JD-P

Alignment

I did a podcast with Zvi after seeing that Shane Legg couldn't answer a straightforward question about deceptive alignment on a podcast. Demis Hassabis was recently interviewed on the same podcast and also doesn't seem able to answer a straightforward question about alignment. OpenAI's "Superalignment" plan is literally to build AGI and have it solve alignment for us. The public consensus seems to be that "alignment" is a mysterious pre-paradigmatic field with a lot of [open problems](https://www.great

laksjdjf

'''
https://arxiv.org/abs/2312.00858
1. put this file in ComfyUI/custom_nodes
2. load node from <loaders>

start_step, end_step: apply this method when the timestep is between start_step and end_step
cache_interval: interval of caching (1 means no caching)
cache_depth: depth of caching
'''

younesbelkada

import argparse
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = torch.device("cuda:0")

def get_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--max-new-tokens",

younesbelkada

import argparse
from mistral.cache import RotatingBufferCache
import torch
import inspect
from typing import List
from pathlib import Path

from mistral.model import Transformer
from mistral.tokenizer import Tokenizer

jefftriplett

# https://medium.com/@dave1010/amazingly-alarming-autonomous-ai-agents-62f8a785e4d8
# https://github.com/dave1010/hubcap

# to run we need a few libraries: 
# pip install rich typer

import os
import subprocess
import sys
import time

chu-tianxiang

# Adapted from the triton implementation of flash-attention v2
# https://github.com/openai/triton/blob/main/python/tutorials/06-fused-attention.py
import time
import torch
import torch.utils.benchmark as benchmark
import triton
import triton.language as tl


@triton.jit

SunMarc

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

davidad

import pandas as pd

# Load the data
df = pd.read_excel('pnas.2118631119.sd01.xlsx')

import matplotlib.pyplot as plt

# Filter the data for ages 22-35
df_filtered = df[(df['AGE'] >= 22) & (df['AGE'] <= 35) & (df['YEAR'] >= 1955) & (df['YEAR'] <= 2040)]

younesbelkada

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

0xdevalias

Bypassing Cloudflare, Akamai, etc

Some notes/resources for bypassing anti-bot/scraping features on Cloudflare, Akamai, etc.

Table of Contents

• Notes
• See Also
• My Other Related Deepdive Gist's and Projects