
#include <stdlib.h>
#include <string.h>
#include <stdint.h>
#include <stdio.h>
#include <math.h>
@python273
python273 / jitvis.py
Last active July 25, 2024 22:56
TinyJit vis WIP
from tinygrad import Tensor, TinyJit, nn
from tinygrad.helpers import JIT
from tinygrad.nn.optim import SGD
from tinygrad.nn.state import get_parameters
class TinyNet:
def __init__(self):
self.l1 = nn.Linear(784, 128, bias=False)
self.l2 = nn.Linear(128, 10, bias=False)