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in the flow

Pham Thanh Lam lampts

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in the flow
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lampts / app.py
Created July 30, 2023 04:29 — forked from python273/app.py
Flask Streaming Langchain Example
import os
os.environ["OPENAI_API_KEY"] = ""
from flask import Flask, Response
import threading
import queue
from langchain.llms import OpenAI
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# -*- coding: utf-8 -*-
import os
import tiktoken
import time
import multiprocessing
working_dir = os.path.dirname(os.path.realpath(__file__))
dataset = 'data'
ws = 512*1024*1024 # 128k per chunk
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lampts / chatgpt.md
Created December 12, 2022 07:44 — forked from veekaybee/chatgpt.md
Everything I understand about chatgpt

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowehre. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

@lampts
lampts / stablediffusionwalk.py
Created August 18, 2022 07:16 — forked from karpathy/stablediffusionwalk.py
hacky stablediffusion code for generating videos
"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space
example way to run this script:
$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry
to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4
import pandas as pd
from collections import Counter
import tensorflow as tf
from tffm import TFFMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
# Loading datasets'